[DesireCourse.Net] Udemy - Tensorflow 2.0 Deep Learning and Artificial Intelligence
File List
- 18. Appendix FAQ/2. Windows-Focused Environment Setup 2018.mp4 194.0 MB
- 18. Appendix FAQ/3. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4 166.7 MB
- 6. Recurrent Neural Networks, Time Series, and Sequence Data/12. Demo of the Long Distance Problem.mp4 143.1 MB
- 13. Advanced Tensorflow Usage/2. Tensorflow Serving pt 2.mp4 124.5 MB
- 18. Appendix FAQ/11. What order should I take your courses in (part 2).mp4 122.6 MB
- 18. Appendix FAQ/4. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4 117.1 MB
- 6. Recurrent Neural Networks, Time Series, and Sequence Data/1. Sequence Data.mp4 103.2 MB
- 11. Deep Reinforcement Learning (Theory)/2. Elements of a Reinforcement Learning Problem.mp4 97.8 MB
- 4. Feedforward Artificial Neural Networks/4. Activation Functions.mp4 92.2 MB
- 6. Recurrent Neural Networks, Time Series, and Sequence Data/5. Recurrent Neural Networks.mp4 92.0 MB
- 5. Convolutional Neural Networks/5. CNN Architecture.mp4 90.9 MB
- 2. Google Colab/3. Uploading your own data to Google Colab.mp4 89.1 MB
- 18. Appendix FAQ/10. What order should I take your courses in (part 1).mp4 88.1 MB
- 6. Recurrent Neural Networks, Time Series, and Sequence Data/3. Autoregressive Linear Model for Time Series Prediction.mp4 87.7 MB
- 6. Recurrent Neural Networks, Time Series, and Sequence Data/7. RNN for Time Series Prediction.mp4 87.2 MB
- 10. GANs (Generative Adversarial Networks)/1. GAN Theory.mp4 86.5 MB
- 5. Convolutional Neural Networks/11. Improving CIFAR-10 Results.mp4 86.4 MB
- 5. Convolutional Neural Networks/6. CNN Code Preparation.mp4 86.3 MB
- 4. Feedforward Artificial Neural Networks/9. ANN for Regression.mp4 84.0 MB
- 5. Convolutional Neural Networks/1. What is Convolution (part 1).mp4 83.6 MB
- 12. Stock Trading Project with Deep Reinforcement Learning/6. Code pt 2.mp4 83.4 MB
- 18. Appendix FAQ/5. How to Code Yourself (part 1).mp4 82.1 MB
- 4. Feedforward Artificial Neural Networks/6. How to Represent Images.mp4 80.9 MB
- 6. Recurrent Neural Networks, Time Series, and Sequence Data/15. Stock Return Predictions using LSTMs (pt 1).mp4 80.0 MB
- 10. GANs (Generative Adversarial Networks)/2. GAN Code.mp4 78.2 MB
- 18. Appendix FAQ/7. Proof that using Jupyter Notebook is the same as not using it.mp4 77.9 MB
- 6. Recurrent Neural Networks, Time Series, and Sequence Data/11. A More Challenging Sequence.mp4 77.7 MB
- 5. Convolutional Neural Networks/4. Convolution on Color Images.mp4 77.0 MB
- 6. Recurrent Neural Networks, Time Series, and Sequence Data/17. Stock Return Predictions using LSTMs (pt 3).mp4 76.7 MB
- 6. Recurrent Neural Networks, Time Series, and Sequence Data/9. GRU and LSTM (pt 1).mp4 76.1 MB
- 3. Machine Learning and Neurons/1. What is Machine Learning.mp4 73.2 MB
- 3. Machine Learning and Neurons/5. Regression Notebook.mp4 71.7 MB
- 14. Low-Level Tensorflow/3. Variables and Gradient Tape.mp4 70.6 MB
- 14. Low-Level Tensorflow/4. Build Your Own Custom Model.mp4 70.2 MB
- 8. Recommender Systems/1. Recommender Systems with Deep Learning Theory.mp4 68.7 MB
- 3. Machine Learning and Neurons/2. Code Preparation (Classification Theory).mp4 68.5 MB
- 9. Transfer Learning for Computer Vision/5. Transfer Learning Code (pt 1).mp4 66.6 MB
- 3. Machine Learning and Neurons/3. Classification Notebook.mp4 66.3 MB
- 2. Google Colab/1. Intro to Google Colab, how to use a GPU or TPU for free.mp4 65.2 MB
- 6. Recurrent Neural Networks, Time Series, and Sequence Data/8. Paying Attention to Shapes.mp4 64.3 MB
- 7. Natural Language Processing (NLP)/2. Code Preparation (NLP).mp4 62.9 MB
- 12. Stock Trading Project with Deep Reinforcement Learning/7. Code pt 3.mp4 62.3 MB
- 11. Deep Reinforcement Learning (Theory)/11. Q-Learning.mp4 61.3 MB
- 7. Natural Language Processing (NLP)/4. Text Classification with LSTMs.mp4 60.6 MB
- 12. Stock Trading Project with Deep Reinforcement Learning/8. Code pt 4.mp4 59.2 MB
- 8. Recommender Systems/2. Recommender Systems with Deep Learning Code.mp4 58.8 MB
- 4. Feedforward Artificial Neural Networks/8. ANN for Image Classification.mp4 58.4 MB
- 7. Natural Language Processing (NLP)/1. Embeddings.mp4 58.0 MB
- 18. Appendix FAQ/6. How to Code Yourself (part 2).mp4 56.4 MB
- 4. Feedforward Artificial Neural Networks/7. Code Preparation (ANN).mp4 56.2 MB
- 12. Stock Trading Project with Deep Reinforcement Learning/2. Data and Environment.mp4 56.0 MB
- 11. Deep Reinforcement Learning (Theory)/12. Deep Q-Learning DQN (pt 1).mp4 55.7 MB
- 9. Transfer Learning for Computer Vision/1. Transfer Learning Theory.mp4 55.2 MB
- 3. Machine Learning and Neurons/7. How does a model learn.mp4 55.0 MB
- 4. Feedforward Artificial Neural Networks/3. The Geometrical Picture.mp4 54.8 MB
- 6. Recurrent Neural Networks, Time Series, and Sequence Data/10. GRU and LSTM (pt 2).mp4 53.6 MB
- 11. Deep Reinforcement Learning (Theory)/9. Solving the Bellman Equation with Reinforcement Learning (pt 2).mp4 52.5 MB
- 5. Convolutional Neural Networks/7. CNN for Fashion MNIST.mp4 51.6 MB
- 2. Google Colab/2. Tensorflow 2.0 in Google Colab.mp4 51.1 MB
- 13. Advanced Tensorflow Usage/4. Why is Google the King of Distributed Computing.mp4 50.8 MB
- 14. Low-Level Tensorflow/2. Constants and Basic Computation.mp4 50.2 MB
- 13. Advanced Tensorflow Usage/5. Training with Distributed Strategies.mp4 50.1 MB
- 3. Machine Learning and Neurons/6. The Neuron.mp4 49.4 MB
- 4. Feedforward Artificial Neural Networks/2. Forward Propagation.mp4 49.3 MB
- 11. Deep Reinforcement Learning (Theory)/13. Deep Q-Learning DQN (pt 2).mp4 49.2 MB
- 11. Deep Reinforcement Learning (Theory)/4. Markov Decision Processes (MDPs).mp4 48.9 MB
- 6. Recurrent Neural Networks, Time Series, and Sequence Data/2. Forecasting.mp4 47.2 MB
- 4. Feedforward Artificial Neural Networks/5. Multiclass Classification.mp4 46.9 MB
- 12. Stock Trading Project with Deep Reinforcement Learning/5. Code pt 1.mp4 46.8 MB
- 7. Natural Language Processing (NLP)/6. Text Classification with CNNs.mp4 46.4 MB
- 9. Transfer Learning for Computer Vision/6. Transfer Learning Code (pt 2).mp4 46.1 MB
- 18. Appendix FAQ/9. Is Theano Dead.mp4 44.4 MB
- 2. Google Colab/4. Where can I learn about Numpy, Scipy, Matplotlib, Pandas, and Scikit-Learn.mp4 43.8 MB
- 11. Deep Reinforcement Learning (Theory)/6. Value Functions and the Bellman Equation.mp4 43.3 MB
- 11. Deep Reinforcement Learning (Theory)/3. States, Actions, Rewards, Policies.mp4 43.0 MB
- 16. In-Depth Gradient Descent/5. Adam.mp4 42.6 MB
- 14. Low-Level Tensorflow/1. Differences Between Tensorflow 1.x and Tensorflow 2.x.mp4 42.5 MB
- 13. Advanced Tensorflow Usage/3. Tensorflow Lite (TFLite).mp4 42.4 MB
- 3. Machine Learning and Neurons/8. Making Predictions.mp4 42.0 MB
- 7. Natural Language Processing (NLP)/5. CNNs for Text.mp4 40.9 MB
- 16. In-Depth Gradient Descent/3. Momentum.mp4 39.4 MB
- 5. Convolutional Neural Networks/9. Data Augmentation.mp4 39.2 MB
- 1. Welcome/1. Introduction.mp4 39.2 MB
- 11. Deep Reinforcement Learning (Theory)/8. Solving the Bellman Equation with Reinforcement Learning (pt 1).mp4 39.0 MB
- 18. Appendix FAQ/8. How to Succeed in this Course (Long Version).mp4 38.9 MB
- 16. In-Depth Gradient Descent/4. Variable and Adaptive Learning Rates.mp4 38.5 MB
- 6. Recurrent Neural Networks, Time Series, and Sequence Data/16. Stock Return Predictions using LSTMs (pt 2).mp4 38.2 MB
- 11. Deep Reinforcement Learning (Theory)/1. Deep Reinforcement Learning Section Introduction.mp4 37.8 MB
- 11. Deep Reinforcement Learning (Theory)/10. Epsilon-Greedy.mp4 37.6 MB
- 11. Deep Reinforcement Learning (Theory)/14. How to Learn Reinforcement Learning.mp4 37.5 MB
- 15. In-Depth Loss Functions/1. Mean Squared Error.mp4 37.3 MB
- 9. Transfer Learning for Computer Vision/3. Large Datasets and Data Generators.mp4 36.6 MB
- 7. Natural Language Processing (NLP)/3. Text Preprocessing.mp4 36.1 MB
- 16. In-Depth Gradient Descent/1. Gradient Descent.mp4 35.5 MB
- 15. In-Depth Loss Functions/3. Categorical Cross Entropy.mp4 35.4 MB
- 3. Machine Learning and Neurons/9. Saving and Loading a Model.mp4 35.3 MB
- 5. Convolutional Neural Networks/8. CNN for CIFAR-10.mp4 34.8 MB
- 4. Feedforward Artificial Neural Networks/1. Artificial Neural Networks Section Introduction.mp4 32.5 MB
- 13. Advanced Tensorflow Usage/1. What is a Web Service (Tensorflow Serving pt 1).mp4 31.6 MB
- 9. Transfer Learning for Computer Vision/2. Some Pre-trained Models (VGG, ResNet, Inception, MobileNet).mp4 31.5 MB
- 6. Recurrent Neural Networks, Time Series, and Sequence Data/13. RNN for Image Classification (Theory).mp4 31.5 MB
- 3. Machine Learning and Neurons/4. Code Preparation (Regression Theory).mp4 31.3 MB
- 1. Welcome/2. Outline.mp4 30.8 MB
- 1. Welcome/3. Where to get the code.mp4 30.5 MB
- 11. Deep Reinforcement Learning (Theory)/7. What does it mean to “learn”.mp4 30.3 MB
- 12. Stock Trading Project with Deep Reinforcement Learning/4. Program Design and Layout.mp4 29.8 MB
- 12. Stock Trading Project with Deep Reinforcement Learning/1. Reinforcement Learning Stock Trader Introduction.mp4 29.7 MB
- 5. Convolutional Neural Networks/3. What is Convolution (part 3).mp4 28.9 MB
- 6. Recurrent Neural Networks, Time Series, and Sequence Data/14. RNN for Image Classification (Code).mp4 27.4 MB
- 5. Convolutional Neural Networks/2. What is Convolution (part 2).mp4 25.2 MB
- 16. In-Depth Gradient Descent/2. Stochastic Gradient Descent.mp4 25.0 MB
- 12. Stock Trading Project with Deep Reinforcement Learning/3. Replay Buffer.mp4 24.1 MB
- 5. Convolutional Neural Networks/10. Batch Normalization.mp4 23.4 MB
- 15. In-Depth Loss Functions/2. Binary Cross Entropy.mp4 21.5 MB
- 11. Deep Reinforcement Learning (Theory)/5. The Return.mp4 20.9 MB
- 9. Transfer Learning for Computer Vision/4. 2 Approaches to Transfer Learning.mp4 20.6 MB
- 6. Recurrent Neural Networks, Time Series, and Sequence Data/6. RNN Code Preparation.mp4 20.4 MB
- 6. Recurrent Neural Networks, Time Series, and Sequence Data/4. Proof that the Linear Model Works.mp4 18.3 MB
- 12. Stock Trading Project with Deep Reinforcement Learning/9. Reinforcement Learning Stock Trader Discussion.mp4 18.2 MB
- 18. Appendix FAQ/1. What is the Appendix.mp4 18.0 MB
- 18. Appendix FAQ/12. Bonus Where to get discount coupons and FREE deep learning material.mp4 13.3 MB
- 18. Appendix FAQ/4. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.vtt 27.7 KB
- 5. Convolutional Neural Networks/5. CNN Architecture.vtt 24.4 KB
- 11. Deep Reinforcement Learning (Theory)/2. Elements of a Reinforcement Learning Problem.vtt 22.9 KB
- 6. Recurrent Neural Networks, Time Series, and Sequence Data/5. Recurrent Neural Networks.vtt 22.3 KB
- 6. Recurrent Neural Networks, Time Series, and Sequence Data/1. Sequence Data.vtt 20.9 KB
- 18. Appendix FAQ/11. What order should I take your courses in (part 2).vtt 20.2 KB
- 6. Recurrent Neural Networks, Time Series, and Sequence Data/12. Demo of the Long Distance Problem.vtt 20.1 KB
- 4. Feedforward Artificial Neural Networks/4. Activation Functions.vtt 19.8 KB
- 18. Appendix FAQ/5. How to Code Yourself (part 1).vtt 19.4 KB
- 6. Recurrent Neural Networks, Time Series, and Sequence Data/9. GRU and LSTM (pt 1).vtt 18.4 KB
- 10. GANs (Generative Adversarial Networks)/1. GAN Theory.vtt 18.1 KB
- 5. Convolutional Neural Networks/4. Convolution on Color Images.vtt 18.0 KB
- 13. Advanced Tensorflow Usage/2. Tensorflow Serving pt 2.vtt 17.8 KB
- 3. Machine Learning and Neurons/2. Code Preparation (Classification Theory).vtt 17.8 KB
- 5. Convolutional Neural Networks/1. What is Convolution (part 1).vtt 17.6 KB
- 18. Appendix FAQ/2. Windows-Focused Environment Setup 2018.vtt 17.3 KB
- 5. Convolutional Neural Networks/6. CNN Code Preparation.vtt 17.2 KB
- 3. Machine Learning and Neurons/1. What is Machine Learning.vtt 16.2 KB
- 11. Deep Reinforcement Learning (Theory)/11. Q-Learning.vtt 15.6 KB
- 8. Recommender Systems/1. Recommender Systems with Deep Learning Theory.vtt 15.2 KB
- 7. Natural Language Processing (NLP)/2. Code Preparation (NLP).vtt 14.7 KB
- 11. Deep Reinforcement Learning (Theory)/12. Deep Q-Learning DQN (pt 1).vtt 14.4 KB
- 4. Feedforward Artificial Neural Networks/7. Code Preparation (ANN).vtt 14.3 KB
- 18. Appendix FAQ/10. What order should I take your courses in (part 1).vtt 14.2 KB
- 7. Natural Language Processing (NLP)/1. Embeddings.vtt 14.2 KB
- 12. Stock Trading Project with Deep Reinforcement Learning/2. Data and Environment.vtt 13.8 KB
- 4. Feedforward Artificial Neural Networks/6. How to Represent Images.vtt 13.7 KB
- 6. Recurrent Neural Networks, Time Series, and Sequence Data/15. Stock Return Predictions using LSTMs (pt 1).vtt 13.7 KB
- 16. In-Depth Gradient Descent/4. Variable and Adaptive Learning Rates.vtt 13.3 KB
- 11. Deep Reinforcement Learning (Theory)/9. Solving the Bellman Equation with Reinforcement Learning (pt 2).vtt 13.0 KB
- 10. GANs (Generative Adversarial Networks)/2. GAN Code.vtt 13.0 KB
- 18. Appendix FAQ/8. How to Succeed in this Course (Long Version).vtt 12.8 KB
- 6. Recurrent Neural Networks, Time Series, and Sequence Data/10. GRU and LSTM (pt 2).vtt 12.7 KB
- 6. Recurrent Neural Networks, Time Series, and Sequence Data/17. Stock Return Predictions using LSTMs (pt 3).vtt 12.6 KB
- 18. Appendix FAQ/3. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.vtt 12.6 KB
- 6. Recurrent Neural Networks, Time Series, and Sequence Data/3. Autoregressive Linear Model for Time Series Prediction.vtt 12.4 KB
- 2. Google Colab/1. Intro to Google Colab, how to use a GPU or TPU for free.vtt 12.4 KB
- 18. Appendix FAQ/7. Proof that using Jupyter Notebook is the same as not using it.vtt 12.3 KB
- 3. Machine Learning and Neurons/7. How does a model learn.vtt 12.3 KB
- 9. Transfer Learning for Computer Vision/5. Transfer Learning Code (pt 1).vtt 12.1 KB
- 16. In-Depth Gradient Descent/5. Adam.vtt 11.9 KB
- 14. Low-Level Tensorflow/3. Variables and Gradient Tape.vtt 11.7 KB
- 14. Low-Level Tensorflow/4. Build Your Own Custom Model.vtt 11.6 KB
- 11. Deep Reinforcement Learning (Theory)/13. Deep Q-Learning DQN (pt 2).vtt 11.6 KB
- 5. Convolutional Neural Networks/11. Improving CIFAR-10 Results.vtt 11.5 KB
- 18. Appendix FAQ/6. How to Code Yourself (part 2).vtt 11.4 KB
- 4. Feedforward Artificial Neural Networks/9. ANN for Regression.vtt 11.2 KB
- 11. Deep Reinforcement Learning (Theory)/4. Markov Decision Processes (MDPs).vtt 11.2 KB
- 18. Appendix FAQ/9. Is Theano Dead.vtt 11.1 KB
- 6. Recurrent Neural Networks, Time Series, and Sequence Data/2. Forecasting.vtt 11.1 KB
- 11. Deep Reinforcement Learning (Theory)/8. Solving the Bellman Equation with Reinforcement Learning (pt 1).vtt 11.0 KB
- 3. Machine Learning and Neurons/6. The Neuron.vtt 10.9 KB
- 11. Deep Reinforcement Learning (Theory)/6. Value Functions and the Bellman Equation.vtt 10.9 KB
- 14. Low-Level Tensorflow/1. Differences Between Tensorflow 1.x and Tensorflow 2.x.vtt 10.8 KB
- 4. Feedforward Artificial Neural Networks/2. Forward Propagation.vtt 10.7 KB
- 3. Machine Learning and Neurons/5. Regression Notebook.vtt 10.7 KB
- 2. Google Colab/3. Uploading your own data to Google Colab.vtt 10.5 KB
- 12. Stock Trading Project with Deep Reinforcement Learning/6. Code pt 2.vtt 10.3 KB
- 8. Recommender Systems/2. Recommender Systems with Deep Learning Code.vtt 10.3 KB
- 4. Feedforward Artificial Neural Networks/3. The Geometrical Picture.vtt 10.2 KB
- 2. Google Colab/4. Where can I learn about Numpy, Scipy, Matplotlib, Pandas, and Scikit-Learn.vtt 10.1 KB
- 11. Deep Reinforcement Learning (Theory)/3. States, Actions, Rewards, Policies.vtt 10.0 KB
- 13. Advanced Tensorflow Usage/4. Why is Google the King of Distributed Computing.vtt 9.9 KB
- 15. In-Depth Loss Functions/1. Mean Squared Error.vtt 9.9 KB
- 5. Convolutional Neural Networks/9. Data Augmentation.vtt 9.8 KB
- 13. Advanced Tensorflow Usage/3. Tensorflow Lite (TFLite).vtt 9.7 KB
- 4. Feedforward Artificial Neural Networks/5. Multiclass Classification.vtt 9.7 KB
- 6. Recurrent Neural Networks, Time Series, and Sequence Data/7. RNN for Time Series Prediction.vtt 9.7 KB
- 9. Transfer Learning for Computer Vision/1. Transfer Learning Theory.vtt 9.4 KB
- 9. Transfer Learning for Computer Vision/6. Transfer Learning Code (pt 2).vtt 9.1 KB
- 4. Feedforward Artificial Neural Networks/8. ANN for Image Classification.vtt 8.7 KB
- 6. Recurrent Neural Networks, Time Series, and Sequence Data/8. Paying Attention to Shapes.vtt 8.6 KB
- 16. In-Depth Gradient Descent/1. Gradient Descent.vtt 8.6 KB
- 7. Natural Language Processing (NLP)/4. Text Classification with LSTMs.vtt 8.6 KB
- 15. In-Depth Loss Functions/3. Categorical Cross Entropy.vtt 8.4 KB
- 7. Natural Language Processing (NLP)/5. CNNs for Text.vtt 8.4 KB
- 6. Recurrent Neural Networks, Time Series, and Sequence Data/11. A More Challenging Sequence.vtt 8.4 KB
- 2. Google Colab/2. Tensorflow 2.0 in Google Colab.vtt 8.3 KB
- 14. Low-Level Tensorflow/2. Constants and Basic Computation.vtt 8.3 KB
- 3. Machine Learning and Neurons/3. Classification Notebook.vtt 8.2 KB
- 3. Machine Learning and Neurons/4. Code Preparation (Regression Theory).vtt 7.9 KB
- 17. Extras/1. Links to TF2.0 Notebooks.html 7.8 KB
- 11. Deep Reinforcement Learning (Theory)/7. What does it mean to “learn”.vtt 7.8 KB
- 9. Transfer Learning for Computer Vision/3. Large Datasets and Data Generators.vtt 7.7 KB
- 12. Stock Trading Project with Deep Reinforcement Learning/4. Program Design and Layout.vtt 7.6 KB
- 13. Advanced Tensorflow Usage/5. Training with Distributed Strategies.vtt 7.5 KB
- 11. Deep Reinforcement Learning (Theory)/1. Deep Reinforcement Learning Section Introduction.vtt 7.5 KB
- 12. Stock Trading Project with Deep Reinforcement Learning/8. Code pt 4.vtt 7.2 KB
- 5. Convolutional Neural Networks/3. What is Convolution (part 3).vtt 7.0 KB
- 3. Machine Learning and Neurons/8. Making Predictions.vtt 7.0 KB
- 5. Convolutional Neural Networks/7. CNN for Fashion MNIST.vtt 7.0 KB
- 4. Feedforward Artificial Neural Networks/1. Artificial Neural Networks Section Introduction.vtt 6.9 KB
- 16. In-Depth Gradient Descent/3. Momentum.vtt 6.9 KB
- 12. Stock Trading Project with Deep Reinforcement Learning/7. Code pt 3.vtt 6.8 KB
- 13. Advanced Tensorflow Usage/1. What is a Web Service (Tensorflow Serving pt 1).vtt 6.8 KB
- 11. Deep Reinforcement Learning (Theory)/14. How to Learn Reinforcement Learning.vtt 6.7 KB
- 1. Welcome/3. Where to get the code.vtt 6.6 KB
- 11. Deep Reinforcement Learning (Theory)/10. Epsilon-Greedy.vtt 6.5 KB
- 9. Transfer Learning for Computer Vision/2. Some Pre-trained Models (VGG, ResNet, Inception, MobileNet).vtt 6.4 KB
- 1. Welcome/2. Outline.vtt 6.4 KB
- 15. In-Depth Loss Functions/2. Binary Cross Entropy.vtt 6.4 KB
- 5. Convolutional Neural Networks/2. What is Convolution (part 2).vtt 6.4 KB
- 12. Stock Trading Project with Deep Reinforcement Learning/5. Code pt 1.vtt 6.3 KB
- 6. Recurrent Neural Networks, Time Series, and Sequence Data/6. RNN Code Preparation.vtt 6.3 KB
- 12. Stock Trading Project with Deep Reinforcement Learning/3. Replay Buffer.vtt 6.1 KB
- 12. Stock Trading Project with Deep Reinforcement Learning/1. Reinforcement Learning Stock Trader Introduction.vtt 6.0 KB
- 5. Convolutional Neural Networks/10. Batch Normalization.vtt 5.8 KB
- 7. Natural Language Processing (NLP)/6. Text Classification with CNNs.vtt 5.8 KB
- 6. Recurrent Neural Networks, Time Series, and Sequence Data/16. Stock Return Predictions using LSTMs (pt 2).vtt 5.7 KB
- 11. Deep Reinforcement Learning (Theory)/5. The Return.vtt 5.5 KB
- 7. Natural Language Processing (NLP)/3. Text Preprocessing.vtt 5.4 KB
- 9. Transfer Learning for Computer Vision/4. 2 Approaches to Transfer Learning.vtt 5.2 KB
- 6. Recurrent Neural Networks, Time Series, and Sequence Data/13. RNN for Image Classification (Theory).vtt 5.2 KB
- 1. Welcome/1. Introduction.vtt 5.0 KB
- 16. In-Depth Gradient Descent/2. Stochastic Gradient Descent.vtt 4.8 KB
- 5. Convolutional Neural Networks/8. CNN for CIFAR-10.vtt 4.7 KB
- 3. Machine Learning and Neurons/9. Saving and Loading a Model.vtt 4.3 KB
- 6. Recurrent Neural Networks, Time Series, and Sequence Data/4. Proof that the Linear Model Works.vtt 4.0 KB
- 12. Stock Trading Project with Deep Reinforcement Learning/9. Reinforcement Learning Stock Trader Discussion.vtt 3.9 KB
- 6. Recurrent Neural Networks, Time Series, and Sequence Data/14. RNN for Image Classification (Code).vtt 3.7 KB
- 18. Appendix FAQ/1. What is the Appendix.vtt 3.3 KB
- 18. Appendix FAQ/12. Bonus Where to get discount coupons and FREE deep learning material.vtt 2.9 KB
- 13. Advanced Tensorflow Usage/6. Using the TPU.html 1.6 KB
- [DesireCourse.Net].url 51 bytes
- [CourseClub.Me].url 48 bytes
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