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Building Machine Learning Solutions with TensorFlow | Path [2020, ENG]
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Building Machine Learning Solutions with TensorFlow | Path [2020, ENG] (Size: 6.26 GB)
| Pluralsight Path. Building Machine Learning Solutions with TensorFlow (2019) | |||
| A1. TensorFlow. Getting Started (Jerry Kurata, 2017) | |||
| 1. Course Overview | |||
| 1. Course Overview.mp4 | 3.58 MB | ||
| 1. Course Overview.vtt | 2.2 KB | ||
| 2. Introduction | |||
| 1. Introduction.mp4 | 5.12 MB | ||
| 1. Introduction.vtt | 4.1 KB | ||
| 2. TensorFlow as Interface and Implementation.mp4 | 5.61 MB | ||
| 2. TensorFlow as Interface and Implementation.vtt | 5.11 KB | ||
| 3. Why Is It Called TensorFlow.mp4 | 3.67 MB | ||
| 3. Why Is It Called TensorFlow.vtt | 3.39 KB | ||
| 4. Skills and Course Structure.mp4 | 7.83 MB | ||
| 4. Skills and Course Structure.vtt | 7.95 KB | ||
| 3. Introducing TensorFlow | |||
| 1. Introduction.mp4 | 1.14 MB | ||
| 1. Introduction.vtt | 1.53 KB | ||
| 2. Installing TensorFlow.mp4 | 30.52 MB | ||
| 2. Installing TensorFlow.vtt | 10.75 KB | ||
| 3. Getting Hands-on.mp4 | 6.01 MB | ||
| 3. Getting Hands-on.vtt | 3.9 KB | ||
| 4. Building Our First Model.mp4 | 2.56 MB | ||
| 4. Building Our First Model.vtt | 2.66 KB | ||
| 5. TensorFlow Training.mp4 | 25.78 MB | ||
| 5. TensorFlow Training.vtt | 13.31 KB | ||
| 6. Tensor Properties.mp4 | 4.89 MB | ||
| 6. Tensor Properties.vtt | 5.42 KB | ||
| 7. Gradient Descent.mp4 | 6.1 MB | ||
| 7. Gradient Descent.vtt | 2.61 KB | ||
| 8. Gradient Descent in Action.mp4 | 6.91 MB | ||
| 8. Gradient Descent in Action.vtt | 2.67 KB | ||
| 9. Summary.mp4 | 1003.23 KB | ||
| 9. Summary.vtt | 1.59 KB | ||
| 4. Creating Neural Networks in TensorFlow | |||
| 1. Introduction.mp4 | 2.57 MB | ||
| 1. Introduction.vtt | 3.19 KB | ||
| 2. Introduction to Neural Networks.mp4 | 5.22 MB | ||
| 2. Introduction to Neural Networks.vtt | 4.94 KB | ||
| 3. Neural Network Symbology and Terminology.mp4 | 2.88 MB | ||
| 3. Neural Network Symbology and Terminology.vtt | 2.05 KB | ||
| 4. Simple MNIST.mp4 | 22.34 MB | ||
| 4. Simple MNIST.vtt | 13.88 KB | ||
| 5. Deep MNIST.mp4 | 7.97 MB | ||
| 5. Deep MNIST.vtt | 8.37 KB | ||
| 6. Coding Deep MNIST.mp4 | 22.26 MB | ||
| 6. Coding Deep MNIST.vtt | 12.08 KB | ||
| 7. Summary.mp4 | 3.3 MB | ||
| 7. Summary.vtt | 2.64 KB | ||
| 5. Debugging and Monitoring | |||
| 1. Introduction.mp4 | 1.43 MB | ||
| 1. Introduction.vtt | 1.8 KB | ||
| 2. Why Is TensorFlow Different.mp4 | 3.92 MB | ||
| 2. Why Is TensorFlow Different.vtt | 4.22 KB | ||
| 3. Using Names and Name Scope.mp4 | 17.28 MB | ||
| 3. Using Names and Name Scope.vtt | 5.52 KB | ||
| 4. Introducing TensorBoard.mp4 | 3.11 MB | ||
| 4. Introducing TensorBoard.vtt | 3.42 KB | ||
| 5. Using TensorBoard - Part 1.mp4 | 15.6 MB | ||
| 5. Using TensorBoard - Part 1.vtt | 7.29 KB | ||
| 6. Using TensorBoard - Part 2.mp4 | 18.5 MB | ||
| 6. Using TensorBoard - Part 2.vtt | 8.22 KB | ||
| 7. Summary.mp4 | 2.35 MB | ||
| 7. Summary.vtt | 2.82 KB | ||
| 6. Transfer Learning with TensorFlow | |||
| 1. Introduction.mp4 | 5.21 MB | ||
| 1. Introduction.vtt | 1.3 KB | ||
| 2. The Need for Transfer Learning.mp4 | 3.4 MB | ||
| 2. The Need for Transfer Learning.vtt | 3.71 KB | ||
| 3. Transfer Learning Basics.mp4 | 3.81 MB | ||
| 3. Transfer Learning Basics.vtt | 4.06 KB | ||
| 4. Implementing Transfer Learning in TensorFlow.mp4 | 29.83 MB | ||
| 4. Implementing Transfer Learning in TensorFlow.vtt | 11.89 KB | ||
| 5. Retraining Inception.mp4 | 12.1 MB | ||
| 5. Retraining Inception.vtt | 4.3 KB | ||
| 6. Using Our Retrained Model.mp4 | 13.99 MB | ||
| 6. Using Our Retrained Model.vtt | 3.89 KB | ||
| 7. Summary.mp4 | 2.06 MB | ||
| 7. Summary.vtt | 2.72 KB | ||
| 7. Extending TensorFlow with Add-ons | |||
| 1. Introduction.mp4 | 5.94 MB | ||
| 1. Introduction.vtt | 2.02 KB | ||
| 2. Keras.mp4 | 1.97 MB | ||
| 2. Keras.vtt | 2.7 KB | ||
| 3. Using Keras.mp4 | 21.11 MB | ||
| 3. Using Keras.vtt | 10.03 KB | ||
| 4. DeepMNIST in Keras.mp4 | 17.67 MB | ||
| 4. DeepMNIST in Keras.vtt | 7.57 KB | ||
| 5. TFLearn.mp4 | 1.88 MB | ||
| 5. TFLearn.vtt | 2.49 KB | ||
| 6. Using TFLearn.mp4 | 13.54 MB | ||
| 6. Using TFLearn.vtt | 3.94 KB | ||
| 7. DeepMNIST in TFLearn.mp4 | 13.77 MB | ||
| 7. DeepMNIST in TFLearn.vtt | 5.13 KB | ||
| 8. Summary.mp4 | 5 MB | ||
| 8. Summary.vtt | 3.16 KB | ||
| 8. Summary | |||
| 1. Last Words.mp4 | 4.51 MB | ||
| 1. Last Words.vtt | 6.11 KB | ||
| exercise.7z | 19.73 MB | ||
| playlist.m3u | 2.49 KB | ||
| ~i.txt | 1.68 KB | ||
| A2. Understanding the Foundations of TensorFlow (Janani Ravi, 2017) | |||
| 1. Course Overview | |||
| 1. Course Overview.mp4 | 3.84 MB | ||
| 1. Course Overview.vtt | 2.67 KB | ||
| 2. Introducing TensorFlow | |||
| 1. Version Check.mp4 | 719.17 KB | ||
| 1. Version Check.vtt | 7 B | ||
| 2. Prerequisites and Course Overview.mp4 | 5.68 MB | ||
| 2. Prerequisites and Course Overview.vtt | 5.41 KB | ||
| 3. Traditional ML Algorithms.mp4 | 11.27 MB | ||
| 3. Traditional ML Algorithms.vtt | 12.17 KB | ||
| 4. Representation ML Algorithms.mp4 | 3.73 MB | ||
| 4. Representation ML Algorithms.vtt | 3.28 KB | ||
| 5. Deep Learning and Neural Networks.mp4 | 7.71 MB | ||
| 5. Deep Learning and Neural Networks.vtt | 6.41 KB | ||
| 6. Introducing TensorFlow.mp4 | 6.51 MB | ||
| 6. Introducing TensorFlow.vtt | 6.68 KB | ||
| 7. The World as a Graph.mp4 | 4.69 MB | ||
| 7. The World as a Graph.vtt | 4.2 KB | ||
| 8. Downloading and Installing TensorFlow.mp4 | 21.85 MB | ||
| 8. Downloading and Installing TensorFlow.vtt | 12.46 KB | ||
| 3. Introducing Computation Graphs | |||
| 1. The Computation Graph.mp4 | 6.29 MB | ||
| 1. The Computation Graph.vtt | 5.3 KB | ||
| 2. Modeling Cyclic Dependencies.mp4 | 4.72 MB | ||
| 2. Modeling Cyclic Dependencies.vtt | 4.09 KB | ||
| 3. Building, Running, and Visualizing Graphs.mp4 | 14.26 MB | ||
| 3. Building, Running, and Visualizing Graphs.vtt | 11.29 KB | ||
| 4. Computation Graphs and Distributed Systems.mp4 | 3.49 MB | ||
| 4. Computation Graphs and Distributed Systems.vtt | 2.69 KB | ||
| 5. Simple Math Operations.mp4 | 13.58 MB | ||
| 5. Simple Math Operations.vtt | 8.48 KB | ||
| 6. Tensors.mp4 | 5.63 MB | ||
| 6. Tensors.vtt | 6.09 KB | ||
| 7. Rank of a Tensor.mp4 | 1.76 MB | ||
| 7. Rank of a Tensor.vtt | 1.79 KB | ||
| 8. Tensor Math.mp4 | 6.87 MB | ||
| 8. Tensor Math.vtt | 4.98 KB | ||
| 9. Numpy and TensorFlow.mp4 | 4.44 MB | ||
| 9. Numpy and TensorFlow.vtt | 3.76 KB | ||
| 4. Digging Deeper into Fundamentals | |||
| 1. A TensorFlow Example - Linear Regression.mp4 | 9.06 MB | ||
| 1. A TensorFlow Example - Linear Regression.vtt | 9.05 KB | ||
| 2. Linear Regression in Practice.mp4 | 3.73 MB | ||
| 2. Linear Regression in Practice.vtt | 3.96 KB | ||
| 3. Placeholders.mp4 | 10.33 MB | ||
| 3. Placeholders.vtt | 7.93 KB | ||
| 4. Fetches and the Feed Dictionary.mp4 | 13.22 MB | ||
| 4. Fetches and the Feed Dictionary.vtt | 7.19 KB | ||
| 5. Variables.mp4 | 17.73 MB | ||
| 5. Variables.vtt | 11.31 KB | ||
| 6. Default and Explicitly Specified Graphs.mp4 | 6.02 MB | ||
| 6. Default and Explicitly Specified Graphs.vtt | 4.6 KB | ||
| 7. Named Scopes.mp4 | 9.53 MB | ||
| 7. Named Scopes.vtt | 5.9 KB | ||
| 8. Interactive Sessions.mp4 | 2.89 MB | ||
| 8. Interactive Sessions.vtt | 2.65 KB | ||
| 9. Quick Overview - Linear Regression in TensorFlow.mp4 | 7.72 MB | ||
| 9. Quick Overview - Linear Regression in TensorFlow.vtt | 5.95 KB | ||
| 5. Working with Images | |||
| 1. Image Recognition and Neural Networks.mp4 | 6.41 MB | ||
| 1. Image Recognition and Neural Networks.vtt | 4.42 KB | ||
| 2. Representing Images as Tensors.mp4 | 4.78 MB | ||
| 2. Representing Images as Tensors.vtt | 5.09 KB | ||
| 3. Transposing Images.mp4 | 13.39 MB | ||
| 3. Transposing Images.vtt | 7.55 KB | ||
| 4. Resizing Images.mp4 | 18.99 MB | ||
| 4. Resizing Images.vtt | 9.42 KB | ||
| 5. Representing a List of Images as a 4D Tensor.mp4 | 20.24 MB | ||
| 5. Representing a List of Images as a 4D Tensor.vtt | 8.56 KB | ||
| 6. Solving Basic Math Functions | |||
| 1. The MNIST Dataset.mp4 | 7.35 MB | ||
| 1. The MNIST Dataset.vtt | 4.42 KB | ||
| 2. The K-nearest-neighbors Algorithm.mp4 | 10.79 MB | ||
| 2. The K-nearest-neighbors Algorithm.vtt | 9.15 KB | ||
| 3. L1 Distance.mp4 | 3 MB | ||
| 3. L1 Distance.vtt | 3.9 KB | ||
| 4. KNN in TensorFlow.mp4 | 10.17 MB | ||
| 4. KNN in TensorFlow.vtt | 8.13 KB | ||
| 5. Calculating L1 in TensorFlow.mp4 | 7.94 MB | ||
| 5. Calculating L1 in TensorFlow.vtt | 5.59 KB | ||
| 6. Measuring Accuracy.mp4 | 6.35 MB | ||
| 6. Measuring Accuracy.vtt | 3.76 KB | ||
| exercise.7z | 1.63 MB | ||
| playlist.m3u | 2.35 KB | ||
| ~i.txt | 1.52 KB | ||
| A3. Building Regression Models Using TensorFlow (Vitthal Srinivasan, 2017) | |||
| 1. Course Overview | |||
| 1. Course Overview.mp4 | 3.13 MB | ||
| 1. Course Overview.vtt | 2.08 KB | ||
| 2. Learning Using Neurons | |||
| 01. Version Check.txt | 37 B | ||
| 02. Understanding Deep Learning.mp4 | 6.42 MB | ||
| 02. Understanding Deep Learning.vtt | 7.74 KB | ||
| 03. Deep Learning as a Representation Learning System.mp4 | 8.07 MB | ||
| 03. Deep Learning as a Representation Learning System.vtt | 7.49 KB | ||
| 04. Neurons as Learning Units.mp4 | 7.71 MB | ||
| 04. Neurons as Learning Units.vtt | 6.32 KB | ||
| 05. Understanding a Neuron.mp4 | 11.82 MB | ||
| 05. Understanding a Neuron.vtt | 11.31 KB | ||
| 06. Activation Functions.mp4 | 3.74 MB | ||
| 06. Activation Functions.vtt | 3.52 KB | ||
| 07. Regression - The Simplest Neural Network.mp4 | 7.41 MB | ||
| 07. Regression - The Simplest Neural Network.vtt | 7.35 KB | ||
| 08. XOR - A Slightly More Complex Neural Network.mp4 | 11.31 MB | ||
| 08. XOR - A Slightly More Complex Neural Network.vtt | 10.85 KB | ||
| 09. Learning XOR.mp4 | 8.5 MB | ||
| 09. Learning XOR.vtt | 7.41 KB | ||
| 10. Choice of Activation Function.mp4 | 4.33 MB | ||
| 10. Choice of Activation Function.vtt | 3.7 KB | ||
| 11. Prequisites and Course Outline.mp4 | 2.14 MB | ||
| 11. Prequisites and Course Outline.vtt | 2.16 KB | ||
| 3. Building Linear Regression Models Using TensorFlow | |||
| 1. Outlining Your Approach.mp4 | 9.76 MB | ||
| 1. Outlining Your Approach.vtt | 5.81 KB | ||
| 2. A Baseline Implementation.mp4 | 14.32 MB | ||
| 2. A Baseline Implementation.vtt | 10.84 KB | ||
| 3. Understanding Gradient Descent.mp4 | 14.88 MB | ||
| 3. Understanding Gradient Descent.vtt | 14.82 KB | ||
| 4. Implementing Stochastic Gradient Descent in TensorFlow.mp4 | 21.56 MB | ||
| 4. Implementing Stochastic Gradient Descent in TensorFlow.vtt | 15.01 KB | ||
| 5. Instrumenting and Using TensorBoard.mp4 | 11.33 MB | ||
| 5. Instrumenting and Using TensorBoard.vtt | 7.43 KB | ||
| 6. Implementing Batch Gradient Descent in TensorFlow.mp4 | 14.05 MB | ||
| 6. Implementing Batch Gradient Descent in TensorFlow.vtt | 9.23 KB | ||
| 7. Implementing Multiple Regression.mp4 | 11.67 MB | ||
| 7. Implementing Multiple Regression.vtt | 8.63 KB | ||
| 4. Building Logistic Regression Models Using TensorFlow | |||
| 1. The Intuition Behind Logistic Regression.mp4 | 9.47 MB | ||
| 1. The Intuition Behind Logistic Regression.vtt | 9.86 KB | ||
| 2. Logistic Regression and Linear Regression.mp4 | 5.42 MB | ||
| 2. Logistic Regression and Linear Regression.vtt | 6.51 KB | ||
| 3. A Baseline Implementation.mp4 | 11.05 MB | ||
| 3. A Baseline Implementation.vtt | 6.99 KB | ||
| 4. Logistic Regression in TensorFlow.mp4 | 8.83 MB | ||
| 4. Logistic Regression in TensorFlow.vtt | 9.25 KB | ||
| 5. The Implications of Using Softmax Activation.mp4 | 10 MB | ||
| 5. The Implications of Using Softmax Activation.vtt | 10.17 KB | ||
| 6. Cross Entropy.mp4 | 3.1 MB | ||
| 6. Cross Entropy.vtt | 3.23 KB | ||
| 7. Implementing Linear Classification in TensorFlow.mp4 | 16.08 MB | ||
| 7. Implementing Linear Classification in TensorFlow.vtt | 9.81 KB | ||
| 8. Calculating Accuracy.mp4 | 11.57 MB | ||
| 8. Calculating Accuracy.vtt | 8.58 KB | ||
| 5. Building Generalized Linear Models Using Estimators | |||
| 1. How Estimators Work.mp4 | 18.57 MB | ||
| 1. How Estimators Work.vtt | 11.51 KB | ||
| 2. Linear Regression with Estimators.mp4 | 9.55 MB | ||
| 2. Linear Regression with Estimators.vtt | 3.74 KB | ||
| 3. Logistic Regression with Estimators.mp4 | 10.17 MB | ||
| 3. Logistic Regression with Estimators.vtt | 4.08 KB | ||
| 4. Extending Estimators with Custom Models.mp4 | 19.76 MB | ||
| 4. Extending Estimators with Custom Models.vtt | 10.06 KB | ||
| 5. Course Summary.mp4 | 3.47 MB | ||
| 5. Course Summary.vtt | 3.14 KB | ||
| exercise.7z | 1.77 MB | ||
| playlist.m3u | 2.59 KB | ||
| ~i.txt | 1.65 KB | ||
| A4. Building Classification Models with TensorFlow (Janani Ravi, 2017) | |||
| 1. Course Overview | |||
| 1. Course Overview.mp4 | 4.01 MB | ||
| 1. Course Overview.vtt | 2.58 KB | ||
| 2. Overview of Classification Models | |||
| 1. Version Check.mp4 | 719.15 KB | ||
| 1. Version Check.vtt | 7 B | ||
| 2. Prerequisites and Software Needed for This Course.mp4 | 5.37 MB | ||
| 2. Prerequisites and Software Needed for This Course.vtt | 5.45 KB | ||
| 3. Classification and Classifiers.mp4 | 7.58 MB | ||
| 3. Classification and Classifiers.vtt | 7.92 KB | ||
| 4. Using Accuracy to Evaluate Models.mp4 | 5.86 MB | ||
| 4. Using Accuracy to Evaluate Models.vtt | 6.13 KB | ||
| 5. Using Precision and Recall to Evaluate Models.mp4 | 2.62 MB | ||
| 5. Using Precision and Recall to Evaluate Models.vtt | 2.58 KB | ||
| 6. The PrecisionRecall Tradeoff.mp4 | 6.66 MB | ||
| 6. The PrecisionRecall Tradeoff.vtt | 6.77 KB | ||
| 7. The Precision-Recall Tradeoff.mp4 | 4.89 MB | ||
| 7. The Precision-Recall Tradeoff.vtt | 5.34 KB | ||
| 8. Binary, Multilabel, Multiclass, and Multioutput Classifiers.mp4 | 7.11 MB | ||
| 8. Binary, Multilabel, Multiclass, and Multioutput Classifiers.vtt | 5.99 KB | ||
| 3. Simple Classification Models in TensorFlow | |||
| 01. Representing Images as Tensors.mp4 | 6.42 MB | ||
| 01. Representing Images as Tensors.vtt | 6.05 KB | ||
| 02. The K-nearest Neighbors Algorithm.mp4 | 4.92 MB | ||
| 02. The K-nearest Neighbors Algorithm.vtt | 4.26 KB | ||
| 03. Distance Measures.mp4 | 2.53 MB | ||
| 03. Distance Measures.vtt | 2.68 KB | ||
| 04. Demo - Environment and Package Setup.mp4 | 4.79 MB | ||
| 04. Demo - Environment and Package Setup.vtt | 2.85 KB | ||
| 05. Demo - Image Classification Using K-nearest Neighbors.mp4 | 23 MB | ||
| 05. Demo - Image Classification Using K-nearest Neighbors.vtt | 14.15 KB | ||
| 06. The Intuition Behind Logistic Regression.mp4 | 7.2 MB | ||
| 06. The Intuition Behind Logistic Regression.vtt | 7.8 KB | ||
| 07. Logistic Regression for Prediction.mp4 | 3.22 MB | ||
| 07. Logistic Regression for Prediction.vtt | 2.88 KB | ||
| 08. Cross-entropy as a Cost Function.mp4 | 2.99 MB | ||
| 08. Cross-entropy as a Cost Function.vtt | 3.51 KB | ||
| 09. Demo - Exploring the Census Dataset.mp4 | 8.6 MB | ||
| 09. Demo - Exploring the Census Dataset.vtt | 5.36 KB | ||
| 10. Feature Engineering with Bucketized and Crossed Columns.mp4 | 4.93 MB | ||
| 10. Feature Engineering with Bucketized and Crossed Columns.vtt | 4.82 KB | ||
| 11. Working with Estimators in TensorFlow.mp4 | 6.49 MB | ||
| 11. Working with Estimators in TensorFlow.vtt | 6.12 KB | ||
| 12. Demo - Income Prediction Using Logistic Regression.mp4 | 17.39 MB | ||
| 12. Demo - Income Prediction Using Logistic Regression.vtt | 9.99 KB | ||
| 4. Convolutional Neural Networks for Classification in TensorFlow | |||
| 01. Neurons and Neural Networks.mp4 | 12.33 MB | ||
| 01. Neurons and Neural Networks.vtt | 8.81 KB | ||
| 02. Understanding How Convolution Works.mp4 | 10.47 MB | ||
| 02. Understanding How Convolution Works.vtt | 8.46 KB | ||
| 03. Zero Padding and Stride Size.mp4 | 7.1 MB | ||
| 03. Zero Padding and Stride Size.vtt | 5.95 KB | ||
| 04. Introducing Convolutional Neural Networks.mp4 | 4.62 MB | ||
| 04. Introducing Convolutional Neural Networks.vtt | 4.47 KB | ||
| 05. Convolutional Layers and Feature Maps.mp4 | 11.48 MB | ||
| 05. Convolutional Layers and Feature Maps.vtt | 8.53 KB | ||
| 06. Pooling Layers.mp4 | 7.2 MB | ||
| 06. Pooling Layers.vtt | 4.96 KB | ||
| 07. Architecture of CNNs.mp4 | 10.54 MB | ||
| 07. Architecture of CNNs.vtt | 7.77 KB | ||
| 08. Demo - Image Classification Using CNNs (MNIST Dataset).mp4 | 22.55 MB | ||
| 08. Demo - Image Classification Using CNNs (MNIST Dataset).vtt | 14.31 KB | ||
| 09. Demo - Exploring the CIFAR-10 Dataset.mp4 | 10.03 MB | ||
| 09. Demo - Exploring the CIFAR-10 Dataset.vtt | 6.33 KB | ||
| 10. Demo - Image Classification Using CNNs (CIFAR-10 Dataset).mp4 | 18.38 MB | ||
| 10. Demo - Image Classification Using CNNs (CIFAR-10 Dataset).vtt | 10.65 KB | ||
| 5. Recurrent Neural Networks for Classification in TensorFlow | |||
| 01. Why Is the Past Important.mp4 | 6.36 MB | ||
| 01. Why Is the Past Important.vtt | 5.89 KB | ||
| 02. Understaning the Recurrent Neuron.mp4 | 5.88 MB | ||
| 02. Understaning the Recurrent Neuron.vtt | 6.27 KB | ||
| 03. Training Using Back Propogation.mp4 | 8.31 MB | ||
| 03. Training Using Back Propogation.vtt | 7.37 KB | ||
| 04. Demo - Classifying Images Using RNNs (MNIST Dataset).mp4 | 6.32 MB | ||
| 04. Demo - Classifying Images Using RNNs (MNIST Dataset).vtt | 5.96 KB | ||
| 05. Dealing with Vanishing and Exploding Gradients.mp4 | 12.31 MB | ||
| 05. Dealing with Vanishing and Exploding Gradients.vtt | 8.67 KB | ||
| 06. The LSTM Memory Cell.mp4 | 7.59 MB | ||
| 06. The LSTM Memory Cell.vtt | 7.73 KB | ||
| 07. Word Vector Encodings.mp4 | 10.93 MB | ||
| 07. Word Vector Encodings.vtt | 10.84 KB | ||
| 08. Demo - Exploring the DBPedia Dataset for Text Classification.mp4 | 15.85 MB | ||
| 08. Demo - Exploring the DBPedia Dataset for Text Classification.vtt | 9.5 KB | ||
| 09. Demo - Text Classification Using RNNs.mp4 | 20.97 MB | ||
| 09. Demo - Text Classification Using RNNs.vtt | 11.21 KB | ||
| 10. Summary and Next Steps for Learning.mp4 | 2.23 MB | ||
| 10. Summary and Next Steps for Learning.vtt | 1.96 KB | ||
| exercise.7z | 187.83 MB | ||
| playlist.m3u | 3.88 KB | ||
| ~i.txt | 1.57 KB | ||
| A5. Building Unsupervised Learning Models with TensorFlow (Janani Ravi, 2017) | |||
| 1. Course Overview | |||
| 1. Course Overview.mp4 | 4.17 MB | ||
| 1. Course Overview.vtt | 2.59 KB | ||
| 2. Introduction to Unsupervised Learning | |||
| 1. Version Check.mp4 | 727.17 KB | ||
| 1. Version Check.vtt | 7 B | ||
| 2. Prerequisites and Required Software.mp4 | 4.84 MB | ||
| 2. Prerequisites and Required Software.vtt | 5.39 KB | ||
| 3. Supervised Learning.mp4 | 7.95 MB | ||
| 3. Supervised Learning.vtt | 8.56 KB | ||
| 4. Unsupervised Learning.mp4 | 10 MB | ||
| 4. Unsupervised Learning.vtt | 7.92 KB | ||
| 5. Introduction to Clustering.mp4 | 4.22 MB | ||
| 5. Introduction to Clustering.vtt | 4.75 KB | ||
| 6. Minimize Intra-cluster Similarity; Maximize Inter-cluster Similarity.mp4 | 5.23 MB | ||
| 6. Minimize Intra-cluster Similarity; Maximize Inter-cluster Similarity.vtt | 3.11 KB | ||
| 7. The Intuition Behind How Autoencoders Work.mp4 | 3.23 MB | ||
| 7. The Intuition Behind How Autoencoders Work.vtt | 3.4 KB | ||
| 8. Understanding Principal Components Analysis.mp4 | 9.33 MB | ||
| 8. Understanding Principal Components Analysis.vtt | 10.2 KB | ||
| 9. Dimensionality Reducing Using Autoencoders.mp4 | 9.04 MB | ||
| 9. Dimensionality Reducing Using Autoencoders.vtt | 8.36 KB | ||
| 3. Clustering Using Unsupervised Learning | |||
| 01. The Intuition Behind K-means Clustering.mp4 | 9.14 MB | ||
| 01. The Intuition Behind K-means Clustering.vtt | 7.65 KB | ||
| 02. Setting up for K-means Clustering Demos.mp4 | 4.59 MB | ||
| 02. Setting up for K-means Clustering Demos.vtt | 2.96 KB | ||
| 03. Demo - K-means Clustering on 1D Arrays.mp4 | 20.39 MB | ||
| 03. Demo - K-means Clustering on 1D Arrays.vtt | 10.58 KB | ||
| 04. K-means Clustering - Algorithm and Design Choices.mp4 | 8.89 MB | ||
| 04. K-means Clustering - Algorithm and Design Choices.vtt | 7.64 KB | ||
| 05. Demo - K-means Clustering on 2D Arrays.mp4 | 18.77 MB | ||
| 05. Demo - K-means Clustering on 2D Arrays.vtt | 9.48 KB | ||
| 06. Hyperparameter Tuning.mp4 | 7.12 MB | ||
| 06. Hyperparameter Tuning.vtt | 8.17 KB | ||
| 07. Demo - K-means Clustering on the MNIST Dataset.mp4 | 15.24 MB | ||
| 07. Demo - K-means Clustering on the MNIST Dataset.vtt | 8.75 KB | ||
| 08. Demo - Tweaking the Algorithm on the MNIST Dataset.mp4 | 12.68 MB | ||
| 08. Demo - Tweaking the Algorithm on the MNIST Dataset.vtt | 7.11 KB | ||
| 09. Understanding Hierarchical Clustering.mp4 | 8.69 MB | ||
| 09. Understanding Hierarchical Clustering.vtt | 7.74 KB | ||
| 10. Use Cases of Clustering.mp4 | 3.88 MB | ||
| 10. Use Cases of Clustering.vtt | 3.31 KB | ||
| 4. Understanding Neurons and Neural Networks | |||
| 1. Deep Learning, Neural Networks, and Neurons.mp4 | 11.85 MB | ||
| 1. Deep Learning, Neural Networks, and Neurons.vtt | 8.63 KB | ||
| 2. How Does a Neuron Work.mp4 | 12.78 MB | ||
| 2. How Does a Neuron Work.vtt | 10.24 KB | ||
| 3. Gradient Descent Optimization.mp4 | 6.11 MB | ||
| 3. Gradient Descent Optimization.vtt | 5.4 KB | ||
| 4. Back Propagation in a Neural Network.mp4 | 3.32 MB | ||
| 4. Back Propagation in a Neural Network.vtt | 2.47 KB | ||
| 5. Vanishing, Exploding Gradients, and Dying Neurons.mp4 | 9.67 MB | ||
| 5. Vanishing, Exploding Gradients, and Dying Neurons.vtt | 9 KB | ||
| 6. Overfitting, Dropout, and Regularisation.mp4 | 7.87 MB | ||
| 6. Overfitting, Dropout, and Regularisation.vtt | 7.94 KB | ||
| 7. Overfitting, Regularisation, and Dropout.mp4 | 10.29 MB | ||
| 7. Overfitting, Regularisation, and Dropout.vtt | 8.1 KB | ||
| 5. Autoencoders Using Unsupervised Learning | |||
| 01. Autoencoders as an Unsupervised Learning Technique.mp4 | 3.38 MB | ||
| 01. Autoencoders as an Unsupervised Learning Technique.vtt | 2.82 KB | ||
| 02. Autoencoders Learn the Input to Reproduce at the Output.mp4 | 4.16 MB | ||
| 02. Autoencoders Learn the Input to Reproduce at the Output.vtt | 4.27 KB | ||
| 03. Principal Components Analysis.mp4 | 5.86 MB | ||
| 03. Principal Components Analysis.vtt | 5.33 KB | ||
| 04. Demo - Implementing PCA Using Matplotlib.mp4 | 16.82 MB | ||
| 04. Demo - Implementing PCA Using Matplotlib.vtt | 9.86 KB | ||
| 05. The Undercomplete Autoencoder.mp4 | 10.39 MB | ||
| 05. The Undercomplete Autoencoder.vtt | 8.59 KB | ||
| 06. Demo - Implementing an Autoencoder to Perform PCA.mp4 | 19.4 MB | ||
| 06. Demo - Implementing an Autoencoder to Perform PCA.vtt | 10.53 KB | ||
| 07. Demo - Implementing the Stacked Autoencoder.mp4 | 24.18 MB | ||
| 07. Demo - Implementing the Stacked Autoencoder.vtt | 15.25 KB | ||
| 08. Demo - Implementing a Stacked Autoencoder with Dropout.mp4 | 8.66 MB | ||
| 08. Demo - Implementing a Stacked Autoencoder with Dropout.vtt | 5.12 KB | ||
| 09. Demo - Implementing a Denoising Autoencoder.mp4 | 4.6 MB | ||
| 09. Demo - Implementing a Denoising Autoencoder.vtt | 4.08 KB | ||
| 10. Denoising Autoencoders and Unsupervised Pre-training.mp4 | 7.13 MB | ||
| 10. Denoising Autoencoders and Unsupervised Pre-training.vtt | 4.25 KB | ||
| 11. Use Cases of Autoencoders.mp4 | 6.71 MB | ||
| 11. Use Cases of Autoencoders.vtt | 5.15 KB | ||
| exercise.7z | 3.15 MB | ||
| playlist.m3u | 3.28 KB | ||
| ~i.txt | 1.47 KB | ||
| B1. Debugging and Monitoring TensorFlow Programs (Janani Ravi, 2018) | |||
| 1. Course Overview | |||
| 1. Course Overview.mp4 | 4.07 MB | ||
| 1. Course Overview.vtt | 2.69 KB | ||
| 2. Introducing TensorFlow Debugging Methods | |||
| 01. Version Check.mp4 | 721.32 KB | ||
| 01. Version Check.vtt | 7 B | ||
| 02. Module Overview.mp4 | 3.26 MB | ||
| 02. Module Overview.vtt | 2.5 KB | ||
| 03. Prerequisites and Course Overview.mp4 | 3.59 MB | ||
| 03. Prerequisites and Course Overview.vtt | 4.25 KB | ||
| 04. A Brief Overview of Computation Graphs.mp4 | 3.64 MB | ||
| 04. A Brief Overview of Computation Graphs.vtt | 2.94 KB | ||
| 05. Debugging TensorFlow Programs.mp4 | 4.56 MB | ||
| 05. Debugging TensorFlow Programs.vtt | 5.37 KB | ||
| 06. Fetching Tensors - Computing Intermediate Values.mp4 | 7.89 MB | ||
| 06. Fetching Tensors - Computing Intermediate Values.vtt | 5.87 KB | ||
| 07. Fetching Tensors - A Neural Network Example.mp4 | 11.92 MB | ||
| 07. Fetching Tensors - A Neural Network Example.vtt | 6.17 KB | ||
| 08. Fetching Tensors - Side Effects.mp4 | 9.79 MB | ||
| 08. Fetching Tensors - Side Effects.vtt | 4.39 KB | ||
| 09. Partial Runs.mp4 | 10.89 MB | ||
| 09. Partial Runs.vtt | 7.63 KB | ||
| 10. Introducing tf.Print().mp4 | 8.34 MB | ||
| 10. Introducing tf.Print().vtt | 6.66 KB | ||
| 11. tf.Print() - A Neural Network Example.mp4 | 17.39 MB | ||
| 11. tf.Print() - A Neural Network Example.vtt | 5.43 KB | ||
| 12. Introducing tf.Assert().mp4 | 20.84 MB | ||
| 12. Introducing tf.Assert().vtt | 10.4 KB | ||
| 13. Traditional Python Debuggers.mp4 | 15.15 MB | ||
| 13. Traditional Python Debuggers.vtt | 5.15 KB | ||
| 14. Interposing Python Code in Computation Graphs.mp4 | 15.44 MB | ||
| 14. Interposing Python Code in Computation Graphs.vtt | 5.63 KB | ||
| 15. Introducing tfdbg and TensorBoard.mp4 | 3.27 MB | ||
| 15. Introducing tfdbg and TensorBoard.vtt | 2.76 KB | ||
| 3. Applying tfdbg to Common Use-cases | |||
| 1. Module Overview.mp4 | 2.06 MB | ||
| 1. Module Overview.vtt | 1.86 KB | ||
| 2. The Curses Library for tfdbg.mp4 | 8.76 MB | ||
| 2. The Curses Library for tfdbg.vtt | 5.04 KB | ||
| 3. Introducing tfdbg Commands.mp4 | 17.56 MB | ||
| 3. Introducing tfdbg Commands.vtt | 12.66 KB | ||
| 4. Debugging Shortcuts and Multiple Session Runs.mp4 | 19.36 MB | ||
| 4. Debugging Shortcuts and Multiple Session Runs.vtt | 10.26 KB | ||
| 5. Using has_inf_or_nan and Custom Filters.mp4 | 18.64 MB | ||
| 5. Using has_inf_or_nan and Custom Filters.vtt | 10.89 KB | ||
| 6. Using Filters with Neural Networks.mp4 | 21.47 MB | ||
| 6. Using Filters with Neural Networks.vtt | 10.24 KB | ||
| 7. Debugging Estimators and Experiments.mp4 | 11 MB | ||
| 7. Debugging Estimators and Experiments.vtt | 6.99 KB | ||
| 8. Debugging Keras Models.mp4 | 8.51 MB | ||
| 8. Debugging Keras Models.vtt | 6.38 KB | ||
| 4. Visualizing TensorFlow Using TensorBoard | |||
| 01. Module Overview.mp4 | 2.02 MB | ||
| 01. Module Overview.vtt | 1.86 KB | ||
| 02. Introducing TensorBoard.mp4 | 7.64 MB | ||
| 02. Introducing TensorBoard.vtt | 6.46 KB | ||
| 03. Naming Tensors and Nodes.mp4 | 8.19 MB | ||
| 03. Naming Tensors and Nodes.vtt | 6.17 KB | ||
| 04. Using Named Scopes.mp4 | 14.6 MB | ||
| 04. Using Named Scopes.vtt | 7.87 KB | ||
| 05. Scalar Summaries.mp4 | 20.9 MB | ||
| 05. Scalar Summaries.vtt | 9.55 KB | ||
| 06. Histograms.mp4 | 4.95 MB | ||
| 06. Histograms.vtt | 5.55 KB | ||
| 07. Moving Mean Normal Distribution.mp4 | 6.66 MB | ||
| 07. Moving Mean Normal Distribution.vtt | 4.77 KB | ||
| 08. More Histograms.mp4 | 6.14 MB | ||
| 08. More Histograms.vtt | 3.82 KB | ||
| 09. Runtime Statistics.mp4 | 7.88 MB | ||
| 09. Runtime Statistics.vtt | 4.03 KB | ||
| 10. Working with Images.mp4 | 11.3 MB | ||
| 10. Working with Images.vtt | 7.92 KB | ||
| exercise.7z | 445.97 KB | ||
| playlist.m3u | 2.56 KB | ||
| ~i.txt | 2.21 KB | ||
| B2. Deploying TensorFlow Models to AWS, Azure, and the GCP (Janani Ravi, 2018) | |||
| 1. Course Overview | |||
| 1. Course Overview.mp4 | 4.62 MB | ||
| 1. Course Overview.vtt | 2.9 KB | ||
| 2. Using TensorFlow Serving | |||
| 01. Module Overview.mp4 | 2.36 MB | ||
| 01. Module Overview.vtt | 1.96 KB | ||
| 02. Prerequisites and Course Overview.mp4 | 3.82 MB | ||
| 02. Prerequisites and Course Overview.vtt | 4 KB | ||
| 03. The Machine Learning Workflow - Local Serving.mp4 | 4.28 MB | ||
| 03. The Machine Learning Workflow - Local Serving.vtt | 4.33 KB | ||
| 04. Demo - Exploring the Churn Prediction Dataset.mp4 | 8.74 MB | ||
| 04. Demo - Exploring the Churn Prediction Dataset.vtt | 5.31 KB | ||
| 05. Demo - Training and the Experiment Function.mp4 | 8.92 MB | ||
| 05. Demo - Training and the Experiment Function.vtt | 4.76 KB | ||
| 06. The Saved Model.mp4 | 2.73 MB | ||
| 06. The Saved Model.vtt | 3 KB | ||
| 07. The TensorFlow Model Server.mp4 | 2.25 MB | ||
| 07. The TensorFlow Model Server.vtt | 2.07 KB | ||
| 08. gRPC and Protocol Buffers.mp4 | 3.67 MB | ||
| 08. gRPC and Protocol Buffers.vtt | 2.67 KB | ||
| 09. Demo - Setting up the Azure VM.mp4 | 7.34 MB | ||
| 09. Demo - Setting up the Azure VM.vtt | 4.27 KB | ||
| 10. Demo - Installing TensorFlow, gRPC, Serving APIs and the Model Server.mp4 | 8.47 MB | ||
| 10. Demo - Installing TensorFlow, gRPC, Serving APIs and the Model Server.vtt | 4.25 KB | ||
| 11. Demo - Deploying and Hosting the MNIST Classification Model.mp4 | 9.55 MB | ||
| 11. Demo - Deploying and Hosting the MNIST Classification Model.vtt | 4.81 KB | ||
| 12. Demo - Setting up the Churn Model.mp4 | 7.72 MB | ||
| 12. Demo - Setting up the Churn Model.vtt | 4.32 KB | ||
| 13. Demo - Training and Saving the Model.mp4 | 11.65 MB | ||
| 13. Demo - Training and Saving the Model.vtt | 5.67 KB | ||
| 14. Demo - Making Predictions from a Saved Model.mp4 | 16.47 MB | ||
| 14. Demo - Making Predictions from a Saved Model.vtt | 8.29 KB | ||
| 3. Containerizing TensorFlow Models Using Docker on Microsoft Azure | |||
| 1. Module Overview.mp4 | 2.36 MB | ||
| 1. Module Overview.vtt | 1.9 KB | ||
| 2. Azure ML IaaS and PaaS Options.mp4 | 8.39 MB | ||
| 2. Azure ML IaaS and PaaS Options.vtt | 8.33 KB | ||
| 3. Containers and VMs.mp4 | 5.27 MB | ||
| 3. Containers and VMs.vtt | 4.29 KB | ||
| 4. Demo - Docker CE Install.mp4 | 6.04 MB | ||
| 4. Demo - Docker CE Install.vtt | 3.52 KB | ||
| 5. Demo - Building the Docker Image.mp4 | 10.58 MB | ||
| 5. Demo - Building the Docker Image.vtt | 5.24 KB | ||
| 6. Demo - Running a Docker Container for Predictions.mp4 | 4.29 MB | ||
| 6. Demo - Running a Docker Container for Predictions.vtt | 2.96 KB | ||
| 7. Demo - Registering the Image with Docker Hub.mp4 | 6.82 MB | ||
| 7. Demo - Registering the Image with Docker Hub.vtt | 3.91 KB | ||
| 8. Demo - Running Docker Using the Docker Hub Image.mp4 | 5.92 MB | ||
| 8. Demo - Running Docker Using the Docker Hub Image.vtt | 3.15 KB | ||
| 9. Demo - Making Predictions from a Saved Model Using a Dock.mp4 | 6.41 MB | ||
| 9. Demo - Making Predictions from a Saved Model Using a Dock.vtt | 3.86 KB | ||
| 4. Deploying TensorFlow Models on Amazon AWS | |||
| 01. Module Overview.mp4 | 1.54 MB | ||
| 01. Module Overview.vtt | 1.29 KB | ||
| 02. The Machine Learning Workflow - SageMaker.mp4 | 5.58 MB | ||
| 02. The Machine Learning Workflow - SageMaker.vtt | 5.74 KB | ||
| 03. Training the Model.mp4 | 3.1 MB | ||
| 03. Training the Model.vtt | 2.63 KB | ||
| 04. Deploying the Model.mp4 | 4.47 MB | ||
| 04. Deploying the Model.vtt | 3.85 KB | ||
| 05. Training and Inference Code Interface.mp4 | 2.58 MB | ||
| 05. Training and Inference Code Interface.vtt | 2.61 KB | ||
| 06. Demo - Setting up an S3 Bucket.mp4 | 4.99 MB | ||
| 06. Demo - Setting up an S3 Bucket.vtt | 2.57 KB | ||
| 07. Demo - Setting up a Notebook Instance.mp4 | 5.37 MB | ||
| 07. Demo - Setting up a Notebook Instance.vtt | 2.99 KB | ||
| 08. Demo - Data Preparation.mp4 | 6.11 MB | ||
| 08. Demo - Data Preparation.vtt | 3.49 KB | ||
| 09. Demo - Setting up the TensorFlow Model.mp4 | 7.3 MB | ||
| 09. Demo - Setting up the TensorFlow Model.vtt | 3.53 KB | ||
| 10. Demo - Training and Deploying the Model.mp4 | 8.65 MB | ||
| 10. Demo - Training and Deploying the Model.vtt | 3.72 KB | ||
| 11. Demo - Models and Endpoints.mp4 | 5.57 MB | ||
| 11. Demo - Models and Endpoints.vtt | 3.16 KB | ||
| 5. Deploying TensorFlow Models on the Google Cloud Platform | |||
| 01. Module Overview.mp4 | 2.4 MB | ||
| 01. Module Overview.vtt | 2.06 KB | ||
| 02. Cloud ML Engine vs. SageMaker.mp4 | 7.08 MB | ||
| 02. Cloud ML Engine vs. SageMaker.vtt | 5.31 KB | ||
| 03. The Machine Learning Workflow - Cloud ML Engine.mp4 | 6.88 MB | ||
| 03. The Machine Learning Workflow - Cloud ML Engine.vtt | 5.67 KB | ||
| 04. Training the Model.mp4 | 6.44 MB | ||
| 04. Training the Model.vtt | 5.49 KB | ||
| 05. Deploying the Model.mp4 | 2.18 MB | ||
| 05. Deploying the Model.vtt | 2.17 KB | ||
| 06. Demo - Connecting to Datalab.mp4 | 9.72 MB | ||
| 06. Demo - Connecting to Datalab.vtt | 4.76 KB | ||
| 07. Demo - Creating a GCS Bucket.mp4 | 3.2 MB | ||
| 07. Demo - Creating a GCS Bucket.vtt | 1.42 KB | ||
| 08. Demo - Data Preparation.mp4 | 6.46 MB | ||
| 08. Demo - Data Preparation.vtt | 3.2 KB | ||
| 09. Demo - Setting up Bucket Permissions.mp4 | 9.81 MB | ||
| 09. Demo - Setting up Bucket Permissions.vtt | 4.16 KB | ||
| 10. Demo - Python Package Contents.mp4 | 10.77 MB | ||
| 10. Demo - Python Package Contents.vtt | 5.96 KB | ||
| 11. Demo - Local Training and Prediction.mp4 | 6.73 MB | ||
| 11. Demo - Local Training and Prediction.vtt | 2.85 KB | ||
| 12. Demo - Distributed Training and Deployment.mp4 | 10.35 MB | ||
| 12. Demo - Distributed Training and Deployment.vtt | 4.54 KB | ||
| 13. Demo - Making Predictions Using Cloud ML Endpoints.mp4 | 4.6 MB | ||
| 13. Demo - Making Predictions Using Cloud ML Endpoints.vtt | 2.11 KB | ||
| 14. Summary and Further Study.mp4 | 2.52 MB | ||
| 14. Summary and Further Study.vtt | 2.46 KB | ||
| exercise.7z | 1.68 MB | ||
| playlist.m3u | 4.3 KB | ||
| ~i.txt | 1.72 KB | ||
| C1. Language Modeling with Recurrent Neural Networks in TensorFlow (Janani Ravi, 2018) | |||
| 1. Course Overview | |||
| 1. Course Overview.mp4 | 3.71 MB | ||
| 1. Course Overview.vtt | 2.66 KB | ||
| 2. Applying Bidirectional Recurrent Neural Networks to Word Recognition | |||
| 01. Version Check.mp4 | 736.22 KB | ||
| 01. Version Check.vtt | 7 B | ||
| 02. Module Overview.mp4 | 3.06 MB | ||
| 02. Module Overview.vtt | 2.63 KB | ||
| 03. Prerequisites and Course Outline.mp4 | 3.2 MB | ||
| 03. Prerequisites and Course Outline.vtt | 3.47 KB | ||
| 04. The Recurrent Neuron.mp4 | 4.89 MB | ||
| 04. The Recurrent Neuron.vtt | 5.39 KB | ||
| 05. Training a Recurrent Neural Network.mp4 | 8.24 MB | ||
| 05. Training a Recurrent Neural Network.vtt | 7.74 KB | ||
| 06. The Long Memory Cell.mp4 | 6.89 MB | ||
| 06. The Long Memory Cell.vtt | 7.12 KB | ||
| 07. Bidirectional RNNs.mp4 | 11.26 MB | ||
| 07. Bidirectional RNNs.vtt | 10.67 KB | ||
| 08. OCR - A Sequence Labelling Problem.mp4 | 4.96 MB | ||
| 08. OCR - A Sequence Labelling Problem.vtt | 4.94 KB | ||
| 09. OCR File Format.mp4 | 5.35 MB | ||
| 09. OCR File Format.vtt | 5.81 KB | ||
| 10. Features and Labels for OCR.mp4 | 2.26 MB | ||
| 10. Features and Labels for OCR.vtt | 3.02 KB | ||
| 11. Conventional RNN Architecture.mp4 | 8.12 MB | ||
| 11. Conventional RNN Architecture.vtt | 7.47 KB | ||
| 12. Bidirectional RNN Architecture.mp4 | 5.12 MB | ||
| 12. Bidirectional RNN Architecture.vtt | 4.26 KB | ||
| 3. Implementing Character Recognition Using Bidirectional RNNs | |||
| 01. Module Overview.mp4 | 1.87 MB | ||
| 01. Module Overview.vtt | 1.92 KB | ||
| 02. Running Jupyter Notebook and Import Statements.mp4 | 4.51 MB | ||
| 02. Running Jupyter Notebook and Import Statements.vtt | 4.42 KB | ||
| 03. Download and Parse OCR File.mp4 | 4.46 MB | ||
| 03. Download and Parse OCR File.vtt | 3.42 KB | ||
| 04. Features and Labels.mp4 | 13.71 MB | ||
| 04. Features and Labels.vtt | 10.13 KB | ||
| 05. Shuffle and Feed in Training Data.mp4 | 5.76 MB | ||
| 05. Shuffle and Feed in Training Data.vtt | 4.9 KB | ||
| 06. Sequence Length Calculations.mp4 | 3.49 MB | ||
| 06. Sequence Length Calculations.vtt | 2.94 KB | ||
| 07. Building the RNN.mp4 | 12.95 MB | ||
| 07. Building the RNN.vtt | 10.13 KB | ||
| 08. Training and Evaluating the RNN.mp4 | 11.11 MB | ||
| 08. Training and Evaluating the RNN.vtt | 9.08 KB | ||
| 09. Manually Setup the Bidirectional RNN.mp4 | 17.34 MB | ||
| 09. Manually Setup the Bidirectional RNN.vtt | 8.77 KB | ||
| 10. Bidirectional RNN Using the TF Library.mp4 | 6.6 MB | ||
| 10. Bidirectional RNN Using the TF Library.vtt | 3.81 KB | ||
| 4. Applying RNNs to Character Prediction for Text Generation | |||
| 1. Module Overview.mp4 | 2.63 MB | ||
| 1. Module Overview.vtt | 2.34 KB | ||
| 2. Using Neural Networks for Natural Language Processing.mp4 | 6.84 MB | ||
| 2. Using Neural Networks for Natural Language Processing.vtt | 6.6 KB | ||
| 3. Language Modeling Problems.mp4 | 6.05 MB | ||
| 3. Language Modeling Problems.vtt | 6.85 KB | ||
| 4. The Multi-RNN Cell.mp4 | 8.82 MB | ||
| 4. The Multi-RNN Cell.vtt | 7.92 KB | ||
| 5. Generate Training Data and Labels Using a Sliding Window.mp4 | 8.35 MB | ||
| 5. Generate Training Data and Labels Using a Sliding Window.vtt | 6.51 KB | ||
| 6. Text Generation Using Character Prediction.mp4 | 1.86 MB | ||
| 6. Text Generation Using Character Prediction.vtt | 2.17 KB | ||
| 7. RNN Architecture for Text Prediction.mp4 | 9.44 MB | ||
| 7. RNN Architecture for Text Prediction.vtt | 8.3 KB | ||
| 8. Understanding Perplexity.mp4 | 8.57 MB | ||
| 8. Understanding Perplexity.vtt | 8.38 KB | ||
| 5. Implementing RNNs for Character Prediction Used to Generate Text | |||
| 1. Module Overview.mp4 | 1.68 MB | ||
| 1. Module Overview.vtt | 1.46 KB | ||
| 2. Character Prediction - Retrieve Data from ArXiv.o.mp4 | 16.46 MB | ||
| 2. Character Prediction - Retrieve Data from ArXiv.o.vtt | 8.38 KB | ||
| 3. Representing Characters in One Hot Encoding.mp4 | 8.48 MB | ||
| 3. Representing Characters in One Hot Encoding.vtt | 5.58 KB | ||
| 4. Training the Model.mp4 | 16.26 MB | ||
| 4. Training the Model.vtt | 10.39 KB | ||
| 5. Build the RNN for Prediction.mp4 | 8.43 MB | ||
| 5. Build the RNN for Prediction.vtt | 4.73 KB | ||
| 6. Text Generation Using Character Prediction.mp4 | 20.68 MB | ||
| 6. Text Generation Using Character Prediction.vtt | 12.69 KB | ||
| 7. Summary and Further Reading.mp4 | 2.03 MB | ||
| 7. Summary and Further Reading.vtt | 2.07 KB | ||
| exercise.7z | 3.19 MB | ||
| playlist.m3u | 3.8 KB | ||
| ~i.txt | 1.82 KB | ||
| C2. Implementing Image Recognition Systems with TensorFlow (Jon Flanders, 2019) | |||
| 1. Course Overview | |||
| 1. Course Overview.mp4 | 2.49 MB | ||
| 1. Course Overview.vtt | 1.71 KB | ||
| 2. Introduction | |||
| 1. Introduction.mp4 | 4.33 MB | ||
| 1. Introduction.vtt | 4.45 KB | ||
| 2. Some ML Imaging Basics.mp4 | 6.17 MB | ||
| 2. Some ML Imaging Basics.vtt | 5.12 KB | ||
| 3. ML and Imaging.mp4 | 7.93 MB | ||
| 3. ML and Imaging.vtt | 5.87 KB | ||
| 4. Over and Underfitting.mp4 | 2.85 MB | ||
| 4. Over and Underfitting.vtt | 2.84 KB | ||
| 5. Summary.mp4 | 252.39 KB | ||
| 5. Summary.vtt | 265 B | ||
| 3. Picking and Using a Model | |||
| 1. Introduction.mp4 | 2.57 MB | ||
| 1. Introduction.vtt | 2.46 KB | ||
| 2. Steps for Picking a Model.mp4 | 2.46 MB | ||
| 2. Steps for Picking a Model.vtt | 2.36 KB | ||
| 3. Picking Your Model.mp4 | 1.14 MB | ||
| 3. Picking Your Model.vtt | 1.16 KB | ||
| 4. Demo - Using a TensorFlow Model.mp4 | 10.94 MB | ||
| 4. Demo - Using a TensorFlow Model.vtt | 4.32 KB | ||
| 5. Demo - Creating a Session and Using Tensorboard.mp4 | 14.44 MB | ||
| 5. Demo - Creating a Session and Using Tensorboard.vtt | 5.3 KB | ||
| 6. Demo - Getting Predictions.mp4 | 8.99 MB | ||
| 6. Demo - Getting Predictions.vtt | 2.12 KB | ||
| 7. Demo - Predictions into Human Readable Names.mp4 | 34.12 MB | ||
| 7. Demo - Predictions into Human Readable Names.vtt | 7.92 KB | ||
| 4. Transfer Learning | |||
| 1. Introduction.mp4 | 6.75 MB | ||
| 1. Introduction.vtt | 7.29 KB | ||
| 2. Transfer Learning Limitations.mp4 | 2.01 MB | ||
| 2. Transfer Learning Limitations.vtt | 1.19 KB | ||
| 3. Demo - Transfer Learning.mp4 | 10.59 MB | ||
| 3. Demo - Transfer Learning.vtt | 2.89 KB | ||
| 4. Demo - Preparing the Images.mp4 | 27.54 MB | ||
| 4. Demo - Preparing the Images.vtt | 9.66 KB | ||
| 5. Demo - TensorFlow Hub and Retraining.mp4 | 29.85 MB | ||
| 5. Demo - TensorFlow Hub and Retraining.vtt | 9.43 KB | ||
| 6. Demo - Code Updates to Run the New Model.mp4 | 29.56 MB | ||
| 6. Demo - Code Updates to Run the New Model.vtt | 9.36 KB | ||
| 7. Summary.mp4 | 643.95 KB | ||
| 7. Summary.vtt | 452 B | ||
| 5. Localization and Segmentation | |||
| 1. Introduction.mp4 | 3.7 MB | ||
| 1. Introduction.vtt | 2.63 KB | ||
| 2. Localization.mp4 | 4.72 MB | ||
| 2. Localization.vtt | 3.3 KB | ||
| 3. Segementation.mp4 | 33.35 MB | ||
| 3. Segementation.vtt | 11.19 KB | ||
| 4. Demo - Segmentation.mp4 | 30.35 MB | ||
| 4. Demo - Segmentation.vtt | 8.81 KB | ||
| 5. Summary.mp4 | 596.93 KB | ||
| 5. Summary.vtt | 537 B | ||
| 6. Face Recognition | |||
| 1. Introduction.mp4 | 2.15 MB | ||
| 1. Introduction.vtt | 1.89 KB | ||
| 2. Demo - Using Facenet.mp4 | 22.54 MB | ||
| 2. Demo - Using Facenet.vtt | 7.12 KB | ||
| 3. Demo - Building the Classifier.mp4 | 19.61 MB | ||
| 3. Demo - Building the Classifier.vtt | 6.32 KB | ||
| 4. Summary.mp4 | 599.21 KB | ||
| 4. Summary.vtt | 412 B | ||
| exercise.7z | 527.93 MB | ||
| playlist.m3u | 1.49 KB | ||
| ~i.txt | 1.61 KB | ||
| C3. Implementing Predictive Analytics with TensorFlow (Justin Flett, 2018) | |||
| 1. Course Overview | |||
| 1. Course Overview.mp4 | 4.06 MB | ||
| 1. Course Overview.vtt | 2.22 KB | ||
| 2. Implementing Supervised Learning Systems | |||
| 1. Introduction.mp4 | 1.07 MB | ||
| 1. Introduction.vtt | 914 B | ||
| 2. Understanding Supervised Learning and Linear Regression.mp4 | 6.01 MB | ||
| 2. Understanding Supervised Learning and Linear Regression.vtt | 4.44 KB | ||
| 3. Implementing Linear Regression.mp4 | 40.72 MB | ||
| 3. Implementing Linear Regression.vtt | 16.41 KB | ||
| 4. Understanding Neural Networks.mp4 | 3.57 MB | ||
| 4. Understanding Neural Networks.vtt | 3.54 KB | ||
| 5. Implementing Neural Networks.mp4 | 16.57 MB | ||
| 5. Implementing Neural Networks.vtt | 6.19 KB | ||
| 6. Summary.mp4 | 1.32 MB | ||
| 6. Summary.vtt | 1.26 KB | ||
| 3. Implementing Recommendation Systems | |||
| 1. Introduction.mp4 | 1.79 MB | ||
| 1. Introduction.vtt | 1.33 KB | ||
| 2. Understanding Recommendation Learning Systems.mp4 | 7.94 MB | ||
| 2. Understanding Recommendation Learning Systems.vtt | 6.34 KB | ||
| 3. Understanding Matrix Factorization.mp4 | 4.69 MB | ||
| 3. Understanding Matrix Factorization.vtt | 4.64 KB | ||
| 4. Implementing a Small-scale Collaborative Filtering System.mp4 | 23.51 MB | ||
| 4. Implementing a Small-scale Collaborative Filtering System.vtt | 10.81 KB | ||
| 5. Implementing a Larger-scale Collaborative Filtering System.mp4 | 13.31 MB | ||
| 5. Implementing a Larger-scale Collaborative Filtering System.vtt | 5.27 KB | ||
| 6. Summary.mp4 | 3.16 MB | ||
| 6. Summary.vtt | 2.71 KB | ||
| 4. Implementing Reinforcement Learning Systems | |||
| 1. Introduction.mp4 | 1.32 MB | ||
| 1. Introduction.vtt | 1.11 KB | ||
| 2. Understanding Reinforcement Learning Systems.mp4 | 7.11 MB | ||
| 2. Understanding Reinforcement Learning Systems.vtt | 6.49 KB | ||
| 3. Implementing a Simple Reinforcement Learning System.mp4 | 24.4 MB | ||
| 3. Implementing a Simple Reinforcement Learning System.vtt | 12.84 KB | ||
| 4. Understanding Markov Decision Process and Policy-based Agents.mp4 | 5.15 MB | ||
| 4. Understanding Markov Decision Process and Policy-based Agents.vtt | 4.64 KB | ||
| 5. Further Learning and Next Steps.mp4 | 3.53 MB | ||
| 5. Further Learning and Next Steps.vtt | 3.47 KB | ||
| 6. Summary.mp4 | 2.76 MB | ||
| 6. Summary.vtt | 2.4 KB | ||
| exercise.7z | 2.4 MB | ||
| playlist.m3u | 1.51 KB | ||
| ~i.txt | 1.48 KB | ||
| C4. Sentiment Analysis with Recurrent Neural Networks in TensorFlow (Janani Ravi, 2017) | |||
| 1. Course Overview | |||
| 1. Course Overview.mp4 | 3.4 MB | ||
| 1. Course Overview.vtt | 2.39 KB | ||
| 2. Applying Word Vector Embeddings to Language Modeling | |||
| 1. Version Check.mp4 | 724.55 KB | ||
| 1. Version Check.vtt | 7 B | ||
| 2. Classification as a Machine Learning Problem.mp4 | 4.48 MB | ||
| 2. Classification as a Machine Learning Problem.vtt | 4.41 KB | ||
| 3. Prerequisites and Software.mp4 | 3.38 MB | ||
| 3. Prerequisites and Software.vtt | 3.48 KB | ||
| 4. A Rule-based System for Sentiment Analysis.mp4 | 7.16 MB | ||
| 4. A Rule-based System for Sentiment Analysis.vtt | 7.26 KB | ||
| 5. An Introduction to Neural Networks.mp4 | 10.52 MB | ||
| 5. An Introduction to Neural Networks.vtt | 8.71 KB | ||
| 6. One-hot Encoding.mp4 | 7.07 MB | ||
| 6. One-hot Encoding.vtt | 6.46 KB | ||
| 7. Frequency-based Embeddings.mp4 | 11.98 MB | ||
| 7. Frequency-based Embeddings.vtt | 11.93 KB | ||
| 8. Prediction-based Embeddings.mp4 | 7.56 MB | ||
| 8. Prediction-based Embeddings.vtt | 7.27 KB | ||
| 9. Introducing Word2Vec.mp4 | 5.27 MB | ||
| 9. Introducing Word2Vec.vtt | 5.04 KB | ||
| 3. Implementing Word Embeddings in TensorFlow | |||
| 01. Overview.mp4 | 2.6 MB | ||
| 01. Overview.vtt | 2.24 KB | ||
| 02. Maximum Likelihood Estimation.mp4 | 6.78 MB | ||
| 02. Maximum Likelihood Estimation.vtt | 7.09 KB | ||
| 03. The Continuous Bag of Words Neural Network.mp4 | 8.02 MB | ||
| 03. The Continuous Bag of Words Neural Network.vtt | 7.8 KB | ||
| 04. The Skip-gram Neural Network.mp4 | 2.11 MB | ||
| 04. The Skip-gram Neural Network.vtt | 1.77 KB | ||
| 05. Useful Python Packages.mp4 | 5.95 MB | ||
| 05. Useful Python Packages.vtt | 2.41 KB | ||
| 06. Demo - Download Data and Extract Words.mp4 | 9.02 MB | ||
| 06. Demo - Download Data and Extract Words.vtt | 7.45 KB | ||
| 07. Demo - Build and Prepare Dataset.mp4 | 8.19 MB | ||
| 07. Demo - Build and Prepare Dataset.vtt | 6.16 KB | ||
| 08. Demo - Generate Training Batches.mp4 | 12.09 MB | ||
| 08. Demo - Generate Training Batches.vtt | 8.42 KB | ||
| 09. Demo - Contruct the Neural Network.mp4 | 14.09 MB | ||
| 09. Demo - Contruct the Neural Network.vtt | 11.78 KB | ||
| 10. Demo - Train the Neural Network.mp4 | 14.24 MB | ||
| 10. Demo - Train the Neural Network.vtt | 8.2 KB | ||
| 11. Noise Contrastive Estimators to Measure Loss.mp4 | 7.09 MB | ||
| 11. Noise Contrastive Estimators to Measure Loss.vtt | 6.81 KB | ||
| 12. Demo - Implementing Noise Contrastive Estimation.mp4 | 30.09 MB | ||
| 12. Demo - Implementing Noise Contrastive Estimation.vtt | 11.93 KB | ||
| 13. Summary.mp4 | 1.3 MB | ||
| 13. Summary.vtt | 1.15 KB | ||
| 4. Performing Sequence Classification with RNNs | |||
| 1. Text as Sequential Data.mp4 | 5.96 MB | ||
| 1. Text as Sequential Data.vtt | 5.62 KB | ||
| 2. The Recurrent Neuron.mp4 | 6.52 MB | ||
| 2. The Recurrent Neuron.vtt | 6.85 KB | ||
| 3. Input Sequence as a Time Step.mp4 | 2.74 MB | ||
| 3. Input Sequence as a Time Step.vtt | 2.59 KB | ||
| 4. Back Propagation Through Time.mp4 | 12.33 MB | ||
| 4. Back Propagation Through Time.vtt | 11.01 KB | ||
| 5. Long Term Memory.mp4 | 7.21 MB | ||
| 5. Long Term Memory.vtt | 7.02 KB | ||
| 6. The LSTM Cell.mp4 | 7.71 MB | ||
| 6. The LSTM Cell.vtt | 7.15 KB | ||
| 5. Implementing Sequence Classification Using RNNs in TensorFlow | |||
| 1. Naive Bayes Intuition.mp4 | 10.17 MB | ||
| 1. Naive Bayes Intuition.vtt | 10.05 KB | ||
| 2. Demo - Implementing Naive Bayes as a Baseline.mp4 | 31 MB | ||
| 2. Demo - Implementing Naive Bayes as a Baseline.vtt | 14.94 KB | ||
| 3. Drawbacks of Naive Bayes.mp4 | 2.18 MB | ||
| 3. Drawbacks of Naive Bayes.vtt | 2.04 KB | ||
| 4. Demo - Data Preparation for Classification Using RN.mp4 | 13.69 MB | ||
| 4. Demo - Data Preparation for Classification Using RN.vtt | 7.11 KB | ||
| 5. Demo - Build and Run the Neural Network.mp4 | 19.75 MB | ||
| 5. Demo - Build and Run the Neural Network.vtt | 12.18 KB | ||
| 6. Advantages of RNNs for Sentiment Analysis.mp4 | 3.42 MB | ||
| 6. Advantages of RNNs for Sentiment Analysis.vtt | 3.06 KB | ||
| 7. Demo - Use Pre-trained GloVe Embeddings for Classif.mp4 | 18.86 MB | ||
| 7. Demo - Use Pre-trained GloVe Embeddings for Classif.vtt | 9.19 KB | ||
| 8. Summary and Further Learning.mp4 | 3.75 MB | ||
| 8. Summary and Further Learning.vtt | 3.53 KB | ||
| exercise.7z | 131.26 MB | ||
| playlist.m3u | 3.26 KB | ||
| ~i.txt | 1.89 KB | ||
| scr.png | 187.9 KB | ||
| ~i.txt | 1.07 KB | ||
| Pluralsight Path. Building Machine Learning Solutions with TensorFlow 2.0 (2020) | |||
| A1. Getting Started with TensorFlow 2.0 (Janani Ravi, 2020) | |||
| 1. Course Overview | |||
| 1. Course Overview.mp4 | 4.25 MB | ||
| 1. Course Overview.vtt | 3.16 KB | ||
| 2. Exploring the TensorFlow 2.0 Framework | |||
| 1. Version Check.mp4 | 509.81 KB | ||
| 1. Version Check.vtt | 7 B | ||
| 2. Prerequisites and Course Outline.mp4 | 3.86 MB | ||
| 2. Prerequisites and Course Outline.vtt | 3.73 KB | ||
| 3. TensorFlow 1.x vs. TensorFlow 2.0.mp4 | 12.38 MB | ||
| 3. TensorFlow 1.x vs. TensorFlow 2.0.vtt | 10.11 KB | ||
| 4. Introducing Neural Networks.mp4 | 7.77 MB | ||
| 4. Introducing Neural Networks.vtt | 6.09 KB | ||
| 5. Neurons and Activation Functions.mp4 | 14.24 MB | ||
| 5. Neurons and Activation Functions.vtt | 10.27 KB | ||
| 6. Demo - Install and Set up TensorFlow.mp4 | 9.41 MB | ||
| 6. Demo - Install and Set up TensorFlow.vtt | 5.16 KB | ||
| 7. Demo - Tensors and Tensor Operations.mp4 | 13.85 MB | ||
| 7. Demo - Tensors and Tensor Operations.vtt | 8.97 KB | ||
| 8. Demo - Variables.mp4 | 11.08 MB | ||
| 8. Demo - Variables.vtt | 6.37 KB | ||
| 9. TensorFlow and Keras.mp4 | 3.44 MB | ||
| 9. TensorFlow and Keras.vtt | 3 KB | ||
| 3. Understanding Dynamic and Static Computation Graphs | |||
| 1. The Computation Graph.mp4 | 5.81 MB | ||
| 1. The Computation Graph.vtt | 4.6 KB | ||
| 2. Static and Dynamic Computation Graphs.mp4 | 13.29 MB | ||
| 2. Static and Dynamic Computation Graphs.vtt | 10.75 KB | ||
| 3. Demo - TensorFlow V1 Sessions to Execute Static Computation Graphs.mp4 | 14.79 MB | ||
| 3. Demo - TensorFlow V1 Sessions to Execute Static Computation Graphs.vtt | 9.24 KB | ||
| 4. Demo - TensorBoard to Visualize Graphs.mp4 | 4.83 MB | ||
| 4. Demo - TensorBoard to Visualize Graphs.vtt | 2.91 KB | ||
| 5. Demo - Eager Execution.mp4 | 7.92 MB | ||
| 5. Demo - Eager Execution.vtt | 5.07 KB | ||
| 6. tf.function.mp4 | 14.13 MB | ||
| 6. tf.function.vtt | 10.47 KB | ||
| 7. Demo - Running in Graph Mode Using @tf.function.mp4 | 11.46 MB | ||
| 7. Demo - Running in Graph Mode Using @tf.function.vtt | 7.84 KB | ||
| 8. Demo - Statements with Python Side Effects in Graph Mode.mp4 | 9.73 MB | ||
| 8. Demo - Statements with Python Side Effects in Graph Mode.vtt | 6.27 KB | ||
| 9. Demo - Instantiating Variables in Graph Mode.mp4 | 10.42 MB | ||
| 9. Demo - Instantiating Variables in Graph Mode.vtt | 6.39 KB | ||
| 4. Computing Gradients for Model Training | |||
| 1. Gradient Descent.mp4 | 9.89 MB | ||
| 1. Gradient Descent.vtt | 8.64 KB | ||
| 2. Forward and Backward Passes.mp4 | 5.25 MB | ||
| 2. Forward and Backward Passes.vtt | 3.67 KB | ||
| 3. Calculating Gradients Using Gradient Tape.mp4 | 9.21 MB | ||
| 3. Calculating Gradients Using Gradient Tape.vtt | 7.59 KB | ||
| 4. Reverse Mode Automatic Differentiation.mp4 | 7.28 MB | ||
| 4. Reverse Mode Automatic Differentiation.vtt | 5.7 KB | ||
| 5. Demo - Gradient Tape for Gradient Calculations.mp4 | 8.29 MB | ||
| 5. Demo - Gradient Tape for Gradient Calculations.vtt | 5.72 KB | ||
| 6. Demo - Understanding Gradient Tape Operations.mp4 | 10.42 MB | ||
| 6. Demo - Understanding Gradient Tape Operations.vtt | 7.21 KB | ||
| 7. Demo - Simple Regression Using Gradient Calculation.mp4 | 19.49 MB | ||
| 7. Demo - Simple Regression Using Gradient Calculation.vtt | 10.67 KB | ||
| 8. Demo - Simple Regression with a Sequential Model.mp4 | 7.82 MB | ||
| 8. Demo - Simple Regression with a Sequential Model.vtt | 5.46 KB | ||
| 5. Using the Sequential API in Keras | |||
| 1. Introducing the Sequential API in Keras.mp4 | 9.83 MB | ||
| 1. Introducing the Sequential API in Keras.vtt | 8.37 KB | ||
| 2. Demo - Exploring and Processing the Life Expectancy Dataset.mp4 | 20.06 MB | ||
| 2. Demo - Exploring and Processing the Life Expectancy Dataset.vtt | 12.37 KB | ||
| 3. Demo - Building and Training a Sequential Model.mp4 | 11.5 MB | ||
| 3. Demo - Building and Training a Sequential Model.vtt | 6.72 KB | ||
| 4. Demo - TensorBoard to Visualize the Training Process.mp4 | 17.7 MB | ||
| 4. Demo - TensorBoard to Visualize the Training Process.vtt | 10.72 KB | ||
| 5. Demo - Configuring Optimizers and Activation Functions.mp4 | 12.01 MB | ||
| 5. Demo - Configuring Optimizers and Activation Functions.vtt | 6.83 KB | ||
| 6. Using the Functional API and Model Subclassing in Keras | |||
| 1. The Functional API and Model Subclassing.mp4 | 8.09 MB | ||
| 1. The Functional API and Model Subclassing.vtt | 6.99 KB | ||
| 2. Demo - Exploring the Heart Disease Dataset.mp4 | 10.97 MB | ||
| 2. Demo - Exploring the Heart Disease Dataset.vtt | 7.01 KB | ||
| 3. Demo - Building a Model Using the Keras Functional API.mp4 | 16.38 MB | ||
| 3. Demo - Building a Model Using the Keras Functional API.vtt | 9.26 KB | ||
| 4. Demo - Exploring and Processing the Wine Dataset.mp4 | 9.86 MB | ||
| 4. Demo - Exploring and Processing the Wine Dataset.vtt | 5.51 KB | ||
| 5. Demo - Building and Training a Multi Class Classification Model Using Model Subclassi.mp4 | 13.89 MB | ||
| 5. Demo - Building and Training a Multi Class Classification Model Using Model Subclassi.vtt | 7.97 KB | ||
| 6. Summary and Further Study.mp4 | 2.21 MB | ||
| 6. Summary and Further Study.vtt | 2.44 KB | ||
| exercise.7z | 2.98 MB | ||
| playlist.m3u | 3.45 KB | ||
| ~i.txt | 2.52 KB | ||
| A2. Installation Guide for TensorFlow 2.0 (Omotayo Aina, 2020).chm | 573.53 KB | ||
| B1. Designing Data Pipelines with TensorFlow 2.0 (Chase DeHan, 2020) | |||
| 1. Course Overview | |||
| 1. Course Overview.mp4 | 3.11 MB | ||
| 1. Course Overview.vtt | 1.87 KB | ||
| 2. Evaluating TensorFlow Capabilities | |||
| 1. Introduction.mp4 | 3.21 MB | ||
| 1. Introduction.vtt | 2.79 KB | ||
| 2. TensorFlow 2.0 Introduction.mp4 | 4.79 MB | ||
| 2. TensorFlow 2.0 Introduction.vtt | 4.57 KB | ||
| 3. Migrating to TensorFlow 2.0.mp4 | 3.62 MB | ||
| 3. Migrating to TensorFlow 2.0.vtt | 3.63 KB | ||
| 4. Data Pipeline and Model Training.mp4 | 21.92 MB | ||
| 4. Data Pipeline and Model Training.vtt | 11.52 KB | ||
| 5. Conclusion.mp4 | 641.03 KB | ||
| 5. Conclusion.vtt | 653 B | ||
| 3. Loading Data in TensorFlow | |||
| 1. Introduction.mp4 | 4.03 MB | ||
| 1. Introduction.vtt | 3.47 KB | ||
| 2. Load from CSV.mp4 | 21.68 MB | ||
| 2. Load from CSV.vtt | 9 KB | ||
| 3. Load from NumPy and pandas.mp4 | 22.16 MB | ||
| 3. Load from NumPy and pandas.vtt | 10.94 KB | ||
| 4. TFExample.mp4 | 20.95 MB | ||
| 4. TFExample.vtt | 11.6 KB | ||
| 5. TFRecord.mp4 | 15.41 MB | ||
| 5. TFRecord.vtt | 8.97 KB | ||
| 6. Load Image Data.mp4 | 11.13 MB | ||
| 6. Load Image Data.vtt | 5.97 KB | ||
| 7. Conclusion.mp4 | 1.34 MB | ||
| 7. Conclusion.vtt | 1.58 KB | ||
| 4. Prepping Data | |||
| 1. Introduction.mp4 | 3.21 MB | ||
| 1. Introduction.vtt | 3 KB | ||
| 2. Feature Engineering with pandas.mp4 | 29.2 MB | ||
| 2. Feature Engineering with pandas.vtt | 17.42 KB | ||
| 3. Using Zip and Map.mp4 | 9.35 MB | ||
| 3. Using Zip and Map.vtt | 3.63 KB | ||
| 4. Load Image Data.mp4 | 20.45 MB | ||
| 4. Load Image Data.vtt | 9.65 KB | ||
| 5. Image Data Augmentation.mp4 | 23.21 MB | ||
| 5. Image Data Augmentation.vtt | 11.86 KB | ||
| 6. Conclusion.mp4 | 1.06 MB | ||
| 6. Conclusion.vtt | 963 B | ||
| 5. Optimizing Performance of Pipelines | |||
| 1. Introduction.mp4 | 3.07 MB | ||
| 1. Introduction.vtt | 2.74 KB | ||
| 2. Prep Data for Model Training.mp4 | 14.11 MB | ||
| 2. Prep Data for Model Training.vtt | 7.4 KB | ||
| 3. Use Keras Sequential API.mp4 | 18.32 MB | ||
| 3. Use Keras Sequential API.vtt | 8.98 KB | ||
| 4. Batching and Prefetching.mp4 | 12.66 MB | ||
| 4. Batching and Prefetching.vtt | 7 KB | ||
| 5. Parallelizing Data Extraction.mp4 | 9.75 MB | ||
| 5. Parallelizing Data Extraction.vtt | 9.06 KB | ||
| 6. Conclusion.mp4 | 1.29 MB | ||
| 6. Conclusion.vtt | 1.33 KB | ||
| exercise.7z | 6.07 MB | ||
| playlist.m3u | 1.37 KB | ||
| ~i.txt | 1.37 KB | ||
| B2. Implement Hyperparameter Tuning for TensorFlow 2.0 (Gaurav Singhal, 2020).chm | 490.23 KB | ||
| B3. Building Machine Learning Solutions with TensorFlow.js (Abhishek Kumar, 2020) | |||
| 01. Course Overview | |||
| 1. Course Overview.mp4 | 5.45 MB | ||
| 1. Course Overview.vtt | 2.64 KB | ||
| 02. Introduction | |||
| 1. Introduction.mp4 | 7.25 MB | ||
| 1. Introduction.vtt | 4.76 KB | ||
| 2. Why TensorFlow.js.mp4 | 5.8 MB | ||
| 2. Why TensorFlow.js.vtt | 5.22 KB | ||
| 3. TensorFlow.js Performance.mp4 | 3.89 MB | ||
| 3. TensorFlow.js Performance.vtt | 3.43 KB | ||
| 4. TensorFlow.js Overview.mp4 | 4.63 MB | ||
| 4. TensorFlow.js Overview.vtt | 3.92 KB | ||
| 5. Course Demo.mp4 | 6.49 MB | ||
| 5. Course Demo.vtt | 5.23 KB | ||
| 6. Course Structure.mp4 | 3.14 MB | ||
| 6. Course Structure.vtt | 3.36 KB | ||
| 03. Setting up TensorFlow.js Environment | |||
| 1. Introduction.mp4 | 2.02 MB | ||
| 1. Introduction.vtt | 2 KB | ||
| 2. TensorFlow.js in Browser Using Script Tag.mp4 | 1.23 MB | ||
| 2. TensorFlow.js in Browser Using Script Tag.vtt | 1.19 KB | ||
| 3. Demo - Running TensorFlow.js in Browser with Script Tag.mp4 | 14.88 MB | ||
| 3. Demo - Running TensorFlow.js in Browser with Script Tag.vtt | 6.85 KB | ||
| 4. TensorFlow.js in Browser Using Package Managers.mp4 | 4.1 MB | ||
| 4. TensorFlow.js in Browser Using Package Managers.vtt | 3.61 KB | ||
| 5. Demo - Running TensorFlow.js in Browser Using NPM and Parcel.mp4 | 21.77 MB | ||
| 5. Demo - Running TensorFlow.js in Browser Using NPM and Parcel.vtt | 8.56 KB | ||
| 6. Demo - Exploring TensorFlow.js Backends.mp4 | 18.26 MB | ||
| 6. Demo - Exploring TensorFlow.js Backends.vtt | 7.37 KB | ||
| 7. Demo - Running TensorFlow.js in Node.js.mp4 | 16.09 MB | ||
| 7. Demo - Running TensorFlow.js in Node.js.vtt | 6.52 KB | ||
| 8. Summary.mp4 | 1.76 MB | ||
| 8. Summary.vtt | 1.68 KB | ||
| 04. Understanding TensorFlow.js Core Concepts | |||
| 1. Introduction.mp4 | 2.6 MB | ||
| 1. Introduction.vtt | 2.72 KB | ||
| 2. Tensor Overview.mp4 | 5.39 MB | ||
| 2. Tensor Overview.vtt | 4.86 KB | ||
| 3. Demo - Working with Tensors.mp4 | 6.36 MB | ||
| 3. Demo - Working with Tensors.vtt | 2.99 KB | ||
| 4. Basic Tensor Operations.mp4 | 2.58 MB | ||
| 4. Basic Tensor Operations.vtt | 1.66 KB | ||
| 5. Demo - Performing Basic Tensor Operations.mp4 | 11.73 MB | ||
| 5. Demo - Performing Basic Tensor Operations.vtt | 5.38 KB | ||
| 6. Managing Memory with TensorFlow.js.mp4 | 4.39 MB | ||
| 6. Managing Memory with TensorFlow.js.vtt | 4.48 KB | ||
| 7. Demo - Managing Memory with TensorFlow.js.mp4 | 5.91 MB | ||
| 7. Demo - Managing Memory with TensorFlow.js.vtt | 2.59 KB | ||
| 8. Summary.mp4 | 2.4 MB | ||
| 8. Summary.vtt | 2.57 KB | ||
| 05. Preparing Data for Machine Learning Model - Part 1 | |||
| 1. Introduction.mp4 | 2.27 MB | ||
| 1. Introduction.vtt | 2.28 KB | ||
| 2. Machine Learning Workflow.mp4 | 3 MB | ||
| 2. Machine Learning Workflow.vtt | 2.31 KB | ||
| 3. Toxicity Detection Use Case.mp4 | 6.42 MB | ||
| 3. Toxicity Detection Use Case.vtt | 5.08 KB | ||
| 4. Working with TFJS Data.mp4 | 3.57 MB | ||
| 4. Working with TFJS Data.vtt | 3.49 KB | ||
| 5. Async JS Programming.mp4 | 9.07 MB | ||
| 5. Async JS Programming.vtt | 7.84 KB | ||
| 6. Demo - Reading Data Using TFJS Data.mp4 | 23.9 MB | ||
| 6. Demo - Reading Data Using TFJS Data.vtt | 10.59 KB | ||
| 7. Working with TFVis.mp4 | 2.4 MB | ||
| 7. Working with TFVis.vtt | 1.78 KB | ||
| 8. Demo - Visualizing Data Using TFVis.mp4 | 15.87 MB | ||
| 8. Demo - Visualizing Data Using TFVis.vtt | 6.01 KB | ||
| 9. Summary.mp4 | 2.44 MB | ||
| 9. Summary.vtt | 2.2 KB | ||
| 06. Preparing Data for Machine Learning Model - Part 2 | |||
| 1. Introduction.mp4 | 2.59 MB | ||
| 1. Introduction.vtt | 2.5 KB | ||
| 2. Generating Features from Text.mp4 | 10.45 MB | ||
| 2. Generating Features from Text.vtt | 6.48 KB | ||
| 3. Demo - Generating TFIDF Features.mp4 | 36.48 MB | ||
| 3. Demo - Generating TFIDF Features.vtt | 12.87 KB | ||
| 4. Function Generator.mp4 | 2.78 MB | ||
| 4. Function Generator.vtt | 2.5 KB | ||
| 5. Demo - Creating Feature Dataset Using Generators.mp4 | 6.33 MB | ||
| 5. Demo - Creating Feature Dataset Using Generators.vtt | 3 KB | ||
| 6. Train Validation Test Split.mp4 | 3.67 MB | ||
| 6. Train Validation Test Split.vtt | 2.67 KB | ||
| 7. Demo - Splitting Data into Train Validation and Test Datasets.mp4 | 12.53 MB | ||
| 7. Demo - Splitting Data into Train Validation and Test Datasets.vtt | 4.87 KB | ||
| 8. Summary.mp4 | 1.88 MB | ||
| 8. Summary.vtt | 1.84 KB | ||
| 07. Building, Training, and Evaluating Machine Learning Model | |||
| 01. Introduction.mp4 | 2.76 MB | ||
| 01. Introduction.vtt | 2.6 KB | ||
| 02. Neural Network Overview.mp4 | 6.11 MB | ||
| 02. Neural Network Overview.vtt | 5.04 KB | ||
| 03. Building Neural Network Using Layers API.mp4 | 4.21 MB | ||
| 03. Building Neural Network Using Layers API.vtt | 3.39 KB | ||
| 04. Demo - Building Neural Network Using Layers API.mp4 | 9.88 MB | ||
| 04. Demo - Building Neural Network Using Layers API.vtt | 3.72 KB | ||
| 05. Training Model Using TensorFlow.js.mp4 | 5.11 MB | ||
| 05. Training Model Using TensorFlow.js.vtt | 4.84 KB | ||
| 06. Demo - Training Neural Network Model.mp4 | 11.75 MB | ||
| 06. Demo - Training Neural Network Model.vtt | 4.21 KB | ||
| 07. Demo - Visualizing Training Performance.mp4 | 7.13 MB | ||
| 07. Demo - Visualizing Training Performance.vtt | 2.59 KB | ||
| 08. Demo - Evaluating Model Performance.mp4 | 6 MB | ||
| 08. Demo - Evaluating Model Performance.vtt | 2.84 KB | ||
| 09. Model Performance Metrics.mp4 | 2.43 MB | ||
| 09. Model Performance Metrics.vtt | 2.44 KB | ||
| 10. Demo - Visualizing Model Performance Metrics.mp4 | 11.74 MB | ||
| 10. Demo - Visualizing Model Performance Metrics.vtt | 4.42 KB | ||
| 11. Demo - Running Training in Node.js.mp4 | 7.56 MB | ||
| 11. Demo - Running Training in Node.js.vtt | 3.05 KB | ||
| 12. Summary.mp4 | 2.24 MB | ||
| 12. Summary.vtt | 1.91 KB | ||
| 08. Saving and Loading Machine Learning Model | |||
| 1. Introduction.mp4 | 1.52 MB | ||
| 1. Introduction.vtt | 1.34 KB | ||
| 2. Model Export Options.mp4 | 2.65 MB | ||
| 2. Model Export Options.vtt | 2.19 KB | ||
| 3. Demo - Exporting Trained Model.mp4 | 10.59 MB | ||
| 3. Demo - Exporting Trained Model.vtt | 3.7 KB | ||
| 4. Load Model.mp4 | 1.75 MB | ||
| 4. Load Model.vtt | 1.38 KB | ||
| 5. Demo - Loading Trained TensorFlow.js Model.mp4 | 3.68 MB | ||
| 5. Demo - Loading Trained TensorFlow.js Model.vtt | 1.57 KB | ||
| 6. Summary.mp4 | 1.81 MB | ||
| 6. Summary.vtt | 1.44 KB | ||
| 09. Predicting Using Trained Machine Learning Model | |||
| 1. Introduction.mp4 | 2.17 MB | ||
| 1. Introduction.vtt | 1.78 KB | ||
| 2. Model Scoring.mp4 | 2.94 MB | ||
| 2. Model Scoring.vtt | 1.98 KB | ||
| 3. Demo - Predicting Using Trained TensorFlow.js Model.mp4 | 12.31 MB | ||
| 3. Demo - Predicting Using Trained TensorFlow.js Model.vtt | 4.99 KB | ||
| 4. Materialize UI.mp4 | 1.67 MB | ||
| 4. Materialize UI.vtt | 1.34 KB | ||
| 5. Demo - Setting up Materialize UI.mp4 | 12.93 MB | ||
| 5. Demo - Setting up Materialize UI.vtt | 5.49 KB | ||
| 6. Demo - Integrate Steps with UI.mp4 | 26.6 MB | ||
| 6. Demo - Integrate Steps with UI.vtt | 13.34 KB | ||
| 7. TensorFlow.js Converter.mp4 | 2.46 MB | ||
| 7. TensorFlow.js Converter.vtt | 1.81 KB | ||
| 8. Demo - Predicting Using Python Exported Model.mp4 | 29.39 MB | ||
| 8. Demo - Predicting Using Python Exported Model.vtt | 12.77 KB | ||
| 9. Summary.mp4 | 2.54 MB | ||
| 9. Summary.vtt | 2.13 KB | ||
| 10. Using Pre-trained Models with TensorFlow.js | |||
| 1. Introduction.mp4 | 1.92 MB | ||
| 1. Introduction.vtt | 1.82 KB | ||
| 2. Transfer Learning with TensorFlow.js.mp4 | 6.93 MB | ||
| 2. Transfer Learning with TensorFlow.js.vtt | 5.04 KB | ||
| 3. Demo - Creating Features from Universal Sentence Encoder (USE) Model.mp4 | 16.48 MB | ||
| 3. Demo - Creating Features from Universal Sentence Encoder (USE) Model.vtt | 6.91 KB | ||
| 4. Demo - Performing Transfer Learning on USE Encoded Features.mp4 | 14.59 MB | ||
| 4. Demo - Performing Transfer Learning on USE Encoded Features.vtt | 5.87 KB | ||
| 5. Toxicity Detection Model.mp4 | 5.87 MB | ||
| 5. Toxicity Detection Model.vtt | 3.63 KB | ||
| 6. Demo - Using TensorFlow.js Toxicity Detection Model.mp4 | 12.17 MB | ||
| 6. Demo - Using TensorFlow.js Toxicity Detection Model.vtt | 6.59 KB | ||
| 7. Summary.mp4 | 1.65 MB | ||
| 7. Summary.vtt | 1.46 KB | ||
| 11. Whats Next | |||
| 1. Taking Your Journey Forward.mp4 | 20.56 MB | ||
| 1. Taking Your Journey Forward.vtt | 6.12 KB | ||
| exercise.7z | 55.16 MB | ||
| playlist.m3u | 6.12 KB | ||
| ~i.txt | 2.13 KB | ||
| C1. Build a Machine Learning Workflow with Keras TensorFlow 2.0 (Janani Ravi, 2020) | |||
| 1. Course Overview | |||
| 1. Course Overview.mp4 | 3.98 MB | ||
| 1. Course Overview.vtt | 3.2 KB | ||
| 2. Understanding Keras Models and Layers | |||
| 1. Version Check.mp4 | 532.4 KB | ||
| 1. Version Check.vtt | 7 B | ||
| 2. Prerequisites and Course Outline.mp4 | 4.3 MB | ||
| 2. Prerequisites and Course Outline.vtt | 3.92 KB | ||
| 3. Introducing Keras.mp4 | 5.33 MB | ||
| 3. Introducing Keras.vtt | 4.1 KB | ||
| 4. Supervised Learning.mp4 | 7.86 MB | ||
| 4. Supervised Learning.vtt | 6.81 KB | ||
| 5. Unsupervised Learning.mp4 | 10.15 MB | ||
| 5. Unsupervised Learning.vtt | 7.73 KB | ||
| 6. Sequential Models.mp4 | 11.98 MB | ||
| 6. Sequential Models.vtt | 9.41 KB | ||
| 7. The Functional API.mp4 | 4.86 MB | ||
| 7. The Functional API.vtt | 3.97 KB | ||
| 8. Saving and Loading Models.mp4 | 8.32 MB | ||
| 8. Saving and Loading Models.vtt | 7.53 KB | ||
| 9. Demo - Install and Set up Tensor Flow.mp4 | 19.52 MB | ||
| 9. Demo - Install and Set up Tensor Flow.vtt | 9.28 KB | ||
| 3. Building Regression and Classification Models | |||
| 1. Demo - Exploring and Processing the Insurance Dataset.mp4 | 18.92 MB | ||
| 1. Demo - Exploring and Processing the Insurance Dataset.vtt | 12.92 KB | ||
| 2. Demo - Training a Simple Sequential Model.mp4 | 19.88 MB | ||
| 2. Demo - Training a Simple Sequential Model.vtt | 11.88 KB | ||
| 3. Demo - Configuring Training Behavior Using Callbacks.mp4 | 15.19 MB | ||
| 3. Demo - Configuring Training Behavior Using Callbacks.vtt | 8.69 KB | ||
| 4. Demo - Saving Model Architecture and Weights.mp4 | 11.94 MB | ||
| 4. Demo - Saving Model Architecture and Weights.vtt | 6.14 KB | ||
| 5. Demo - Loading Saved Models.mp4 | 9.16 MB | ||
| 5. Demo - Loading Saved Models.vtt | 5.57 KB | ||
| 6. Demo - Exploring and Processing the Spine Dataset.mp4 | 12.16 MB | ||
| 6. Demo - Exploring and Processing the Spine Dataset.vtt | 6.72 KB | ||
| 7. Demo - Build and Train Model Using the Functional API.mp4 | 19.14 MB | ||
| 7. Demo - Build and Train Model Using the Functional API.vtt | 10.2 KB | ||
| 8. Demo - Checkpointing Models Using Callbacks.mp4 | 6.97 MB | ||
| 8. Demo - Checkpointing Models Using Callbacks.vtt | 3.98 KB | ||
| 9. Demo - Monitoring Models Using TensorBoard.mp4 | 14.16 MB | ||
| 9. Demo - Monitoring Models Using TensorBoard.vtt | 8.39 KB | ||
| 4. Building Image Classification Models | |||
| 01. Drawbacks of Dense Neural Networks.mp4 | 5.59 MB | ||
| 01. Drawbacks of Dense Neural Networks.vtt | 3.86 KB | ||
| 02. Introducing Convolutional Neural Networks.mp4 | 8.15 MB | ||
| 02. Introducing Convolutional Neural Networks.vtt | 5.48 KB | ||
| 03. Convolution.mp4 | 7.4 MB | ||
| 03. Convolution.vtt | 5.37 KB | ||
| 04. Convolutional Layers.mp4 | 11.38 MB | ||
| 04. Convolutional Layers.vtt | 7.88 KB | ||
| 05. Pooling Layers.mp4 | 7.8 MB | ||
| 05. Pooling Layers.vtt | 5.12 KB | ||
| 06. CNN Architecture.mp4 | 6.3 MB | ||
| 06. CNN Architecture.vtt | 4.02 KB | ||
| 07. Demo - Loading and Preprocessing the Cifar10 Dataset.mp4 | 16.61 MB | ||
| 07. Demo - Loading and Preprocessing the Cifar10 Dataset.vtt | 10.45 KB | ||
| 08. Demo - Designing the Convolutional Neural Network.mp4 | 8.72 MB | ||
| 08. Demo - Designing the Convolutional Neural Network.vtt | 4.67 KB | ||
| 09. Demo - Training and Prediction Using a CNN.mp4 | 9.43 MB | ||
| 09. Demo - Training and Prediction Using a CNN.vtt | 5.21 KB | ||
| 10. Demo - Using Image Transformations and Dropout.mp4 | 13.95 MB | ||
| 10. Demo - Using Image Transformations and Dropout.vtt | 7.46 KB | ||
| 5. Building Unsupervised Machine Learning Models | |||
| 1. Supervised vs. Unsupervised Learning.mp4 | 2.49 MB | ||
| 1. Supervised vs. Unsupervised Learning.vtt | 2.07 KB | ||
| 2. Autoencoders as Unsupervised Machine Learning.mp4 | 6.79 MB | ||
| 2. Autoencoders as Unsupervised Machine Learning.vtt | 5.64 KB | ||
| 3. Dimensionality Reduction Using Autoencoders.mp4 | 10.79 MB | ||
| 3. Dimensionality Reduction Using Autoencoders.vtt | 7.3 KB | ||
| 4. Demo - Preprocessing Images.mp4 | 8.54 MB | ||
| 4. Demo - Preprocessing Images.vtt | 5.52 KB | ||
| 5. Demo - Reconstructing Images Using a Stacked Autoencoder.mp4 | 10.53 MB | ||
| 5. Demo - Reconstructing Images Using a Stacked Autoencoder.vtt | 5.94 KB | ||
| 6. Demo - Reconstructing Images Using a CNN Based Autoencoder.mp4 | 11.37 MB | ||
| 6. Demo - Reconstructing Images Using a CNN Based Autoencoder.vtt | 6.28 KB | ||
| 6. Implementing Custom Layers and Models | |||
| 01. Customizing Layers and Models.mp4 | 2.12 MB | ||
| 01. Customizing Layers and Models.vtt | 2.06 KB | ||
| 02. Model Subclassing and Custom Layers.mp4 | 8.26 MB | ||
| 02. Model Subclassing and Custom Layers.vtt | 6.77 KB | ||
| 03. Demo - Creating a Custom Layer.mp4 | 15.47 MB | ||
| 03. Demo - Creating a Custom Layer.vtt | 9.96 KB | ||
| 04. Demo - Deferring Weight Creation in a Layer.mp4 | 5.72 MB | ||
| 04. Demo - Deferring Weight Creation in a Layer.vtt | 3 KB | ||
| 05. Demo - Accumulating Losses with Custom Layers.mp4 | 15.56 MB | ||
| 05. Demo - Accumulating Losses with Custom Layers.vtt | 7.28 KB | ||
| 06. Demo - Serializing Layers and the Training Parameter.mp4 | 7.48 MB | ||
| 06. Demo - Serializing Layers and the Training Parameter.vtt | 4.32 KB | ||
| 07. Demo - Building Custom Models.mp4 | 9.95 MB | ||
| 07. Demo - Building Custom Models.vtt | 4.55 KB | ||
| 08. Demo - Building and Training a Regression Model Using Custom Layers.mp4 | 13.04 MB | ||
| 08. Demo - Building and Training a Regression Model Using Custom Layers.vtt | 6.71 KB | ||
| 09. Demo - Building and Training a Custom Model with Custom Layers.mp4 | 11.99 MB | ||
| 09. Demo - Building and Training a Custom Model with Custom Layers.vtt | 5.76 KB | ||
| 10. Summary and Further Study.mp4 | 2.9 MB | ||
| 10. Summary and Further Study.vtt | 2.84 KB | ||
| exercise.7z | 89.1 MB | ||
| playlist.m3u | 3.84 KB | ||
| ~i.txt | 2.25 KB | ||
| C2. Implement Time Series Analysis, Forecasting and Prediction with TensorFlow 2.0 (Chase DeHan, 2020) | |||
| 1. Course Overview | |||
| 1. Course Overview.mp4 | 2.96 MB | ||
| 1. Course Overview.vtt | 2.11 KB | ||
| 2. Understanding Time Series Data | |||
| 1. Introduction.mp4 | 2.66 MB | ||
| 1. Introduction.vtt | 2.68 KB | ||
| 2. What Is a Time Series.mp4 | 4.02 MB | ||
| 2. What Is a Time Series.vtt | 4.4 KB | ||
| 3. Evaluation Metrics.mp4 | 6.08 MB | ||
| 3. Evaluation Metrics.vtt | 4.87 KB | ||
| 4. Using a Hold Out.mp4 | 3.64 MB | ||
| 4. Using a Hold Out.vtt | 3.49 KB | ||
| 5. Load Data.mp4 | 4.5 MB | ||
| 5. Load Data.vtt | 2.8 KB | ||
| 6. Basic Time Series Windows.mp4 | 6.44 MB | ||
| 6. Basic Time Series Windows.vtt | 4.97 KB | ||
| 3. Building a Baseline Model | |||
| 1. Introduction.mp4 | 2.37 MB | ||
| 1. Introduction.vtt | 2.03 KB | ||
| 2. Data Preparation.mp4 | 4.32 MB | ||
| 2. Data Preparation.vtt | 2.85 KB | ||
| 3. Split Data.mp4 | 6.3 MB | ||
| 3. Split Data.vtt | 3.38 KB | ||
| 4. WindowGenerator Class.mp4 | 8.88 MB | ||
| 4. WindowGenerator Class.vtt | 5.84 KB | ||
| 5. Additional Methods in WindowGenerator.mp4 | 3.09 MB | ||
| 5. Additional Methods in WindowGenerator.vtt | 2.17 KB | ||
| 6. More Methods.mp4 | 4.1 MB | ||
| 6. More Methods.vtt | 2.68 KB | ||
| 7. Single Step Window.mp4 | 4.35 MB | ||
| 7. Single Step Window.vtt | 2.69 KB | ||
| 8. Baseline Model Class.mp4 | 7.91 MB | ||
| 8. Baseline Model Class.vtt | 4.28 KB | ||
| 9. Linear Model.mp4 | 8.31 MB | ||
| 9. Linear Model.vtt | 4.79 KB | ||
| 4. Utilizing Neural Networks | |||
| 1. Introduction.mp4 | 2.2 MB | ||
| 1. Introduction.vtt | 2.23 KB | ||
| 2. Compile and Fit.mp4 | 7.52 MB | ||
| 2. Compile and Fit.vtt | 4.63 KB | ||
| 3. Dense Model.mp4 | 6.34 MB | ||
| 3. Dense Model.vtt | 3.16 KB | ||
| 4. Convolutional Model.mp4 | 8.44 MB | ||
| 4. Convolutional Model.vtt | 4.72 KB | ||
| 5. Recurrent Neural Networks.mp4 | 10.78 MB | ||
| 5. Recurrent Neural Networks.vtt | 6.21 KB | ||
| 5. Expanding the Modeling Approach | |||
| 1. Introduction.mp4 | 1.92 MB | ||
| 1. Introduction.vtt | 1.61 KB | ||
| 2. Predict Multiple Outputs.mp4 | 6.57 MB | ||
| 2. Predict Multiple Outputs.vtt | 4.16 KB | ||
| 3. RNN on Multiple Outputs.mp4 | 3.25 MB | ||
| 3. RNN on Multiple Outputs.vtt | 2.11 KB | ||
| 4. Predict Multiple Periods.mp4 | 6.47 MB | ||
| 4. Predict Multiple Periods.vtt | 4.13 KB | ||
| 5. Linear Model and Multiple Periods.mp4 | 4.9 MB | ||
| 5. Linear Model and Multiple Periods.vtt | 2.88 KB | ||
| 6. Dense Model and Multiple Periods.mp4 | 2.36 MB | ||
| 6. Dense Model and Multiple Periods.vtt | 1.39 KB | ||
| 7. CNNs and Multiple Outputs.mp4 | 5.86 MB | ||
| 7. CNNs and Multiple Outputs.vtt | 3.01 KB | ||
| 8. LSTM and Multiple Outputs.mp4 | 6.31 MB | ||
| 8. LSTM and Multiple Outputs.vtt | 3.81 KB | ||
| exercise.7z | 84.37 MB | ||
| playlist.m3u | 1.65 KB | ||
| ~i.txt | 1.65 KB | ||
| scr.png | 147.59 KB | ||
| ~i.txt | 874 B |
Description
Обучающие видео » Компьютерные видеоуроки и обучающие интерактивные DVD » Программирование (видеоуроки)
Building Machine Learning Solutions with TensorFlow | Path
Год выпуска: 2020
Производитель: Pluralsight
Сайт производителя: //app.pluralsight.com/paths/skills/tensorflow
//app.pluralsight.com/paths/skills/building-machine-learning-solutions-with-tensorflow-20 Автор: Коллектив авторов
Продолжительность: 40h
Тип раздаваемого материала: Видеоурок
Язык: Английский
Описание:
TensorFlow is an open-source machine learning software library developed Google. Since it was released in 2015, it has become one of the most widely-used machine learning libraries. This skill will teach you how to implement the machine learning workflow using TensorFlow, and apply the library from Python to solve simple and complex machine learning problems.
Google released TensorFlow 2.0 in October 2019 which uses the dynamic graph and is more Python friendly. There are multiple changes to ensure removal of redundant APIs and better integration with Python runtime and Eager Execution.
What You Will Learn: Design and implementation of machine learning solutions using TensorFlow 2.0
Applying Tensorflow to common analytical problems, such as classification, clustering, and regression
Debugging TensorFlow projects
Deploying TensorFlow projects to the cloud
Designing optimal Data pipelines
Applying TensorFlow to more advanced problems spaces, such as image recognition, language modeling, and predictive analytics
Prerequisites: Python Programming
Machine Learning Literacy
Related Topics: Statistics
Feature Engineering
Deep Learning
PyTorch
[spoiler="Содержание"]
Building Machine Learning Solutions with TensorFlow (2019) A1. TensorFlow: Getting Started (Jerry Kurata, 2017)
A2. Understanding the Foundations of TensorFlow (Janani Ravi, 2017)
A3. Building Regression Models Using TensorFlow (Vitthal Srinivasan, 2017)
A4. Building Classification Models with TensorFlow (Janani Ravi, 2017)
A5. Building Unsupervised Learning Models with TensorFlow (Janani Ravi, 2017)
B1. Debugging and Monitoring TensorFlow Programs (Janani Ravi, 2018)
B2. Deploying TensorFlow Models to AWS, Azure, and the GCP (Janani Ravi, 2018)
C1. Language Modeling with Recurrent Neural Networks in TensorFlow (Janani Ravi, 2018)
C2. Implementing Image Recognition Systems with TensorFlow (Jon Flanders, 2019)
C3. Implementing Predictive Analytics with TensorFlow (Justin Flett, 2018)
C4. Sentiment Analysis with Recurrent Neural Networks in TensorFlow (Janani Ravi, 2017)
Building Machine Learning Solutions with TensorFlow 2.0 (2020) A1. Getting Started with TensorFlow 2.0 (Janani Ravi, 2020)
A2. Installation Guide for TensorFlow 2.0 (Omotayo Aina, 2020) | Guide
B1. Designing Data Pipelines with TensorFlow 2.0 (Chase DeHan, 2020)
B2. Implement Hyperparameter Tuning for TensorFlow 2.0 (Gaurav Singhal, 2020) | Guide
B3. Building Machine Learning Solutions with TensorFlow.js (Abhishek Kumar, 2020)
C1. Build a Machine Learning Workflow with Keras TensorFlow 2.0 (Janani Ravi, 2020)
C2. Implement Time Series Analysis, Forecasting and Prediction with TensorFlow 2.0 (Chase DeHan, 2020)
[/spoiler]
Файлы примеров: присутствуют
Формат видео: MP4
Видео: H.264/AVC, 1280x720, 16:9, 30fps, 125 kb/s
Аудио: AAC, 44.1 kHz, 96 kbit/s, 2.0 chn
[spoiler="Скриншоты"]
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[/spoiler]
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