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文档列表

18. Setting up your Environment (FAQ by Student Request)/2. Anaconda Environment Setup.mp4 189.7 MB
18. Setting up your Environment (FAQ by Student Request)/3. Installing NVIDIA GPU-Accelerated Deep Learning Libraries on your Home Computer.mp4 175.4 MB
18. Setting up your Environment (FAQ by Student Request)/1. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4 157.9 MB
6. Recurrent Neural Networks, Time Series, and Sequence Data/12. Demo of the Long Distance Problem.mp4 130.1 MB
20. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/4. Machine Learning and AI Prerequisite Roadmap (pt 2).mp4 113.4 MB
20. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/2. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4 110.7 MB
13. Advanced Tensorflow Usage/2. Tensorflow Serving pt 2.mp4 110.1 MB
11. Deep Reinforcement Learning (Theory)/2. Elements of a Reinforcement Learning Problem.mp4 103.4 MB
6. Recurrent Neural Networks, Time Series, and Sequence Data/1. Sequence Data.mp4 94.5 MB
10. GANs (Generative Adversarial Networks)/1. GAN Theory.mp4 91.4 MB
6. Recurrent Neural Networks, Time Series, and Sequence Data/5. Recurrent Neural Networks.mp4 87.0 MB
5. Convolutional Neural Networks/5. CNN Architecture.mp4 84.5 MB
4. Feedforward Artificial Neural Networks/5. Activation Functions.mp4 84.4 MB
6. Recurrent Neural Networks, Time Series, and Sequence Data/9. GRU and LSTM (pt 1).mp4 83.7 MB
5. Convolutional Neural Networks/1. What is Convolution (part 1).mp4 83.6 MB
20. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/3. Machine Learning and AI Prerequisite Roadmap (pt 1).mp4 83.6 MB
10. GANs (Generative Adversarial Networks)/2. GAN Code.mp4 82.1 MB
5. Convolutional Neural Networks/6. CNN Code Preparation.mp4 80.6 MB
19. Extra Help With Python Coding for Beginners (FAQ by Student Request)/1. Beginner's Coding Tips.mp4 79.4 MB
6. Recurrent Neural Networks, Time Series, and Sequence Data/7. RNN for Time Series Prediction.mp4 77.7 MB
1. Welcome/2. Outline.mp4 77.3 MB
2. Google Colab/3. Uploading your own data to Google Colab.mp4 77.2 MB
5. Convolutional Neural Networks/11. Improving CIFAR-10 Results.mp4 76.5 MB
19. Extra Help With Python Coding for Beginners (FAQ by Student Request)/2. How to Code Yourself (part 1).mp4 75.3 MB
6. Recurrent Neural Networks, Time Series, and Sequence Data/3. Autoregressive Linear Model for Time Series Prediction.mp4 75.2 MB
4. Feedforward Artificial Neural Networks/7. How to Represent Images.mp4 73.9 MB
19. Extra Help With Python Coding for Beginners (FAQ by Student Request)/4. Proof that using Jupyter Notebook is the same as not using it.mp4 72.8 MB
5. Convolutional Neural Networks/4. Convolution on Color Images.mp4 72.8 MB
4. Feedforward Artificial Neural Networks/10. ANN for Regression.mp4 72.6 MB
8. Recommender Systems/1. Recommender Systems with Deep Learning Theory.mp4 72.0 MB
4. Feedforward Artificial Neural Networks/2. Beginners Rejoice The Math in This Course is Optional.mp4 71.8 MB
12. Stock Trading Project with Deep Reinforcement Learning/6. Code pt 2.mp4 71.3 MB
6. Recurrent Neural Networks, Time Series, and Sequence Data/17. Stock Return Predictions using LSTMs (pt 3).mp4 70.6 MB
6. Recurrent Neural Networks, Time Series, and Sequence Data/15. Stock Return Predictions using LSTMs (pt 1).mp4 70.4 MB
9. Transfer Learning for Computer Vision/5. Transfer Learning Code (pt 1).mp4 69.8 MB
3. Machine Learning and Neurons/1. What is Machine Learning.mp4 68.7 MB
6. Recurrent Neural Networks, Time Series, and Sequence Data/11. A More Challenging Sequence.mp4 67.8 MB
1. Welcome/3. Where to get the code.mp4 66.0 MB
11. Deep Reinforcement Learning (Theory)/11. Q-Learning.mp4 64.8 MB
3. Machine Learning and Neurons/2. Code Preparation (Classification Theory).mp4 62.7 MB
8. Recommender Systems/2. Recommender Systems with Deep Learning Code.mp4 61.7 MB
14. Low-Level Tensorflow/4. Build Your Own Custom Model.mp4 61.4 MB
3. Machine Learning and Neurons/5. Regression Notebook.mp4 60.3 MB
7. Natural Language Processing (NLP)/2. Code Preparation (NLP).mp4 59.8 MB
4. Feedforward Artificial Neural Networks/4. The Geometrical Picture.mp4 59.2 MB
11. Deep Reinforcement Learning (Theory)/12. Deep Q-Learning DQN (pt 1).mp4 59.0 MB
14. Low-Level Tensorflow/3. Variables and Gradient Tape.mp4 58.8 MB
9. Transfer Learning for Computer Vision/1. Transfer Learning Theory.mp4 57.8 MB
16. In-Depth Gradient Descent/5. Adam (pt 1).mp4 57.8 MB
3. Machine Learning and Neurons/3. Classification Notebook.mp4 57.2 MB
2. Google Colab/1. Intro to Google Colab, how to use a GPU or TPU for free.mp4 56.5 MB
11. Deep Reinforcement Learning (Theory)/9. Solving the Bellman Equation with Reinforcement Learning (pt 2).mp4 55.5 MB
16. In-Depth Gradient Descent/6. Adam (pt 2).mp4 55.3 MB
7. Natural Language Processing (NLP)/1. Embeddings.mp4 55.1 MB
12. Stock Trading Project with Deep Reinforcement Learning/8. Code pt 4.mp4 55.1 MB
6. Recurrent Neural Networks, Time Series, and Sequence Data/8. Paying Attention to Shapes.mp4 55.0 MB
12. Stock Trading Project with Deep Reinforcement Learning/7. Code pt 3.mp4 54.6 MB
12. Stock Trading Project with Deep Reinforcement Learning/2. Data and Environment.mp4 53.4 MB
4. Feedforward Artificial Neural Networks/8. Code Preparation (ANN).mp4 53.4 MB
7. Natural Language Processing (NLP)/4. Text Classification with LSTMs.mp4 53.1 MB
6. Recurrent Neural Networks, Time Series, and Sequence Data/10. GRU and LSTM (pt 2).mp4 52.8 MB
11. Deep Reinforcement Learning (Theory)/13. Deep Q-Learning DQN (pt 2).mp4 52.0 MB
11. Deep Reinforcement Learning (Theory)/4. Markov Decision Processes (MDPs).mp4 51.7 MB
19. Extra Help With Python Coding for Beginners (FAQ by Student Request)/3. How to Code Yourself (part 2).mp4 51.5 MB
3. Machine Learning and Neurons/7. How does a model learn.mp4 50.3 MB
4. Feedforward Artificial Neural Networks/9. ANN for Image Classification.mp4 50.0 MB
6. Recurrent Neural Networks, Time Series, and Sequence Data/2. Forecasting.mp4 49.0 MB
4. Feedforward Artificial Neural Networks/3. Forward Propagation.mp4 49.0 MB
9. Transfer Learning for Computer Vision/6. Transfer Learning Code (pt 2).mp4 48.3 MB
13. Advanced Tensorflow Usage/6. Using the TPU.mp4 47.4 MB
13. Advanced Tensorflow Usage/4. Why is Google the King of Distributed Computing.mp4 47.1 MB
2. Google Colab/5. How to Succeed in this Course.mp4 45.9 MB
11. Deep Reinforcement Learning (Theory)/6. Value Functions and the Bellman Equation.mp4 45.7 MB
13. Advanced Tensorflow Usage/5. Training with Distributed Strategies.mp4 45.7 MB
11. Deep Reinforcement Learning (Theory)/3. States, Actions, Rewards, Policies.mp4 45.4 MB
5. Convolutional Neural Networks/7. CNN for Fashion MNIST.mp4 44.9 MB
11. Deep Reinforcement Learning (Theory)/8. Solving the Bellman Equation with Reinforcement Learning (pt 1).mp4 44.8 MB
13. Advanced Tensorflow Usage/3. Tensorflow Lite (TFLite).mp4 44.7 MB
3. Machine Learning and Neurons/6. The Neuron.mp4 44.6 MB
12. Stock Trading Project with Deep Reinforcement Learning/10. Help! Why is the code slower on my machine.mp4 44.5 MB
4. Feedforward Artificial Neural Networks/6. Multiclass Classification.mp4 43.4 MB
19. Extra Help With Python Coding for Beginners (FAQ by Student Request)/5. Is Theano Dead.mp4 42.7 MB
2. Google Colab/2. Tensorflow 2.0 in Google Colab.mp4 42.6 MB
7. Natural Language Processing (NLP)/5. CNNs for Text.mp4 42.4 MB
14. Low-Level Tensorflow/2. Constants and Basic Computation.mp4 42.3 MB
11. Deep Reinforcement Learning (Theory)/10. Epsilon-Greedy.mp4 42.1 MB
7. Natural Language Processing (NLP)/6. Text Classification with CNNs.mp4 41.5 MB
12. Stock Trading Project with Deep Reinforcement Learning/5. Code pt 1.mp4 41.5 MB
2. Google Colab/4. Where can I learn about Numpy, Scipy, Matplotlib, Pandas, and Scikit-Learn.mp4 40.8 MB
14. Low-Level Tensorflow/1. Differences Between Tensorflow 1.x and Tensorflow 2.x.mp4 40.6 MB
11. Deep Reinforcement Learning (Theory)/1. Deep Reinforcement Learning Section Introduction.mp4 39.9 MB
17. Extras/1. How to Choose Hyperparameters.mp4 39.8 MB
21. Appendix FAQ Finale/2. BONUS Lecture.mp4 39.6 MB
11. Deep Reinforcement Learning (Theory)/14. How to Learn Reinforcement Learning.mp4 39.5 MB
9. Transfer Learning for Computer Vision/3. Large Datasets and Data Generators.mp4 38.3 MB
20. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/1. How to Succeed in this Course (Long Version).mp4 36.9 MB
5. Convolutional Neural Networks/9. Data Augmentation.mp4 36.6 MB
16. In-Depth Gradient Descent/1. Gradient Descent.mp4 36.6 MB
16. In-Depth Gradient Descent/4. Variable and Adaptive Learning Rates.mp4 36.5 MB
1. Welcome/1. Introduction.mp4 36.5 MB
16. In-Depth Gradient Descent/3. Momentum.mp4 35.9 MB
3. Machine Learning and Neurons/8. Making Predictions.mp4 35.5 MB
15. In-Depth Loss Functions/1. Mean Squared Error.mp4 35.4 MB
6. Recurrent Neural Networks, Time Series, and Sequence Data/16. Stock Return Predictions using LSTMs (pt 2).mp4 34.6 MB
11. Deep Reinforcement Learning (Theory)/7. What does it mean to “learn”.mp4 33.3 MB
15. In-Depth Loss Functions/3. Categorical Cross Entropy.mp4 33.2 MB
9. Transfer Learning for Computer Vision/2. Some Pre-trained Models (VGG, ResNet, Inception, MobileNet).mp4 33.1 MB
4. Feedforward Artificial Neural Networks/1. Artificial Neural Networks Section Introduction.mp4 31.3 MB
3. Machine Learning and Neurons/9. Saving and Loading a Model.mp4 31.2 MB
5. Convolutional Neural Networks/8. CNN for CIFAR-10.mp4 31.1 MB
6. Recurrent Neural Networks, Time Series, and Sequence Data/13. RNN for Image Classification (Theory).mp4 30.5 MB
7. Natural Language Processing (NLP)/3. Text Preprocessing.mp4 30.2 MB
6. Recurrent Neural Networks, Time Series, and Sequence Data/18. Other Ways to Forecast.mp4 29.7 MB
13. Advanced Tensorflow Usage/1. What is a Web Service (Tensorflow Serving pt 1).mp4 29.1 MB
5. Convolutional Neural Networks/3. What is Convolution (part 3).mp4 29.0 MB
3. Machine Learning and Neurons/4. Code Preparation (Regression Theory).mp4 28.6 MB
3. Machine Learning and Neurons/11. Suggestion Box.mp4 28.4 MB
3. Machine Learning and Neurons/10. Why Keras.mp4 27.8 MB
12. Stock Trading Project with Deep Reinforcement Learning/1. Reinforcement Learning Stock Trader Introduction.mp4 27.3 MB
12. Stock Trading Project with Deep Reinforcement Learning/4. Program Design and Layout.mp4 27.2 MB
17. Extras/2. Where Are The Exercises.mp4 27.2 MB
12. Stock Trading Project with Deep Reinforcement Learning/3. Replay Buffer.mp4 25.2 MB
15. In-Depth Loss Functions/2. Binary Cross Entropy.mp4 24.8 MB
6. Recurrent Neural Networks, Time Series, and Sequence Data/14. RNN for Image Classification (Code).mp4 24.4 MB
16. In-Depth Gradient Descent/2. Stochastic Gradient Descent.mp4 24.1 MB
5. Convolutional Neural Networks/2. What is Convolution (part 2).mp4 23.3 MB
11. Deep Reinforcement Learning (Theory)/5. The Return.mp4 22.2 MB
5. Convolutional Neural Networks/10. Batch Normalization.mp4 22.1 MB
9. Transfer Learning for Computer Vision/4. 2 Approaches to Transfer Learning.mp4 21.6 MB
6. Recurrent Neural Networks, Time Series, and Sequence Data/6. RNN Code Preparation.mp4 19.3 MB
12. Stock Trading Project with Deep Reinforcement Learning/9. Reinforcement Learning Stock Trader Discussion.mp4 17.4 MB
21. Appendix FAQ Finale/1. What is the Appendix.mp4 17.2 MB
6. Recurrent Neural Networks, Time Series, and Sequence Data/4. Proof that the Linear Model Works.mp4 17.0 MB
18. Setting up your Environment (FAQ by Student Request)/3. Installing NVIDIA GPU-Accelerated Deep Learning Libraries on your Home Computer.srt 32.8 kB
20. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/2. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.srt 32.4 kB
5. Convolutional Neural Networks/5. CNN Architecture.srt 28.6 kB
11. Deep Reinforcement Learning (Theory)/2. Elements of a Reinforcement Learning Problem.srt 26.8 kB
6. Recurrent Neural Networks, Time Series, and Sequence Data/5. Recurrent Neural Networks.srt 26.2 kB
6. Recurrent Neural Networks, Time Series, and Sequence Data/1. Sequence Data.srt 24.6 kB
6. Recurrent Neural Networks, Time Series, and Sequence Data/12. Demo of the Long Distance Problem.srt 23.6 kB
20. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/4. Machine Learning and AI Prerequisite Roadmap (pt 2).srt 23.6 kB
6. Recurrent Neural Networks, Time Series, and Sequence Data/9. GRU and LSTM (pt 1).srt 23.3 kB
4. Feedforward Artificial Neural Networks/5. Activation Functions.srt 23.2 kB
19. Extra Help With Python Coding for Beginners (FAQ by Student Request)/2. How to Code Yourself (part 1).srt 22.7 kB
10. GANs (Generative Adversarial Networks)/1. GAN Theory.srt 21.2 kB
5. Convolutional Neural Networks/4. Convolution on Color Images.srt 21.0 kB
13. Advanced Tensorflow Usage/2. Tensorflow Serving pt 2.srt 20.9 kB
3. Machine Learning and Neurons/2. Code Preparation (Classification Theory).srt 20.7 kB
5. Convolutional Neural Networks/1. What is Convolution (part 1).srt 20.6 kB
18. Setting up your Environment (FAQ by Student Request)/2. Anaconda Environment Setup.srt 20.4 kB
5. Convolutional Neural Networks/6. CNN Code Preparation.srt 20.1 kB
19. Extra Help With Python Coding for Beginners (FAQ by Student Request)/1. Beginner's Coding Tips.srt 19.5 kB
3. Machine Learning and Neurons/1. What is Machine Learning.srt 18.9 kB
11. Deep Reinforcement Learning (Theory)/11. Q-Learning.srt 18.3 kB
8. Recommender Systems/1. Recommender Systems with Deep Learning Theory.srt 17.8 kB
1. Welcome/2. Outline.srt 17.5 kB
4. Feedforward Artificial Neural Networks/2. Beginners Rejoice The Math in This Course is Optional.srt 17.4 kB
7. Natural Language Processing (NLP)/2. Code Preparation (NLP).srt 17.2 kB
16. In-Depth Gradient Descent/5. Adam (pt 1).srt 17.1 kB
11. Deep Reinforcement Learning (Theory)/12. Deep Q-Learning DQN (pt 1).srt 16.8 kB
4. Feedforward Artificial Neural Networks/8. Code Preparation (ANN).srt 16.7 kB
7. Natural Language Processing (NLP)/1. Embeddings.srt 16.6 kB
20. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/3. Machine Learning and AI Prerequisite Roadmap (pt 1).srt 16.5 kB
6. Recurrent Neural Networks, Time Series, and Sequence Data/15. Stock Return Predictions using LSTMs (pt 1).srt 16.1 kB
12. Stock Trading Project with Deep Reinforcement Learning/2. Data and Environment.srt 16.1 kB
4. Feedforward Artificial Neural Networks/7. How to Represent Images.srt 16.0 kB
1. Welcome/3. Where to get the code.srt 15.7 kB
16. In-Depth Gradient Descent/4. Variable and Adaptive Learning Rates.srt 15.5 kB
11. Deep Reinforcement Learning (Theory)/9. Solving the Bellman Equation with Reinforcement Learning (pt 2).srt 15.2 kB
10. GANs (Generative Adversarial Networks)/2. GAN Code.srt 15.2 kB
18. Setting up your Environment (FAQ by Student Request)/1. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.srt 15.0 kB
20. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/1. How to Succeed in this Course (Long Version).srt 15.0 kB
16. In-Depth Gradient Descent/6. Adam (pt 2).srt 14.8 kB
6. Recurrent Neural Networks, Time Series, and Sequence Data/17. Stock Return Predictions using LSTMs (pt 3).srt 14.8 kB
6. Recurrent Neural Networks, Time Series, and Sequence Data/10. GRU and LSTM (pt 2).srt 14.6 kB
6. Recurrent Neural Networks, Time Series, and Sequence Data/3. Autoregressive Linear Model for Time Series Prediction.srt 14.6 kB
19. Extra Help With Python Coding for Beginners (FAQ by Student Request)/4. Proof that using Jupyter Notebook is the same as not using it.srt 14.6 kB
2. Google Colab/1. Intro to Google Colab, how to use a GPU or TPU for free.srt 14.5 kB
3. Machine Learning and Neurons/7. How does a model learn.srt 14.3 kB
9. Transfer Learning for Computer Vision/5. Transfer Learning Code (pt 1).srt 14.1 kB
14. Low-Level Tensorflow/3. Variables and Gradient Tape.srt 13.9 kB
6. Recurrent Neural Networks, Time Series, and Sequence Data/2. Forecasting.srt 13.7 kB
14. Low-Level Tensorflow/4. Build Your Own Custom Model.srt 13.6 kB
11. Deep Reinforcement Learning (Theory)/13. Deep Q-Learning DQN (pt 2).srt 13.5 kB
5. Convolutional Neural Networks/11. Improving CIFAR-10 Results.srt 13.5 kB
19. Extra Help With Python Coding for Beginners (FAQ by Student Request)/3. How to Code Yourself (part 2).srt 13.3 kB
4. Feedforward Artificial Neural Networks/10. ANN for Regression.srt 13.1 kB
11. Deep Reinforcement Learning (Theory)/4. Markov Decision Processes (MDPs).srt 13.0 kB
19. Extra Help With Python Coding for Beginners (FAQ by Student Request)/5. Is Theano Dead.srt 12.9 kB
11. Deep Reinforcement Learning (Theory)/6. Value Functions and the Bellman Equation.srt 12.8 kB
3. Machine Learning and Neurons/6. The Neuron.srt 12.8 kB
11. Deep Reinforcement Learning (Theory)/8. Solving the Bellman Equation with Reinforcement Learning (pt 1).srt 12.7 kB
4. Feedforward Artificial Neural Networks/3. Forward Propagation.srt 12.5 kB
14. Low-Level Tensorflow/1. Differences Between Tensorflow 1.x and Tensorflow 2.x.srt 12.5 kB
3. Machine Learning and Neurons/5. Regression Notebook.srt 12.4 kB
2. Google Colab/3. Uploading your own data to Google Colab.srt 12.3 kB
12. Stock Trading Project with Deep Reinforcement Learning/6. Code pt 2.srt 12.0 kB
12. Stock Trading Project with Deep Reinforcement Learning/10. Help! Why is the code slower on my machine.srt 12.0 kB
8. Recommender Systems/2. Recommender Systems with Deep Learning Code.srt 12.0 kB
2. Google Colab/4. Where can I learn about Numpy, Scipy, Matplotlib, Pandas, and Scikit-Learn.srt 11.8 kB
4. Feedforward Artificial Neural Networks/4. The Geometrical Picture.srt 11.8 kB
11. Deep Reinforcement Learning (Theory)/3. States, Actions, Rewards, Policies.srt 11.6 kB
13. Advanced Tensorflow Usage/4. Why is Google the King of Distributed Computing.srt 11.5 kB
5. Convolutional Neural Networks/9. Data Augmentation.srt 11.5 kB
6. Recurrent Neural Networks, Time Series, and Sequence Data/7. RNN for Time Series Prediction.srt 11.5 kB
15. In-Depth Loss Functions/1. Mean Squared Error.srt 11.5 kB
13. Advanced Tensorflow Usage/3. Tensorflow Lite (TFLite).srt 11.3 kB
4. Feedforward Artificial Neural Networks/6. Multiclass Classification.srt 11.2 kB
9. Transfer Learning for Computer Vision/1. Transfer Learning Theory.srt 10.9 kB
9. Transfer Learning for Computer Vision/6. Transfer Learning Code (pt 2).srt 10.7 kB
7. Natural Language Processing (NLP)/5. CNNs for Text.srt 10.3 kB
4. Feedforward Artificial Neural Networks/9. ANN for Image Classification.srt 10.2 kB
6. Recurrent Neural Networks, Time Series, and Sequence Data/8. Paying Attention to Shapes.srt 10.1 kB
7. Natural Language Processing (NLP)/4. Text Classification with LSTMs.srt 10.0 kB
16. In-Depth Gradient Descent/1. Gradient Descent.srt 10.0 kB
14. Low-Level Tensorflow/2. Constants and Basic Computation.srt 9.9 kB
15. In-Depth Loss Functions/3. Categorical Cross Entropy.srt 9.9 kB
6. Recurrent Neural Networks, Time Series, and Sequence Data/11. A More Challenging Sequence.srt 9.8 kB
2. Google Colab/2. Tensorflow 2.0 in Google Colab.srt 9.7 kB
3. Machine Learning and Neurons/3. Classification Notebook.srt 9.6 kB
3. Machine Learning and Neurons/4. Code Preparation (Regression Theory).srt 9.3 kB
11. Deep Reinforcement Learning (Theory)/7. What does it mean to “learn”.srt 9.1 kB
9. Transfer Learning for Computer Vision/3. Large Datasets and Data Generators.srt 9.0 kB
17. Extras/1. How to Choose Hyperparameters.srt 8.9 kB
12. Stock Trading Project with Deep Reinforcement Learning/4. Program Design and Layout.srt 8.8 kB
11. Deep Reinforcement Learning (Theory)/1. Deep Reinforcement Learning Section Introduction.srt 8.8 kB
13. Advanced Tensorflow Usage/5. Training with Distributed Strategies.srt 8.7 kB
12. Stock Trading Project with Deep Reinforcement Learning/8. Code pt 4.srt 8.6 kB
2. Google Colab/5. How to Succeed in this Course.srt 8.5 kB
17. Extras/3. Links to TF2.0 Notebooks.html 8.3 kB
5. Convolutional Neural Networks/3. What is Convolution (part 3).srt 8.2 kB
3. Machine Learning and Neurons/8. Making Predictions.srt 8.2 kB
5. Convolutional Neural Networks/7. CNN for Fashion MNIST.srt 8.2 kB
4. Feedforward Artificial Neural Networks/1. Artificial Neural Networks Section Introduction.srt 8.1 kB
21. Appendix FAQ Finale/2. BONUS Lecture.srt 8.1 kB
16. In-Depth Gradient Descent/3. Momentum.srt 8.0 kB
12. Stock Trading Project with Deep Reinforcement Learning/7. Code pt 3.srt 7.9 kB
13. Advanced Tensorflow Usage/1. What is a Web Service (Tensorflow Serving pt 1).srt 7.9 kB
11. Deep Reinforcement Learning (Theory)/14. How to Learn Reinforcement Learning.srt 7.8 kB
11. Deep Reinforcement Learning (Theory)/10. Epsilon-Greedy.srt 7.7 kB
9. Transfer Learning for Computer Vision/2. Some Pre-trained Models (VGG, ResNet, Inception, MobileNet).srt 7.5 kB
15. In-Depth Loss Functions/2. Binary Cross Entropy.srt 7.4 kB
5. Convolutional Neural Networks/2. What is Convolution (part 2).srt 7.4 kB
12. Stock Trading Project with Deep Reinforcement Learning/5. Code pt 1.srt 7.4 kB
6. Recurrent Neural Networks, Time Series, and Sequence Data/18. Other Ways to Forecast.srt 7.4 kB
6. Recurrent Neural Networks, Time Series, and Sequence Data/6. RNN Code Preparation.srt 7.3 kB
13. Advanced Tensorflow Usage/6. Using the TPU.srt 7.1 kB
12. Stock Trading Project with Deep Reinforcement Learning/3. Replay Buffer.srt 7.1 kB
12. Stock Trading Project with Deep Reinforcement Learning/1. Reinforcement Learning Stock Trader Introduction.srt 7.0 kB
7. Natural Language Processing (NLP)/6. Text Classification with CNNs.srt 6.8 kB
5. Convolutional Neural Networks/10. Batch Normalization.srt 6.7 kB
6. Recurrent Neural Networks, Time Series, and Sequence Data/16. Stock Return Predictions using LSTMs (pt 2).srt 6.7 kB
11. Deep Reinforcement Learning (Theory)/5. The Return.srt 6.4 kB
7. Natural Language Processing (NLP)/3. Text Preprocessing.srt 6.3 kB
6. Recurrent Neural Networks, Time Series, and Sequence Data/13. RNN for Image Classification (Theory).srt 6.1 kB
9. Transfer Learning for Computer Vision/4. 2 Approaches to Transfer Learning.srt 6.1 kB
3. Machine Learning and Neurons/10. Why Keras.srt 5.9 kB
1. Welcome/1. Introduction.srt 5.8 kB
17. Extras/2. Where Are The Exercises.srt 5.5 kB
16. In-Depth Gradient Descent/2. Stochastic Gradient Descent.srt 5.5 kB
5. Convolutional Neural Networks/8. CNN for CIFAR-10.srt 5.5 kB
3. Machine Learning and Neurons/9. Saving and Loading a Model.srt 5.0 kB
3. Machine Learning and Neurons/11. Suggestion Box.srt 4.9 kB
6. Recurrent Neural Networks, Time Series, and Sequence Data/4. Proof that the Linear Model Works.srt 4.7 kB
12. Stock Trading Project with Deep Reinforcement Learning/9. Reinforcement Learning Stock Trader Discussion.srt 4.5 kB
6. Recurrent Neural Networks, Time Series, and Sequence Data/14. RNN for Image Classification (Code).srt 4.3 kB
21. Appendix FAQ Finale/1. What is the Appendix.srt 3.8 kB
1. Welcome/3.1 Colab Notebooks.html 157 Bytes
0. Websites you may like/[FreeCourseSite.com].url 127 Bytes
0. Websites you may like/[CourseClub.Me].url 122 Bytes
1. Welcome/3.2 Github Link.html 120 Bytes
0. Websites you may like/[GigaCourse.Com].url 49 Bytes

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