| 1. Artificial Neural Networks Section Introduction.mp4 | 32.5 MB | ||
| 1. Artificial Neural Networks Section Introduction.srt | 7.9 KB | ||
| 1. Deep Reinforcement Learning Section Introduction.mp4 | 37.8 MB | ||
| 1. Deep Reinforcement Learning Section Introduction.srt | 8.6 KB | ||
| 1. Differences Between Tensorflow 1.x and Tensorflow 2.x.mp4 | 42.5 MB | ||
| 1. Differences Between Tensorflow 1.x and Tensorflow 2.x.srt | 12.2 KB | ||
| 1. Embeddings.mp4 | 58 MB | ||
| 1. Embeddings.srt | 16.2 KB | ||
| 1. GAN Theory.mp4 | 86.5 MB | ||
| 1. GAN Theory.srt | 20.7 KB | ||
| 1. Gradient Descent.mp4 | 34.9 MB | ||
| 1. Gradient Descent.srt | 9.8 KB | ||
| 1. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4 | 166.7 MB | ||
| 1. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.srt | 14.7 KB | ||
| 1. Intro to Google Colab, how to use a GPU or TPU for free.mp4 | 65.2 MB | ||
| 1. Intro to Google Colab, how to use a GPU or TPU for free.srt | 14.1 KB | ||
| 1. Introduction.mp4 | 39.2 MB | ||
| 1. Introduction.srt | 5.7 KB | ||
| 1. Links to TF2.0 Notebooks.html | 7.8 KB | ||
| 1. Mean Squared Error.mp4 | 37.3 MB | ||
| 1. Mean Squared Error.srt | 11.2 KB | ||
| 1. Recommender Systems with Deep Learning Theory.mp4 | 68.7 MB | ||
| 1. Recommender Systems with Deep Learning Theory.srt | 17.4 KB | ||
| 1. Reinforcement Learning Stock Trader Introduction.mp4 | 29.7 MB | ||
| 1. Reinforcement Learning Stock Trader Introduction.srt | 6.8 KB | ||
| 1. Sequence Data.mp4 | 103.2 MB | ||
| 1. Sequence Data.srt | 24 KB | ||
| 1. Transfer Learning Theory.mp4 | 55.2 MB | ||
| 1. Transfer Learning Theory.srt | 10.7 KB | ||
| 1. What is Convolution (part 1).mp4 | 83.6 MB | ||
| 1. What is Convolution (part 1).srt | 20.1 KB | ||
| 1. What is Machine Learning.mp4 | 73.2 MB | ||
| 1. What is Machine Learning.srt | 18.4 KB | ||
| 1. What is a Web Service (Tensorflow Serving pt 1).mp4 | 31.6 MB | ||
| 1. What is a Web Service (Tensorflow Serving pt 1).srt | 7.7 KB | ||
| 1. What is the Appendix.mp4 | 18 MB | ||
| 1. What is the Appendix.srt | 3.7 KB | ||
| 10. BONUS Where to get discount coupons and FREE deep learning material.mp4 | 37.8 MB | ||
| 10. BONUS Where to get discount coupons and FREE deep learning material.srt | 7.9 KB | ||
| 10. Batch Normalization.mp4 | 23.5 MB | ||
| 10. Batch Normalization.srt | 6.5 KB | ||
| 10. Epsilon-Greedy.mp4 | 37.6 MB | ||
| 10. Epsilon-Greedy.srt | 7.4 KB | ||
| 10. GRU and LSTM (pt 2).mp4 | 53.6 MB | ||
| 10. GRU and LSTM (pt 2).srt | 14.4 KB | ||
| 11. A More Challenging Sequence.mp4 | 77.7 MB | ||
| 11. A More Challenging Sequence.srt | 9.6 KB | ||
| 11. Improving CIFAR-10 Results.mp4 | 86.4 MB | ||
| 11. Improving CIFAR-10 Results.srt | 13.2 KB | ||
| 11. Q-Learning.mp4 | 61.3 MB | ||
| 11. Q-Learning.srt | 17.9 KB | ||
| 12. Deep Q-Learning DQN (pt 1).mp4 | 55.7 MB | ||
| 12. Deep Q-Learning DQN (pt 1).srt | 16.4 KB | ||
| 12. Demo of the Long Distance Problem.mp4 | 143.1 MB | ||
| 12. Demo of the Long Distance Problem.srt | 23.1 KB | ||
| 13. Deep Q-Learning DQN (pt 2).mp4 | 49.2 MB | ||
| 13. Deep Q-Learning DQN (pt 2).srt | 13.2 KB | ||
| 13. RNN for Image Classification (Theory).mp4 | 31.5 MB | ||
| 13. RNN for Image Classification (Theory).srt | 6 KB | ||
| 14. How to Learn Reinforcement Learning.mp4 | 37.5 MB | ||
| 14. How to Learn Reinforcement Learning.srt | 7.6 KB | ||
| 14. RNN for Image Classification (Code).mp4 | 27.4 MB | ||
| 14. RNN for Image Classification (Code).srt | 4.2 KB | ||
| 15. Stock Return Predictions using LSTMs (pt 1).mp4 | 80 MB | ||
| 15. Stock Return Predictions using LSTMs (pt 1).srt | 15.7 KB | ||
| 16. Stock Return Predictions using LSTMs (pt 2).mp4 | 38.2 MB | ||
| 16. Stock Return Predictions using LSTMs (pt 2).srt | 6.5 KB | ||
| 17. Stock Return Predictions using LSTMs (pt 3).mp4 | 76.7 MB | ||
| 17. Stock Return Predictions using LSTMs (pt 3).srt | 14.4 KB | ||
| 2. Binary Cross Entropy.mp4 | 21.5 MB | ||
| 2. Binary Cross Entropy.srt | 7.3 KB | ||
| 2. Code Preparation (Classification Theory).mp4 | 68.5 MB | ||
| 2. Code Preparation (Classification Theory).srt | 20.3 KB | ||
| 2. Code Preparation (NLP).mp4 | 62.9 MB | ||
| 2. Code Preparation (NLP).srt | 16.8 KB | ||
| 2. Constants and Basic Computation.mp4 | 50.2 MB | ||
| 2. Constants and Basic Computation.srt | 9.6 KB | ||
| 2. Data and Environment.mp4 | 56 MB | ||
| 2. Data and Environment.srt | 15.7 KB | ||
| 2. Elements of a Reinforcement Learning Problem.mp4 | 97.8 MB | ||
| 2. Elements of a Reinforcement Learning Problem.srt | 26.2 KB | ||
| 2. Forecasting.mp4 | 47.2 MB | ||
| 2. Forecasting.srt | 12.7 KB | ||
| 2. Forward Propagation.mp4 | 49.3 MB | ||
| 2. Forward Propagation.srt | 12.2 KB | ||
| 2. GAN Code.mp4 | 78.2 MB | ||
| 2. GAN Code.srt | 14.9 KB | ||
| 2. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4 | 117.1 MB | ||
| 2. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.srt | 31.6 KB | ||
| 2. Outline.mp4 | 73.7 MB | ||
| 2. Outline.srt | 17.1 KB | ||
| 2. Recommender Systems with Deep Learning Code.mp4 | 58.8 MB | ||
| 2. Recommender Systems with Deep Learning Code.srt | 11.7 KB | ||
| 2. Some Pre-trained Models (VGG, ResNet, Inception, MobileNet).mp4 | 31.5 MB | ||
| 2. Some Pre-trained Models (VGG, ResNet, Inception, MobileNet).srt | 7.3 KB | ||
| 2. Stochastic Gradient Descent.mp4 | 25 MB | ||
| 2. Stochastic Gradient Descent.srt | 5.4 KB | ||
| 2. Tensorflow 2.0 in Google Colab.mp4 | 51.1 MB | ||
| 2. Tensorflow 2.0 in Google Colab.srt | 9.5 KB | ||
| 2. Tensorflow Serving pt 2.mp4 | 124.5 MB | ||
| 2. Tensorflow Serving pt 2.srt | 20.4 KB | ||
| 2. What is Convolution (part 2).mp4 | 25.2 MB | ||
| 2. What is Convolution (part 2).srt | 7.2 KB | ||
| 2. Windows-Focused Environment Setup 2018.mp4 | 194 MB | ||
| 2. Windows-Focused Environment Setup 2018.srt | 20 KB | ||
| 3. Autoregressive Linear Model for Time Series Prediction.mp4 | 87.7 MB | ||
| 3. Autoregressive Linear Model for Time Series Prediction.srt | 14.2 KB | ||
| 3. Categorical Cross Entropy.mp4 | 35.4 MB | ||
| 3. Categorical Cross Entropy.srt | 9.6 KB | ||
| 3. Classification Notebook.mp4 | 66.3 MB | ||
| 3. Classification Notebook.srt | 9.4 KB | ||
| 3. How to Code Yourself (part 1).mp4 | 82.1 MB | ||
| 3. How to Code Yourself (part 1).srt | 22.1 KB | ||
| 3. Installing NVIDIA GPU-Accelerated Deep Learning Libraries on your Home Computer.mp4 | 167.3 MB | ||
| 3. Installing NVIDIA GPU-Accelerated Deep Learning Libraries on your Home Computer.srt | 32 KB | ||
| 3. Large Datasets and Data Generators.mp4 | 36.6 MB | ||
| 3. Large Datasets and Data Generators.srt | 8.8 KB | ||
| 3. Momentum.mp4 | 39.4 MB | ||
| 3. Momentum.srt | 7.8 KB | ||
| 3. Replay Buffer.mp4 | 24.1 MB | ||
| 3. Replay Buffer.srt | 6.9 KB | ||
| 3. States, Actions, Rewards, Policies.mp4 | 43 MB | ||
| 3. States, Actions, Rewards, Policies.srt | 11.3 KB | ||
| 3. Tensorflow Lite (TFLite).mp4 | 42.4 MB | ||
| 3. Tensorflow Lite (TFLite).srt | 11 KB | ||
| 3. Text Preprocessing.mp4 | 36.1 MB | ||
| 3. Text Preprocessing.srt | 6.2 KB | ||
| 3. The Geometrical Picture.mp4 | 56.5 MB | ||
| 3. The Geometrical Picture.srt | 11.5 KB | ||
| 3. Uploading your own data to Google Colab.mp4 | 89.1 MB | ||
| 3. Uploading your own data to Google Colab.srt | 12 KB | ||
| 3. Variables and Gradient Tape.mp4 | 70.6 MB | ||
| 3. Variables and Gradient Tape.srt | 13.6 KB | ||
| 3. What is Convolution (part 3).mp4 | 27.6 MB | ||
| 3. What is Convolution (part 3).srt | 8 KB | ||
| 3. Where to get the code.mp4 | 30.5 MB | ||
| 3. Where to get the code.srt | 7.6 KB | ||
| 4. 2 Approaches to Transfer Learning.mp4 | 20.6 MB | ||
| 4. 2 Approaches to Transfer Learning.srt | 6 KB | ||
| 4. Activation Functions.mp4 | 92.2 MB | ||
| 4. Activation Functions.srt | 22.6 KB | ||
| 4. Build Your Own Custom Model.mp4 | 70.2 MB | ||
| 4. Build Your Own Custom Model.srt | 13.3 KB | ||
| 4. Code Preparation (Regression Theory).mp4 | 31.3 MB | ||
| 4. Code Preparation (Regression Theory).srt | 9.1 KB | ||
| 4. Convolution on Color Images.mp4 | 77 MB | ||
| 4. Convolution on Color Images.srt | 20.6 KB | ||
| 4. How to Code Yourself (part 2).mp4 | 56.4 MB | ||
| 4. How to Code Yourself (part 2).srt | 13 KB | ||
| 4. Markov Decision Processes (MDPs).mp4 | 49 MB | ||
| 4. Markov Decision Processes (MDPs).srt | 12.7 KB | ||
| 4. Program Design and Layout.mp4 | 29.8 MB | ||
| 4. Program Design and Layout.srt | 8.6 KB | ||
| 4. Proof that the Linear Model Works.mp4 | 18.3 MB | ||
| 4. Proof that the Linear Model Works.srt | 4.6 KB | ||
| 4. Text Classification with LSTMs.mp4 | 60.6 MB | ||
| 4. Text Classification with LSTMs.srt | 9.8 KB | ||
| 4. Variable and Adaptive Learning Rates.mp4 | 38.5 MB | ||
| 4. Variable and Adaptive Learning Rates.srt | 15.2 KB | ||
| 4. Where can I learn about Numpy, Scipy, Matplotlib, Pandas, and Scikit-Learn.mp4 | 43.8 MB | ||
| 4. Where can I learn about Numpy, Scipy, Matplotlib, Pandas, and Scikit-Learn.srt | 11.5 KB | ||
| 4. Why is Google the King of Distributed Computing.mp4 | 50.8 MB | ||
| 4. Why is Google the King of Distributed Computing.srt | 11.3 KB | ||
| 5. Adam.mp4 | 42.6 MB | ||
| 5. Adam.srt | 13.5 KB | ||
| 5. CNN Architecture.mp4 | 90.9 MB | ||
| 5. CNN Architecture.srt | 27.9 KB | ||
| 5. CNNs for Text.mp4 | 40.9 MB | ||
| 5. CNNs for Text.srt | 9.6 KB | ||
| 5. Code pt 1.mp4 | 46.8 MB | ||
| 5. Code pt 1.srt | 7.2 KB | ||
| 5. Multiclass Classification.mp4 | 46.9 MB | ||
| 5. Multiclass Classification.srt | 11 KB | ||
| 5. Proof that using Jupyter Notebook is the same as not using it.mp4 | 77.9 MB | ||
| 5. Proof that using Jupyter Notebook is the same as not using it.srt | 14.2 KB | ||
| 5. Recurrent Neural Networks.mp4 | 92 MB | ||
| 5. Recurrent Neural Networks.srt | 25.6 KB | ||
| 5. Regression Notebook.mp4 | 71.7 MB | ||
| 5. Regression Notebook.srt | 12.1 KB | ||
| 5. The Return.mp4 | 20.9 MB | ||
| 5. The Return.srt | 6.3 KB | ||
| 5. Training with Distributed Strategies.mp4 | 50.1 MB | ||
| 5. Training with Distributed Strategies.srt | 8.5 KB | ||
| 5. Transfer Learning Code (pt 1).mp4 | 66.6 MB | ||
| 5. Transfer Learning Code (pt 1).srt | 13.8 KB | ||
| 6. CNN Code Preparation.mp4 | 86.3 MB | ||
| 6. CNN Code Preparation.srt | 19.6 KB | ||
| 6. Code pt 2.mp4 | 83.4 MB | ||
| 6. Code pt 2.srt | 11.8 KB | ||
| 6. How to Represent Images.mp4 | 80.9 MB | ||
| 6. How to Represent Images.srt | 15.6 KB | ||
| 6. How to Succeed in this Course (Long Version).mp4 | 38.9 MB | ||
| 6. How to Succeed in this Course (Long Version).srt | 14.6 KB | ||
| 6. RNN Code Preparation.mp4 | 20.4 MB | ||
| 6. RNN Code Preparation.srt | 7.1 KB | ||
| 6. Text Classification with CNNs.mp4 | 46.4 MB | ||
| 6. Text Classification with CNNs.srt | 6.6 KB | ||
| 6. The Neuron.mp4 | 49.4 MB | ||
| 6. The Neuron.srt | 12.5 KB | ||
| 6. Transfer Learning Code (pt 2).mp4 | 46.1 MB | ||
| 6. Transfer Learning Code (pt 2).srt | 10.4 KB | ||
| 6. Using the TPU.html | 1.8 KB | ||
| 6. Value Functions and the Bellman Equation.mp4 | 43.3 MB | ||
| 6. Value Functions and the Bellman Equation.srt | 12.5 KB | ||
| 7. CNN for Fashion MNIST.mp4 | 51.6 MB | ||
| 7. CNN for Fashion MNIST.srt | 8 KB | ||
| 7. Code Preparation (ANN).mp4 | 56.2 MB | ||
| 7. Code Preparation (ANN).srt | 16.3 KB | ||
| 7. Code pt 3.mp4 | 62.3 MB | ||
| 7. Code pt 3.srt | 7.8 KB | ||
| 7. How does a model learn.mp4 | 55 MB | ||
| 7. How does a model learn.srt | 14 KB | ||
| 7. Is Theano Dead.mp4 | 44.4 MB | ||
| 7. Is Theano Dead.srt | 12.6 KB | ||
| 7. RNN for Time Series Prediction.mp4 | 87.2 MB | ||
| 7. RNN for Time Series Prediction.srt | 11.2 KB | ||
| 7. What does it mean to “learn”.mp4 | 30.3 MB | ||
| 7. What does it mean to “learn”.srt | 8.9 KB | ||
| 8. ANN for Image Classification.mp4 | 58.4 MB | ||
| 8. ANN for Image Classification.srt | 9.9 KB | ||
| 8. CNN for CIFAR-10.mp4 | 34.8 MB | ||
| 8. CNN for CIFAR-10.srt | 5.4 KB | ||
| 8. Code pt 4.mp4 | 59.2 MB | ||
| 8. Code pt 4.srt | 8.2 KB | ||
| 8. Making Predictions.mp4 | 42 MB | ||
| 8. Making Predictions.srt | 8 KB | ||
| 8. Paying Attention to Shapes.mp4 | 64.3 MB | ||
| 8. Paying Attention to Shapes.srt | 9.9 KB | ||
| 8. Solving the Bellman Equation with Reinforcement Learning (pt 1).mp4 | 39 MB | ||
| 8. Solving the Bellman Equation with Reinforcement Learning (pt 1).srt | 12.7 KB | ||
| 8. What order should I take your courses in (part 1).mp4 | 88.1 MB | ||
| 8. What order should I take your courses in (part 1).srt | 16.1 KB | ||
| 9. ANN for Regression.mp4 | 84 MB | ||
| 9. ANN for Regression.srt | 12.8 KB | ||
| 9. Data Augmentation.mp4 | 39.2 MB | ||
| 9. Data Augmentation.srt | 11.2 KB | ||
| 9. GRU and LSTM (pt 1).mp4 | 76.1 MB | ||
| 9. GRU and LSTM (pt 1).srt | 21.1 KB | ||
| 9. Reinforcement Learning Stock Trader Discussion.mp4 | 18.2 MB | ||
| 9. Reinforcement Learning Stock Trader Discussion.srt | 4.4 KB | ||
| 9. Saving and Loading a Model.mp4 | 35.3 MB | ||
| 9. Saving and Loading a Model.srt | 4.9 KB | ||
| 9. Solving the Bellman Equation with Reinforcement Learning (pt 2).mp4 | 52.5 MB | ||
| 9. Solving the Bellman Equation with Reinforcement Learning (pt 2).srt | 14.9 KB | ||
| 9. What order should I take your courses in (part 2).mp4 | 122.6 MB | ||
| 9. What order should I take your courses in (part 2).srt | 23 KB | ||
| [FreeCourseWorld.Com].url | 102.4 B | ||
| ▲ 247 total files | |||
Tensorflow 2.0: Deep Learning and Artificial Intelligence
Tensorflow is the world’s most popular library for deep learning, and it’s built by Google, whose parent Alphabet recently became the most cash-rich company in the world (just a few days before I wrote this). It is the library of choice for many companies doing AI and machine learning.
Created byLazy Programmer Inc., Lazy Programmer Team
Last updated 2/2020
English
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