[FreeCourseLab.com] Udemy - Zero to Deep Learning™ with Python and Keras

磁力链接/BT种子名称

[FreeCourseLab.com] Udemy - Zero to Deep Learning™ with Python and Keras

磁力链接/BT种子简介

种子哈希：5737267f67f7a4f8d1fef878d051f8fa9db957b2
文件大小： 1.89G
已经下载：1024次
下载速度：极快
收录时间：2022-04-06
最近下载：2025-05-21

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文件列表

  • 1. Welcome to the course!/5. Installation Video Guide.mp4 64.1 MB
  • 3. Machine Learning/22. Exercise 2 solution.mp4 45.3 MB
  • 3. Machine Learning/8. Linear Regression code along.mp4 41.8 MB
  • 3. Machine Learning/1. Section 3 Intro.mp4 40.9 MB
  • 3. Machine Learning/20. Exercise 1 solution.mp4 39.1 MB
  • 6. Convolutional Neural Networks/1. Section 6 Intro.mp4 37.2 MB
  • 4. Deep Learning Intro/7. Multiclass classification code along.mp4 35.6 MB
  • 1. Welcome to the course!/2. Introduction.mp4 35.5 MB
  • 5. Gradient Descent/10. Learning Rate code along.mp4 34.8 MB
  • 1. Welcome to the course!/8. Your first deep learning model.mp4 34.5 MB
  • 2. Data/5. Plotting with Matplotlib.mp4 34.4 MB
  • 5. Gradient Descent/17. Inner Layers Visualization code along.mp4 33.8 MB
  • 2. Data/3. Data exploration with Pandas code along.mp4 32.4 MB
  • 4. Deep Learning Intro/1. Section 4 Intro.mp4 32.4 MB
  • 5. Gradient Descent/1. Section 5 Intro.mp4 32.3 MB
  • 1. Welcome to the course!/1. Welcome to the course!.mp4 28.7 MB
  • 9. Improving performance/14. Movies Reviews Sentiment Analysis code along.mp4 28.0 MB
  • 1. Welcome to the course!/4. Download and install Anaconda.mp4 26.8 MB
  • 4. Deep Learning Intro/13. Exercise 2 Solution.mp4 26.4 MB
  • 4. Deep Learning Intro/11. Exercise 1 Solution.mp4 26.0 MB
  • 8. Recurrent Neural Networks/1. Section 8 Intro.mp4 25.7 MB
  • 9. Improving performance/19. Exercise 3 Presentation.mp4 25.0 MB
  • 6. Convolutional Neural Networks/22. Exercise 2 Presentation.mp4 24.9 MB
  • 1. Welcome to the course!/3. Real world applications of deep learning.mp4 24.8 MB
  • 3. Machine Learning/12. Classification code along.mp4 24.0 MB
  • 2. Data/1. Section 2 Intro.mp4 23.3 MB
  • 8. Recurrent Neural Networks/9. Rolling Windows code along.mp4 22.2 MB
  • 4. Deep Learning Intro/5. Neural Networks code along.mp4 22.1 MB
  • 4. Deep Learning Intro/15. Exercise 3 Solution.mp4 21.6 MB
  • 6. Convolutional Neural Networks/4. MNIST Classification code along.mp4 20.6 MB
  • 5. Gradient Descent/8. Numpy Arrays code along.mp4 20.4 MB
  • 1. Welcome to the course!/7. Course Folder Walkthrough.vtt 20.2 MB
  • 1. Welcome to the course!/7. Course Folder Walkthrough.mp4 20.2 MB
  • 9. Improving performance/1. Section 9 Intro.mp4 20.1 MB
  • 9. Improving performance/3. Learning curves code along.mp4 19.9 MB
  • 3. Machine Learning/6. Cost Function code along.mp4 18.8 MB
  • 5. Gradient Descent/19. Exercise 1 Solution.mp4 18.7 MB
  • 6. Convolutional Neural Networks/20. Exercise 1 Presentation.mp4 18.1 MB
  • 6. Convolutional Neural Networks/7. Tensor Math code along.mp4 18.0 MB
  • 8. Recurrent Neural Networks/6. Time Series Forecasting code along.mp4 16.9 MB
  • 3. Machine Learning/18. Feature Preprocessing code along.mp4 16.8 MB
  • 9. Improving performance/10. Image Generator code along.mp4 16.1 MB
  • 9. Improving performance/5. Batch Normalization code along.mp4 15.7 MB
  • 6. Convolutional Neural Networks/13. Convolutional Layers code along.mp4 15.1 MB
  • 3. Machine Learning/11. Classification.mp4 14.9 MB
  • 2. Data/7. Images and Sound in Jupyter.mp4 14.7 MB
  • 6. Convolutional Neural Networks/17. Convolutional Neural Networks code along.mp4 14.7 MB
  • 4. Deep Learning Intro/17. Exercise 4 Solution.mp4 14.3 MB
  • 2. Data/12. Exercise 2 Solution.mp4 14.2 MB
  • 5. Gradient Descent/16. Initialization code along.mp4 14.0 MB
  • 5. Gradient Descent/12. Gradient Descent code along.mp4 13.8 MB
  • 6. Convolutional Neural Networks/21. Exercise 1 Solution.mp4 13.5 MB
  • 5. Gradient Descent/23. Exercise 3 Solution.mp4 13.2 MB
  • 5. Gradient Descent/6. Fully Connected Backpropagation.mp4 13.1 MB
  • 5. Gradient Descent/4. Chain Rule.mp4 13.1 MB
  • 3. Machine Learning/14. Cross Validation.mp4 13.0 MB
  • 6. Convolutional Neural Networks/12. Convolutional Layers.mp4 12.9 MB
  • 2. Data/2. Tabular data.mp4 12.3 MB
  • 8. Recurrent Neural Networks/5. LSTM and GRU.mp4 12.0 MB
  • 2. Data/6. Unstructured Data.mp4 11.9 MB
  • 3. Machine Learning/16. Confusion matrix.mp4 11.8 MB
  • 5. Gradient Descent/25. Exercise 4 Solution.mp4 11.7 MB
  • 5. Gradient Descent/21. Exercise 2 Solution.mp4 11.5 MB
  • 6. Convolutional Neural Networks/23. Exercise 2 Solution.mp4 11.2 MB
  • 5. Gradient Descent/15. Optimizers code along.mp4 11.2 MB
  • 8. Recurrent Neural Networks/10. Exercise 1 Presentation.mp4 11.2 MB
  • 5. Gradient Descent/2. Derivatives and Gradient.mp4 11.0 MB
  • 8. Recurrent Neural Networks/2. Time Series.mp4 10.9 MB
  • 3. Machine Learning/3. Supervised Learning.mp4 10.9 MB
  • 6. Convolutional Neural Networks/6. Images as Tensors.mp4 10.9 MB
  • 3. Machine Learning/10. Evaluating Performance code along.mp4 10.9 MB
  • 4. Deep Learning Intro/6. Multiple Outputs.mp4 10.8 MB
  • 4. Deep Learning Intro/2. Deep Learning successes.mp4 10.6 MB
  • 4. Deep Learning Intro/9. Feed forward.mp4 10.5 MB
  • 4. Deep Learning Intro/3. Neural Networks.mp4 10.4 MB
  • 3. Machine Learning/4. Linear Regression.mp4 10.2 MB
  • 3. Machine Learning/15. Cross Validation code along.mp4 10.2 MB
  • 8. Recurrent Neural Networks/7. Time Series Forecasting with LSTM code along.mp4 9.9 MB
  • 2. Data/10. Exercise 1 Solution.mp4 9.9 MB
  • 3. Machine Learning/13. Overfitting.mp4 9.8 MB
  • 3. Machine Learning/2. Machine Learning Problems.mp4 9.8 MB
  • 5. Gradient Descent/14. Optimizers.mp4 9.6 MB
  • 2. Data/4. Visual data Exploration.mp4 9.6 MB
  • 3. Machine Learning/9. Evaluating Performance.mp4 9.5 MB
  • 8. Recurrent Neural Networks/3. Sequence problems.mp4 9.2 MB
  • 3. Machine Learning/17. Confusion Matrix code along.mp4 9.1 MB
  • 5. Gradient Descent/7. Matrix Notation.mp4 9.0 MB
  • 4. Deep Learning Intro/8. Activation Functions.mp4 8.9 MB
  • 4. Deep Learning Intro/4. Deeper Networks.mp4 8.9 MB
  • 5. Gradient Descent/26. Tensorboard.mp4 8.7 MB
  • 8. Recurrent Neural Networks/12. Exercise 2 Presentation.mp4 8.6 MB
  • 6. Convolutional Neural Networks/2. Features from Pixels.mp4 8.3 MB
  • 8. Recurrent Neural Networks/11. Exercise 1 Solution.mp4 8.3 MB
  • 2. Data/18. Exercise 5 Solution.mp4 8.2 MB
  • 9. Improving performance/15. Exercise 1 Presentation.mp4 8.0 MB
  • 3. Machine Learning/21. Exercise 2 Presentation.mp4 7.8 MB
  • 5. Gradient Descent/13. EWMA.mp4 7.7 MB
  • 6. Convolutional Neural Networks/5. Beyond Pixels.mp4 7.7 MB
  • 5. Gradient Descent/3. Backpropagation intuition.mp4 7.6 MB
  • 5. Gradient Descent/5. Derivative Calculation.mp4 7.5 MB
  • 9. Improving performance/11. Hyperparameter search.mp4 7.5 MB
  • 2. Data/14. Exercise 3 Solution.mp4 7.5 MB
  • 6. Convolutional Neural Networks/10. Convolution in 2 D.mp4 7.0 MB
  • 6. Convolutional Neural Networks/8. Convolution in 1 D.mp4 7.0 MB
  • 9. Improving performance/12. Embeddings.mp4 6.9 MB
  • 4. Deep Learning Intro/6. Multiple Outputs.vtt 6.8 MB
  • 3. Machine Learning/19. Exercise 1 Presentation.mp4 6.8 MB
  • 5. Gradient Descent/11. Gradient Descent.mp4 6.7 MB
  • 5. Gradient Descent/11. Gradient Descent.vtt 6.7 MB
  • 6. Convolutional Neural Networks/11. Image Filters code along.mp4 6.7 MB
  • 9. Improving performance/7. Dropout and Regularization code along.mp4 6.6 MB
  • 9. Improving performance/8. Data Augmentation.mp4 6.4 MB
  • 3. Machine Learning/5. Cost Function.mp4 6.3 MB
  • 9. Improving performance/6. Dropout.mp4 6.2 MB
  • 2. Data/8. Feature Engineering.mp4 6.1 MB
  • 9. Improving performance/13. Embeddings code along.mp4 6.0 MB
  • 8. Recurrent Neural Networks/4. Vanilla RNN.mp4 5.9 MB
  • 9. Improving performance/17. Exercise 2 Presentation.mp4 5.8 MB
  • 9. Improving performance/2. Learning curves.mp4 5.7 MB
  • 6. Convolutional Neural Networks/15. Pooling Layers code along.mp4 5.5 MB
  • 6. Convolutional Neural Networks/19. Beyond Images.mp4 5.5 MB
  • 6. Convolutional Neural Networks/18. Weights in CNNs.mp4 5.5 MB
  • 6. Convolutional Neural Networks/9. Convolution in 1 D code along.mp4 5.4 MB
  • 3. Machine Learning/7. Finding the best model.mp4 5.3 MB
  • 8. Recurrent Neural Networks/8. Rolling Windows.mp4 5.3 MB
  • 9. Improving performance/9. Continuous Learning.mp4 5.1 MB
  • 4. Deep Learning Intro/10. Exercise 1 Presentation.mp4 5.0 MB
  • 6. Convolutional Neural Networks/16. Convolutional Neural Networks.mp4 4.7 MB
  • 2. Data/16. Exercise 4 Solution.mp4 4.4 MB
  • 6. Convolutional Neural Networks/3. MNIST Classification.mp4 4.1 MB
  • 9. Improving performance/4. Batch Normalization.mp4 4.0 MB
  • 9. Improving performance/4. Batch Normalization.vtt 4.0 MB
  • 5. Gradient Descent/24. Exercise 4 Presentation.mp4 3.9 MB
  • 5. Gradient Descent/9. Learning Rate.mp4 3.9 MB
  • 5. Gradient Descent/18. Exercise 1 Presentation.mp4 3.7 MB
  • 2. Data/9. Exercise 1 Presentation.mp4 3.4 MB
  • 6. Convolutional Neural Networks/14. Pooling Layers.mp4 3.2 MB
  • 4. Deep Learning Intro/12. Exercise 2 Presentation.mp4 3.2 MB
  • 5. Gradient Descent/22. Exercise 3 Presentation.mp4 3.1 MB
  • 4. Deep Learning Intro/14. Exercise 3 Presentation.mp4 3.0 MB
  • 5. Gradient Descent/20. Exercise 2 Presentation.mp4 2.6 MB
  • 2. Data/17. Exercise 5 Presentation.mp4 2.2 MB
  • 4. Deep Learning Intro/16. Exercise 4 Presentation.mp4 2.0 MB
  • 2. Data/11. Exercise 2 Presentation.mp4 2.0 MB
  • 2. Data/13. Exercise 3 Presentation.mp4 1.8 MB
  • 2. Data/15. Exercise 4 Presentation.mp4 1.6 MB
  • 3. Machine Learning/20. Exercise 1 solution.vtt 11.7 kB
  • 3. Machine Learning/22. Exercise 2 solution.vtt 11.6 kB
  • 3. Machine Learning/8. Linear Regression code along.vtt 10.3 kB
  • 2. Data/3. Data exploration with Pandas code along.vtt 10.2 kB
  • 1. Welcome to the course!/3. Real world applications of deep learning.vtt 9.8 kB
  • 9. Improving performance/14. Movies Reviews Sentiment Analysis code along.vtt 9.4 kB
  • 1. Welcome to the course!/8. Your first deep learning model.vtt 9.4 kB
  • 4. Deep Learning Intro/7. Multiclass classification code along.vtt 8.6 kB
  • 5. Gradient Descent/10. Learning Rate code along.vtt 8.0 kB
  • 3. Machine Learning/11. Classification.vtt 7.7 kB
  • 3. Machine Learning/12. Classification code along.vtt 7.5 kB
  • 4. Deep Learning Intro/13. Exercise 2 Solution.vtt 7.1 kB
  • 5. Gradient Descent/17. Inner Layers Visualization code along.vtt 7.0 kB
  • 5. Gradient Descent/8. Numpy Arrays code along.vtt 6.9 kB
  • 3. Machine Learning/14. Cross Validation.vtt 6.8 kB
  • 4. Deep Learning Intro/11. Exercise 1 Solution.vtt 6.8 kB
  • 2. Data/2. Tabular data.vtt 6.4 kB
  • 8. Recurrent Neural Networks/5. LSTM and GRU.vtt 6.4 kB
  • 4. Deep Learning Intro/5. Neural Networks code along.vtt 6.3 kB
  • 8. Recurrent Neural Networks/6. Time Series Forecasting code along.vtt 6.3 kB
  • 3. Machine Learning/16. Confusion matrix.vtt 6.2 kB
  • 1. Welcome to the course!/5. Installation Video Guide.vtt 6.1 kB
  • 3. Machine Learning/6. Cost Function code along.vtt 6.0 kB
  • 6. Convolutional Neural Networks/12. Convolutional Layers.vtt 6.0 kB
  • 9. Improving performance/3. Learning curves code along.vtt 5.9 kB
  • 8. Recurrent Neural Networks/9. Rolling Windows code along.vtt 5.8 kB
  • 8. Recurrent Neural Networks/2. Time Series.vtt 5.6 kB
  • 9. Improving performance/10. Image Generator code along.vtt 5.5 kB
  • 6. Convolutional Neural Networks/4. MNIST Classification code along.vtt 5.4 kB
  • 3. Machine Learning/13. Overfitting.vtt 5.4 kB
  • 3. Machine Learning/9. Evaluating Performance.vtt 5.4 kB
  • 4. Deep Learning Intro/17. Exercise 4 Solution.vtt 5.4 kB
  • 6. Convolutional Neural Networks/17. Convolutional Neural Networks code along.vtt 5.3 kB
  • 2. Data/6. Unstructured Data.vtt 5.3 kB
  • 9. Improving performance/5. Batch Normalization code along.vtt 5.3 kB
  • 6. Convolutional Neural Networks/6. Images as Tensors.vtt 5.2 kB
  • 6. Convolutional Neural Networks/13. Convolutional Layers code along.vtt 5.2 kB
  • 5. Gradient Descent/2. Derivatives and Gradient.vtt 5.2 kB
  • 2. Data/4. Visual data Exploration.vtt 5.2 kB
  • 3. Machine Learning/18. Feature Preprocessing code along.vtt 5.1 kB
  • 4. Deep Learning Intro/3. Neural Networks.vtt 5.0 kB
  • 3. Machine Learning/3. Supervised Learning.vtt 5.0 kB
  • 5. Gradient Descent/19. Exercise 1 Solution.vtt 5.0 kB
  • 8. Recurrent Neural Networks/3. Sequence problems.vtt 5.0 kB
  • 4. Deep Learning Intro/9. Feed forward.vtt 4.9 kB
  • 5. Gradient Descent/14. Optimizers.vtt 4.8 kB
  • 4. Deep Learning Intro/2. Deep Learning successes.vtt 4.7 kB
  • 3. Machine Learning/4. Linear Regression.vtt 4.7 kB
  • 5. Gradient Descent/13. EWMA.vtt 4.5 kB
  • 4. Deep Learning Intro/8. Activation Functions.vtt 4.4 kB
  • 3. Machine Learning/10. Evaluating Performance code along.vtt 4.4 kB
  • 2. Data/7. Images and Sound in Jupyter.vtt 4.3 kB
  • 5. Gradient Descent/23. Exercise 3 Solution.vtt 4.2 kB
  • 6. Convolutional Neural Networks/21. Exercise 1 Solution.vtt 4.1 kB
  • 5. Gradient Descent/16. Initialization code along.vtt 4.1 kB
  • 5. Gradient Descent/7. Matrix Notation.vtt 4.0 kB
  • 9. Improving performance/11. Hyperparameter search.vtt 4.0 kB
  • 5. Gradient Descent/3. Backpropagation intuition.vtt 4.0 kB
  • 3. Machine Learning/15. Cross Validation code along.vtt 4.0 kB
  • 5. Gradient Descent/4. Chain Rule.vtt 3.9 kB
  • 5. Gradient Descent/6. Fully Connected Backpropagation.vtt 3.7 kB
  • 5. Gradient Descent/15. Optimizers code along.vtt 3.7 kB
  • 5. Gradient Descent/5. Derivative Calculation.vtt 3.7 kB
  • 2. Data/12. Exercise 2 Solution.vtt 3.7 kB
  • 4. Deep Learning Intro/4. Deeper Networks.vtt 3.6 kB
  • 6. Convolutional Neural Networks/5. Beyond Pixels.vtt 3.6 kB
  • 3. Machine Learning/2. Machine Learning Problems.vtt 3.5 kB
  • 5. Gradient Descent/21. Exercise 2 Solution.vtt 3.5 kB
  • 6. Convolutional Neural Networks/23. Exercise 2 Solution.vtt 3.5 kB
  • 9. Improving performance/12. Embeddings.vtt 3.5 kB
  • 6. Convolutional Neural Networks/2. Features from Pixels.vtt 3.4 kB
  • 3. Machine Learning/17. Confusion Matrix code along.vtt 3.4 kB
  • 5. Gradient Descent/25. Exercise 4 Solution.vtt 3.4 kB
  • 6. Convolutional Neural Networks/10. Convolution in 2 D.vtt 3.3 kB
  • 3. Machine Learning/5. Cost Function.vtt 3.3 kB
  • 9. Improving performance/2. Learning curves.vtt 3.2 kB
  • 2. Data/10. Exercise 1 Solution.vtt 3.2 kB
  • 5. Gradient Descent/26. Tensorboard.vtt 3.1 kB
  • 8. Recurrent Neural Networks/7. Time Series Forecasting with LSTM code along.vtt 3.1 kB
  • 4. Deep Learning Intro/15. Exercise 3 Solution.vtt 3.0 kB
  • 5. Gradient Descent/12. Gradient Descent code along.vtt 3.0 kB
  • 6. Convolutional Neural Networks/22. Exercise 2 Presentation.vtt 2.9 kB
  • 6. Convolutional Neural Networks/8. Convolution in 1 D.vtt 2.9 kB
  • 3. Machine Learning/21. Exercise 2 Presentation.vtt 2.8 kB
  • 8. Recurrent Neural Networks/8. Rolling Windows.vtt 2.8 kB
  • 9. Improving performance/8. Data Augmentation.vtt 2.8 kB
  • 8. Recurrent Neural Networks/4. Vanilla RNN.vtt 2.8 kB
  • 3. Machine Learning/19. Exercise 1 Presentation.vtt 2.8 kB
  • 9. Improving performance/6. Dropout.vtt 2.8 kB
  • 3. Machine Learning/7. Finding the best model.vtt 2.7 kB
  • 9. Improving performance/9. Continuous Learning.vtt 2.7 kB
  • 1. Welcome to the course!/4. Download and install Anaconda.vtt 2.7 kB
  • 2. Data/8. Feature Engineering.vtt 2.7 kB
  • 6. Convolutional Neural Networks/18. Weights in CNNs.vtt 2.6 kB
  • 6. Convolutional Neural Networks/19. Beyond Images.vtt 2.5 kB
  • 9. Improving performance/19. Exercise 3 Presentation.vtt 2.4 kB
  • 9. Improving performance/13. Embeddings code along.vtt 2.2 kB
  • 9. Improving performance/7. Dropout and Regularization code along.vtt 2.1 kB
  • 6. Convolutional Neural Networks/11. Image Filters code along.vtt 2.1 kB
  • 3. Machine Learning/1. Section 3 Intro.vtt 2.0 kB
  • 6. Convolutional Neural Networks/16. Convolutional Neural Networks.vtt 2.0 kB
  • 5. Gradient Descent/9. Learning Rate.vtt 2.0 kB
  • 7. Cloud GPUs/2. Floyd GPU notebook setup.html 2.0 kB
  • 1. Welcome to the course!/2. Introduction.vtt 1.9 kB
  • 7. Cloud GPUs/1. Google Colaboratory GPU notebook setup.html 1.9 kB
  • 8. Recurrent Neural Networks/11. Exercise 1 Solution.vtt 1.9 kB
  • 6. Convolutional Neural Networks/15. Pooling Layers code along.vtt 1.8 kB
  • 2. Data/9. Exercise 1 Presentation.vtt 1.8 kB
  • 6. Convolutional Neural Networks/20. Exercise 1 Presentation.vtt 1.8 kB
  • 5. Gradient Descent/24. Exercise 4 Presentation.vtt 1.7 kB
  • 2. Data/14. Exercise 3 Solution.vtt 1.7 kB
  • 6. Convolutional Neural Networks/1. Section 6 Intro.vtt 1.7 kB
  • 1. Welcome to the course!/1. Welcome to the course!.vtt 1.7 kB
  • 4. Deep Learning Intro/10. Exercise 1 Presentation.vtt 1.6 kB
  • 5. Gradient Descent/22. Exercise 3 Presentation.vtt 1.6 kB
  • 5. Gradient Descent/1. Section 5 Intro.vtt 1.6 kB
  • 4. Deep Learning Intro/1. Section 4 Intro.vtt 1.6 kB
  • 4. Deep Learning Intro/14. Exercise 3 Presentation.vtt 1.5 kB
  • 4. Deep Learning Intro/12. Exercise 2 Presentation.vtt 1.4 kB
  • 6. Convolutional Neural Networks/3. MNIST Classification.vtt 1.4 kB
  • 2. Data/18. Exercise 5 Solution.vtt 1.3 kB
  • 2. Data/16. Exercise 4 Solution.vtt 1.3 kB
  • 5. Gradient Descent/18. Exercise 1 Presentation.vtt 1.3 kB
  • 6. Convolutional Neural Networks/14. Pooling Layers.vtt 1.3 kB
  • 8. Recurrent Neural Networks/12. Exercise 2 Presentation.vtt 1.2 kB
  • 8. Recurrent Neural Networks/10. Exercise 1 Presentation.vtt 1.2 kB
  • 6. Convolutional Neural Networks/9. Convolution in 1 D code along.vtt 1.2 kB
  • 2. Data/17. Exercise 5 Presentation.vtt 1.2 kB
  • 2. Data/11. Exercise 2 Presentation.vtt 1.1 kB
  • 5. Gradient Descent/20. Exercise 2 Presentation.vtt 1.1 kB
  • 8. Recurrent Neural Networks/1. Section 8 Intro.vtt 1.1 kB
  • 4. Deep Learning Intro/16. Exercise 4 Presentation.vtt 1.1 kB
  • 9. Improving performance/15. Exercise 1 Presentation.vtt 1.1 kB
  • 2. Data/1. Section 2 Intro.vtt 1.0 kB
  • 1. Welcome to the course!/6. Obtain the code for the course.html 1.0 kB
  • 9. Improving performance/1. Section 9 Intro.vtt 1.0 kB
  • 9. Improving performance/17. Exercise 2 Presentation.vtt 984 Bytes
  • 2. Data/13. Exercise 3 Presentation.vtt 893 Bytes
  • 2. Data/15. Exercise 4 Presentation.vtt 784 Bytes
  • [FreeCourseLab.com].url 126 Bytes
  • 1. Welcome to the course!/5.1 Link to Github notebooks.html 120 Bytes
  • 2. Data/5. Plotting with Matplotlib.vtt 111 Bytes
  • 1. Welcome to the course!/5.2 Link to Tensorflow install docs.html 96 Bytes
  • 8. Recurrent Neural Networks/13. Exercise 2 Solution.html 26 Bytes
  • 9. Improving performance/16. Exercise 1 Solution.html 26 Bytes
  • 9. Improving performance/18. Exercise 2 Solution.html 26 Bytes
  • 6. Convolutional Neural Networks/7. Tensor Math code along.vtt 8 Bytes

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