| 1. Artificial Neural Networks Section Introduction.mp4 | 29.8 MB | ||
| 1. Artificial Neural Networks Section Introduction.srt | 7.9 KB | ||
| 1. Beginner's Coding Tips.mp4 | 75.7 MB | ||
| 1. Beginner's Coding Tips.srt | 19 KB | ||
| 1. Deep Reinforcement Learning Section Introduction.mp4 | 38.1 MB | ||
| 1. Deep Reinforcement Learning Section Introduction.srt | 8.6 KB | ||
| 1. Differences Between Tensorflow 1.x and Tensorflow 2.x.mp4 | 38.7 MB | ||
| 1. Differences Between Tensorflow 1.x and Tensorflow 2.x.srt | 12.2 KB | ||
| 1. Embeddings.mp4 | 52.6 MB | ||
| 1. Embeddings.srt | 16.2 KB | ||
| 1. GAN Theory.mp4 | 87.2 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 Choose Hyperparameters.mp4 | 37.9 MB | ||
| 1. How to Choose Hyperparameters.srt | 8.7 KB | ||
| 1. How to Succeed in this Course (Long Version).mp4 | 35.2 MB | ||
| 1. How to Succeed in this Course (Long Version).srt | 14.6 KB | ||
| 1. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4 | 150.6 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 | 53.8 MB | ||
| 1. Intro to Google Colab, how to use a GPU or TPU for free.srt | 14.1 KB | ||
| 1. Introduction.mp4 | 34.8 MB | ||
| 1. Introduction.srt | 5.7 KB | ||
| 1. Mean Squared Error.mp4 | 33.8 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 | 26 MB | ||
| 1. Reinforcement Learning Stock Trader Introduction.srt | 6.8 KB | ||
| 1. Sequence Data.mp4 | 90.1 MB | ||
| 1. Sequence Data.srt | 24 KB | ||
| 1. Transfer Learning Theory.mp4 | 55.1 MB | ||
| 1. Transfer Learning Theory.srt | 10.7 KB | ||
| 1. What is Convolution (part 1).mp4 | 79.8 MB | ||
| 1. What is Convolution (part 1).srt | 20.2 KB | ||
| 1. What is Machine Learning.mp4 | 65.5 MB | ||
| 1. What is Machine Learning.srt | 18.4 KB | ||
| 1. What is a Web Service (Tensorflow Serving pt 1).mp4 | 27.8 MB | ||
| 1. What is a Web Service (Tensorflow Serving pt 1).srt | 7.7 KB | ||
| 1. What is the Appendix.mp4 | 16.4 MB | ||
| 1. What is the Appendix.srt | 3.7 KB | ||
| 10. ANN for Regression.mp4 | 69.3 MB | ||
| 10. ANN for Regression.srt | 12.8 KB | ||
| 10. Batch Normalization.mp4 | 21.1 MB | ||
| 10. Batch Normalization.srt | 6.5 KB | ||
| 10. Epsilon-Greedy.mp4 | 40.1 MB | ||
| 10. Epsilon-Greedy.srt | 7.5 KB | ||
| 10. GRU and LSTM (pt 2).mp4 | 50.4 MB | ||
| 10. GRU and LSTM (pt 2).srt | 14.3 KB | ||
| 10. Help! Why is the code slower on my machine.mp4 | 42.5 MB | ||
| 10. Help! Why is the code slower on my machine.srt | 11.7 KB | ||
| 10. Why Keras.mp4 | 26.5 MB | ||
| 10. Why Keras.srt | 5.8 KB | ||
| 11. A More Challenging Sequence.mp4 | 64.6 MB | ||
| 11. A More Challenging Sequence.srt | 9.6 KB | ||
| 11. Improving CIFAR-10 Results.mp4 | 72.9 MB | ||
| 11. Improving CIFAR-10 Results.srt | 13.2 KB | ||
| 11. Q-Learning.mp4 | 61.8 MB | ||
| 11. Q-Learning.srt | 17.9 KB | ||
| 11. Suggestion Box.mp4 | 27.1 MB | ||
| 11. Suggestion Box.srt | 4.7 KB | ||
| 12. Deep Q-Learning DQN (pt 1).mp4 | 56.3 MB | ||
| 12. Deep Q-Learning DQN (pt 1).srt | 16.4 KB | ||
| 12. Demo of the Long Distance Problem.mp4 | 124 MB | ||
| 12. Demo of the Long Distance Problem.srt | 23.1 KB | ||
| 13. Deep Q-Learning DQN (pt 2).mp4 | 49.6 MB | ||
| 13. Deep Q-Learning DQN (pt 2).srt | 13.2 KB | ||
| 13. RNN for Image Classification (Theory).mp4 | 29.1 MB | ||
| 13. RNN for Image Classification (Theory).srt | 6 KB | ||
| 14. How to Learn Reinforcement Learning.mp4 | 37.7 MB | ||
| 14. How to Learn Reinforcement Learning.srt | 7.6 KB | ||
| 14. RNN for Image Classification (Code).mp4 | 23.3 MB | ||
| 14. RNN for Image Classification (Code).srt | 4.2 KB | ||
| 15. Stock Return Predictions using LSTMs (pt 1).mp4 | 67.1 MB | ||
| 15. Stock Return Predictions using LSTMs (pt 1).srt | 15.7 KB | ||
| 16. Stock Return Predictions using LSTMs (pt 2).mp4 | 33 MB | ||
| 16. Stock Return Predictions using LSTMs (pt 2).srt | 6.5 KB | ||
| 17. Stock Return Predictions using LSTMs (pt 3).mp4 | 67.3 MB | ||
| 17. Stock Return Predictions using LSTMs (pt 3).srt | 14.4 KB | ||
| 18. Other Ways to Forecast.mp4 | 28.3 MB | ||
| 18. Other Ways to Forecast.srt | 7.2 KB | ||
| 2. Anaconda Environment Setup.mp4 | 180.9 MB | ||
| 2. Anaconda Environment Setup.srt | 20 KB | ||
| 2. BONUS Lecture.mp4 | 37.8 MB | ||
| 2. BONUS Lecture.srt | 7.9 KB | ||
| 2. Beginners Rejoice The Math in This Course is Optional.mp4 | 68.5 MB | ||
| 2. Beginners Rejoice The Math in This Course is Optional.srt | 17 KB | ||
| 2. Binary Cross Entropy.mp4 | 23.7 MB | ||
| 2. Binary Cross Entropy.srt | 7.3 KB | ||
| 2. Code Preparation (Classification Theory).mp4 | 59.8 MB | ||
| 2. Code Preparation (Classification Theory).srt | 20.3 KB | ||
| 2. Code Preparation (NLP).mp4 | 57 MB | ||
| 2. Code Preparation (NLP).srt | 16.8 KB | ||
| 2. Constants and Basic Computation.mp4 | 40.3 MB | ||
| 2. Constants and Basic Computation.srt | 9.6 KB | ||
| 2. Data and Environment.mp4 | 51 MB | ||
| 2. Data and Environment.srt | 15.7 KB | ||
| 2. Elements of a Reinforcement Learning Problem.mp4 | 98.6 MB | ||
| 2. Elements of a Reinforcement Learning Problem.srt | 26.2 KB | ||
| 2. Forecasting.mp4 | 46.8 MB | ||
| 2. Forecasting.srt | 13.3 KB | ||
| 2. GAN Code.mp4 | 78.3 MB | ||
| 2. GAN Code.srt | 14.9 KB | ||
| 2. How to Code Yourself (part 1).mp4 | 71.8 MB | ||
| 2. How to Code Yourself (part 1).srt | 22.1 KB | ||
| 2. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4 | 105.6 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 | 31.6 MB | ||
| 2. Some Pre-trained Models (VGG, ResNet, Inception, MobileNet).srt | 7.3 KB | ||
| 2. Stochastic Gradient Descent.mp4 | 23 MB | ||
| 2. Stochastic Gradient Descent.srt | 5.4 KB | ||
| 2. Tensorflow 2.0 in Google Colab.mp4 | 40.7 MB | ||
| 2. Tensorflow 2.0 in Google Colab.srt | 9.5 KB | ||
| 2. Tensorflow Serving pt 2.mp4 | 105 MB | ||
| 2. Tensorflow Serving pt 2.srt | 20.4 KB | ||
| 2. What is Convolution (part 2).mp4 | 22.3 MB | ||
| 2. What is Convolution (part 2).srt | 7.2 KB | ||
| 2. Where Are The Exercises.mp4 | 26 MB | ||
| 2. Where Are The Exercises.srt | 5.4 KB | ||
| 3. Autoregressive Linear Model for Time Series Prediction.mp4 | 71.7 MB | ||
| 3. Autoregressive Linear Model for Time Series Prediction.srt | 14.2 KB | ||
| 3. Categorical Cross Entropy.mp4 | 31.7 MB | ||
| 3. Categorical Cross Entropy.srt | 9.6 KB | ||
| 3. Classification Notebook.mp4 | 54.5 MB | ||
| 3. Classification Notebook.srt | 9.4 KB | ||
| 3. Forward Propagation.mp4 | 46.7 MB | ||
| 3. Forward Propagation.srt | 12.2 KB | ||
| 3. How to Code Yourself (part 2).mp4 | 49.1 MB | ||
| 3. How to Code Yourself (part 2).srt | 13 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. Links to TF2.0 Notebooks.html | 8.1 KB | ||
| 3. Machine Learning and AI Prerequisite Roadmap (pt 1).mp4 | 79.7 MB | ||
| 3. Machine Learning and AI Prerequisite Roadmap (pt 1).srt | 16.1 KB | ||
| 3. Momentum.mp4 | 34.3 MB | ||
| 3. Momentum.srt | 7.8 KB | ||
| 3. Replay Buffer.mp4 | 24 MB | ||
| 3. Replay Buffer.srt | 6.9 KB | ||
| 3. States, Actions, Rewards, Policies.mp4 | 43.3 MB | ||
| 3. States, Actions, Rewards, Policies.srt | 11.3 KB | ||
| 3. Tensorflow Lite (TFLite).mp4 | 42.6 MB | ||
| 3. Tensorflow Lite (TFLite).srt | 11 KB | ||
| 3. Text Preprocessing.mp4 | 28.8 MB | ||
| 3. Text Preprocessing.srt | 6.2 KB | ||
| 3. Uploading your own data to Google Colab.mp4 | 73.6 MB | ||
| 3. Uploading your own data to Google Colab.srt | 12 KB | ||
| 3. Variables and Gradient Tape.mp4 | 56 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 | 62.9 MB | ||
| 3. Where to get the code.srt | 15.4 KB | ||
| 3.1 Colab Notebooks.html | 204.8 B | ||
| 3.2 Github Link.html | 102.4 B | ||
| 4. 2 Approaches to Transfer Learning.mp4 | 20.6 MB | ||
| 4. 2 Approaches to Transfer Learning.srt | 6 KB | ||
| 4. Build Your Own Custom Model.mp4 | 58.5 MB | ||
| 4. Build Your Own Custom Model.srt | 13.3 KB | ||
| 4. Code Preparation (Regression Theory).mp4 | 27.3 MB | ||
| 4. Code Preparation (Regression Theory).srt | 9.1 KB | ||
| 4. Convolution on Color Images.mp4 | 69.4 MB | ||
| 4. Convolution on Color Images.srt | 20.6 KB | ||
| 4. Machine Learning and AI Prerequisite Roadmap (pt 2).mp4 | 108.2 MB | ||
| 4. Machine Learning and AI Prerequisite Roadmap (pt 2).srt | 23 KB | ||
| 4. Markov Decision Processes (MDPs).mp4 | 49.3 MB | ||
| 4. Markov Decision Processes (MDPs).srt | 12.7 KB | ||
| 4. Program Design and Layout.mp4 | 26 MB | ||
| 4. Program Design and Layout.srt | 8.6 KB | ||
| 4. Proof that the Linear Model Works.mp4 | 16.2 MB | ||
| 4. Proof that the Linear Model Works.srt | 4.6 KB | ||
| 4. Proof that using Jupyter Notebook is the same as not using it.mp4 | 69.4 MB | ||
| 4. Proof that using Jupyter Notebook is the same as not using it.srt | 14.2 KB | ||
| 4. Text Classification with LSTMs.mp4 | 50.7 MB | ||
| 4. Text Classification with LSTMs.srt | 9.8 KB | ||
| 4. The Geometrical Picture.mp4 | 56.4 MB | ||
| 4. The Geometrical Picture.srt | 11.5 KB | ||
| 4. Variable and Adaptive Learning Rates.mp4 | 34.9 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 | 38.9 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 | 44.9 MB | ||
| 4. Why is Google the King of Distributed Computing.srt | 11.3 KB | ||
| 5. Activation Functions.mp4 | 80.5 MB | ||
| 5. Activation Functions.srt | 22.6 KB | ||
| 5. Adam (pt 1).mp4 | 55.1 MB | ||
| 5. Adam (pt 1).srt | 16.7 KB | ||
| 5. CNN Architecture.mp4 | 80.6 MB | ||
| 5. CNN Architecture.srt | 27.9 KB | ||
| 5. CNNs for Text.mp4 | 40.4 MB | ||
| 5. CNNs for Text.srt | 10.1 KB | ||
| 5. Code pt 1.mp4 | 39.5 MB | ||
| 5. Code pt 1.srt | 7.2 KB | ||
| 5. How to Succeed in this Course.mp4 | 43.8 MB | ||
| 5. How to Succeed in this Course.srt | 8.3 KB | ||
| 5. Is Theano Dead.mp4 | 40.8 MB | ||
| 5. Is Theano Dead.srt | 12.6 KB | ||
| 5. Recurrent Neural Networks.mp4 | 83 MB | ||
| 5. Recurrent Neural Networks.srt | 25.6 KB | ||
| 5. Regression Notebook.mp4 | 57.5 MB | ||
| 5. Regression Notebook.srt | 12.1 KB | ||
| 5. The Return.mp4 | 21.1 MB | ||
| 5. The Return.srt | 6.3 KB | ||
| 5. Training with Distributed Strategies.mp4 | 43.5 MB | ||
| 5. Training with Distributed Strategies.srt | 8.5 KB | ||
| 5. Transfer Learning Code (pt 1).mp4 | 66.5 MB | ||
| 5. Transfer Learning Code (pt 1).srt | 13.8 KB | ||
| 6. Adam (pt 2).mp4 | 52.8 MB | ||
| 6. Adam (pt 2).srt | 14.5 KB | ||
| 6. CNN Code Preparation.mp4 | 76.9 MB | ||
| 6. CNN Code Preparation.srt | 19.6 KB | ||
| 6. Code pt 2.mp4 | 68 MB | ||
| 6. Code pt 2.srt | 11.8 KB | ||
| 6. Multiclass Classification.mp4 | 41.4 MB | ||
| 6. Multiclass Classification.srt | 11 KB | ||
| 6. RNN Code Preparation.mp4 | 18.4 MB | ||
| 6. RNN Code Preparation.srt | 7.1 KB | ||
| 6. Text Classification with CNNs.mp4 | 39.6 MB | ||
| 6. Text Classification with CNNs.srt | 6.6 KB | ||
| 6. The Neuron.mp4 | 42.6 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.mp4 | 45.2 MB | ||
| 6. Using the TPU.srt | 7 KB | ||
| 6. Value Functions and the Bellman Equation.mp4 | 43.6 MB | ||
| 6. Value Functions and the Bellman Equation.srt | 12.5 KB | ||
| 7. CNN for Fashion MNIST.mp4 | 42.8 MB | ||
| 7. CNN for Fashion MNIST.srt | 8 KB | ||
| 7. Code pt 3.mp4 | 52 MB | ||
| 7. Code pt 3.srt | 7.8 KB | ||
| 7. How does a model learn.mp4 | 48 MB | ||
| 7. How does a model learn.srt | 14 KB | ||
| 7. How to Represent Images.mp4 | 70.5 MB | ||
| 7. How to Represent Images.srt | 15.6 KB | ||
| 7. RNN for Time Series Prediction.mp4 | 74.1 MB | ||
| 7. RNN for Time Series Prediction.srt | 11.2 KB | ||
| 7. What does it mean to “learn”.mp4 | 31.7 MB | ||
| 7. What does it mean to “learn”.srt | 8.9 KB | ||
| 8. CNN for CIFAR-10.mp4 | 29.7 MB | ||
| 8. CNN for CIFAR-10.srt | 5.4 KB | ||
| 8. Code Preparation (ANN).mp4 | 50.9 MB | ||
| 8. Code Preparation (ANN).srt | 16.3 KB | ||
| 8. Code pt 4.mp4 | 52.5 MB | ||
| 8. Code pt 4.srt | 8.4 KB | ||
| 8. Making Predictions.mp4 | 33.9 MB | ||
| 8. Making Predictions.srt | 8 KB | ||
| 8. Paying Attention to Shapes.mp4 | 52.5 MB | ||
| 8. Paying Attention to Shapes.srt | 9.9 KB | ||
| 8. Solving the Bellman Equation with Reinforcement Learning (pt 1).mp4 | 42.7 MB | ||
| 8. Solving the Bellman Equation with Reinforcement Learning (pt 1).srt | 12.4 KB | ||
| 9. ANN for Image Classification.mp4 | 47.7 MB | ||
| 9. ANN for Image Classification.srt | 9.9 KB | ||
| 9. Data Augmentation.mp4 | 35 MB | ||
| 9. Data Augmentation.srt | 11.2 KB | ||
| 9. GRU and LSTM (pt 1).mp4 | 79.9 MB | ||
| 9. GRU and LSTM (pt 1).srt | 22.8 KB | ||
| 9. Reinforcement Learning Stock Trader Discussion.mp4 | 16.6 MB | ||
| 9. Reinforcement Learning Stock Trader Discussion.srt | 4.4 KB | ||
| 9. Saving and Loading a Model.mp4 | 29.7 MB | ||
| 9. Saving and Loading a Model.srt | 4.9 KB | ||
| 9. Solving the Bellman Equation with Reinforcement Learning (pt 2).mp4 | 52.9 MB | ||
| 9. Solving the Bellman Equation with Reinforcement Learning (pt 2).srt | 14.9 KB | ||
| [CourseClub.ME].url | 102.4 B | ||
| [CourseClub.Me].url | 102.4 B | ||
| [GigaCourse.Com].url | 0 B | ||
| ▲ 283 total files | |||
Tensorflow 2.0: Deep Learning and Artificial Intelligence
Neural Networks for Computer Vision, Time Series Forecasting, NLP, GANs, Reinforcement Learning, and More!
Created by Lazy Programmer Team, Lazy Programmer Inc.
Last updated 2/2022
English
English [Auto-generated]
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