Udemy - Tensorflow 2.0: Deep Learning and Artificial Intelligence...

Files:

• 1. Introduction.mp4 (34.8 MB)
• 1. Introduction.srt (5.7 KB)
• 2. Outline.mp4 (73.7 MB)
• 2. Outline.srt (17.1 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 (0.2 KB)
• 3.2 Github Link.html (0.1 KB)
• [Tutorialsplanet.NET].url (0.1 KB)

• 1. GAN Theory.mp4 (87.2 MB)
• 1. GAN Theory.srt (20.7 KB)
• 2. GAN Code.mp4 (78.3 MB)
• 2. GAN Code.srt (14.9 KB)

• 1. Deep Reinforcement Learning Section Introduction.mp4 (38.1 MB)
• 1. Deep Reinforcement Learning Section Introduction.srt (8.6 KB)
• 10. Epsilon-Greedy.mp4 (40.1 MB)
• 10. Epsilon-Greedy.srt (7.5 KB)
• 11. Q-Learning.mp4 (61.8 MB)
• 11. Q-Learning.srt (17.9 KB)
• 12. Deep Q-Learning DQN (pt 1).mp4 (56.3 MB)
• 12. Deep Q-Learning DQN (pt 1).srt (16.4 KB)
• 13. Deep Q-Learning DQN (pt 2).mp4 (49.6 MB)
• 13. Deep Q-Learning DQN (pt 2).srt (13.2 KB)
• 14. How to Learn Reinforcement Learning.mp4 (37.7 MB)
• 14. How to Learn Reinforcement Learning.srt (7.6 KB)
• 2. Elements of a Reinforcement Learning Problem.mp4 (98.6 MB)
• 2. Elements of a Reinforcement Learning Problem.srt (26.2 KB)
• 3. States, Actions, Rewards, Policies.mp4 (43.3 MB)
• 3. States, Actions, Rewards, Policies.srt (11.3 KB)
• 4. Markov Decision Processes (MDPs).mp4 (49.3 MB)
• 4. Markov Decision Processes (MDPs).srt (12.7 KB)
• 5. The Return.mp4 (21.1 MB)
• 5. The Return.srt (6.3 KB)
• 6. Value Functions and the Bellman Equation.mp4 (43.6 MB)
• 6. Value Functions and the Bellman Equation.srt (12.5 KB)
• 7. What does it mean to “learn”.mp4 (31.7 MB)
• 7. What does it mean to “learn”.srt (8.9 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. 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)

• 1. Reinforcement Learning Stock Trader Introduction.mp4 (26.0 MB)
• 1. Reinforcement Learning Stock Trader Introduction.srt (6.8 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)
• 2. Data and Environment.mp4 (51.0 MB)
• 2. Data and Environment.srt (15.7 KB)
• 3. Replay Buffer.mp4 (24.0 MB)
• 3. Replay Buffer.srt (6.9 KB)
• 4. Program Design and Layout.mp4 (26.0 MB)
• 4. Program Design and Layout.srt (8.6 KB)
• 5. Code pt 1.mp4 (39.5 MB)
• 5. Code pt 1.srt (7.2 KB)
• 6. Code pt 2.mp4 (68.0 MB)
• 6. Code pt 2.srt (11.8 KB)
• 7. Code pt 3.mp4 (52.0 MB)
• 7. Code pt 3.srt (7.8 KB)
• 8. Code pt 4.mp4 (52.5 MB)
• 8. Code pt 4.srt (8.4 KB)
• 9. Reinforcement Learning Stock Trader Discussion.mp4 (16.6 MB)
• 9. Reinforcement Learning Stock Trader Discussion.srt (4.4 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)
• 2. Tensorflow Serving pt 2.mp4 (105.0 MB)
• 2. Tensorflow Serving pt 2.srt (20.4 KB)
• 3. Tensorflow Lite (TFLite).mp4 (42.6 MB)
• 3. Tensorflow Lite (TFLite).srt (11.0 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. Training with Distributed Strategies.mp4 (43.5 MB)
• 5. Training with Distributed Strategies.srt (8.5 KB)
• 6. Using the TPU.mp4 (45.2 MB)
• 6. Using the TPU.srt (7.0 KB)
• [Tutorialsplanet.NET].url (0.1 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)
• 2. Constants and Basic Computation.mp4 (40.3 MB)
• 2. Constants and Basic Computation.srt (9.6 KB)
• 3. Variables and Gradient Tape.mp4 (56.0 MB)
• 3. Variables and Gradient Tape.srt (13.6 KB)
• 4. Build Your Own Custom Model.mp4 (58.5 MB)
• 4. Build Your Own Custom Model.srt (13.3 KB)

• 1. Mean Squared Error.mp4 (33.8 MB)
• 1. Mean Squared Error.srt (11.2 KB)
• 2. Binary Cross Entropy.mp4 (23.7 MB)
• 2. Binary Cross Entropy.srt (7.3 KB)
• 3. Categorical Cross Entropy.mp4 (31.7 MB)
• 3. Categorical Cross Entropy.srt (9.6 KB)

• 1. Gradient Descent.mp4 (34.9 MB)
• 1. Gradient Descent.srt (9.8 KB)
• 2. Stochastic Gradient Descent.mp4 (23.0 MB)
• 2. Stochastic Gradient Descent.srt (5.4 KB)
• 3. Momentum.mp4 (34.3 MB)
• 3. Momentum.srt (7.8 KB)
• 4. Variable and Adaptive Learning Rates.mp4 (34.9 MB)
• 4. Variable and Adaptive Learning Rates.srt (15.2 KB)
• 5. Adam (pt 1).mp4 (55.1 MB)
• 5. Adam (pt 1).srt (16.7 KB)
• 6. Adam (pt 2).mp4 (52.8 MB)
• 6. Adam (pt 2).srt (14.5 KB)

• 1. How to Choose Hyperparameters.mp4 (37.9 MB)
• 1. How to Choose Hyperparameters.srt (8.7 KB)
• 2. Where Are The Exercises.mp4 (26.0 MB)
• 2. Where Are The Exercises.srt (5.4 KB)
• 3. Links to TF2.0 Notebooks.html (8.1 KB)
• [Tutorialsplanet.NET].url (0.1 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)
• 2. Anaconda Environment Setup.mp4 (180.9 MB)
• 2. Anaconda Environment Setup.srt (20.0 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 (32.0 KB)

• 1. Beginner's Coding Tips.mp4 (75.7 MB)
• 1. Beginner's Coding Tips.srt (19.0 KB)
• 2. How to Code Yourself (part 1).mp4 (71.8 MB)
• 2. How to Code Yourself (part 1).srt (22.1 KB)
• 3. How to Code Yourself (part 2).mp4 (49.1 MB)
• 3. How to Code Yourself (part 2).srt (13.0 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)
• 5. Is Theano Dead.mp4 (40.8 MB)
• 5. Is Theano Dead.srt (12.6 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)
• 2. Tensorflow 2.0 in Google Colab.mp4 (40.7 MB)
• 2. Tensorflow 2.0 in Google Colab.srt (9.5 KB)
• 3. Uploading your own data to Google Colab.mp4 (73.6 MB)
• 3. Uploading your own data to Google Colab.srt (12.0 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)
• 5. How to Succeed in this Course.mp4 (43.8 MB)
• 5. How to Succeed in this Course.srt (8.3 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)
• 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)
• 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)
• 4. Machine Learning and AI Prerequisite Roadmap (pt 2).mp4 (108.2 MB)
• 4. Machine Learning and AI Prerequisite Roadmap (pt 2).srt (23.0 KB)
• [Tutorialsplanet.NET].url (0.1 KB)

• 1. What is the Appendix.mp4 (16.4 MB)
• 1. What is the Appendix.srt (3.7 KB)
• 2. BONUS Lecture.mp4 (37.8 MB)
• 2. BONUS Lecture.srt (7.9 KB)

• 1. What is Machine Learning.mp4 (65.5 MB)
• 1. What is Machine Learning.srt (18.4 KB)
• 10. Why Keras.mp4 (26.5 MB)
• 10. Why Keras.srt (5.8 KB)
• 11. Suggestion Box.mp4 (27.1 MB)
• 11. Suggestion Box.srt (4.7 KB)
• 2. Code Preparation (Classification Theory).mp4 (59.8 MB)
• 2. Code Preparation (Classification Theory).srt (20.3 KB)
• 3. Classification Notebook.mp4 (54.5 MB)
• 3. Classification Notebook.srt (9.4 KB)
• 4. Code Preparation (Regression Theory).mp4 (27.3 MB)
• 4. Code Preparation (Regression Theory).srt (9.1 KB)
• 5. Regression Notebook.mp4 (57.5 MB)
• 5. Regression Notebook.srt (12.1 KB)
• 6. The Neuron.mp4 (42.6 MB)
• 6. The Neuron.srt (12.5 KB)
• 7. How does a model learn.mp4 (48.0 MB)
• 7. How does a model learn.srt (14.0 KB)
• 8. Making Predictions.mp4 (33.9 MB)
• 8. Making Predictions.srt (8.0 KB)
• 9. Saving and Loading a Model.mp4 (29.7 MB)
• 9. Saving and Loading a Model.srt (4.9 KB)

• 1. Artificial Neural Networks Section Introduction.mp4 (29.8 MB)
• 1. Artificial Neural Networks Section Introduction.srt (7.9 KB)
• 10. ANN for Regression.mp4 (69.3 MB)
• 10. ANN for Regression.srt (12.8 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 (17.0 KB)
• 3. Forward Propagation.mp4 (46.7 MB)
• 3. Forward Propagation.srt (12.2 KB)
• 4. The Geometrical Picture.mp4 (56.4 MB)
• 4. The Geometrical Picture.srt (11.5 KB)
• 5. Activation Functions.mp4 (80.5 MB)
• 5. Activation Functions.srt (22.6 KB)
• 6. Multiclass Classification.mp4 (41.4 MB)
• 6. Multiclass Classification.srt (11.0 KB)
• 7. How to Represent Images.mp4 (70.5 MB)
• 7. How to Represent Images.srt (15.6 KB)
• 8. Code Preparation (ANN).mp4 (50.9 MB)
• 8. Code Preparation (ANN).srt (16.3 KB)
• 9. ANN for Image Classification.mp4 (47.7 MB)
• 9. ANN for Image Classification.srt (9.9 KB)

• 1. What is Convolution (part 1).mp4 (79.8 MB)
• 1. What is Convolution (part 1).srt (20.2 KB)
• 10. Batch Normalization.mp4 (21.1 MB)
• 10. Batch Normalization.srt (6.5 KB)
• 11. Improving CIFAR-10 Results.mp4 (72.9 MB)
• 11. Improving CIFAR-10 Results.srt (13.2 KB)
• 2. What is Convolution (part 2).mp4 (22.3 MB)
• 2. What is Convolution (part 2).srt (7.2 KB)
• 3. What is Convolution (part 3).mp4 (27.6 MB)
• 3. What is Convolution (part 3).srt (8.0 KB)
• 4. Convolution on Color Images.mp4 (69.4 MB)
• 4. Convolution on Color Images.srt (20.6 KB)
• 5. CNN Architecture.mp4 (80.6 MB)
• 5. CNN Architecture.srt (27.9 KB)
• 6. CNN Code Preparation.mp4 (76.9 MB)
• 6. CNN Code Preparation.srt (19.6 KB)
• 7. CNN for Fashion MNIST.mp4 (42.8 MB)
• 7. CNN for Fashion MNIST.srt (8.0 KB)
• 8. CNN for CIFAR-10.mp4 (29.7 MB)
• 8. CNN for CIFAR-10.srt (5.4 KB)
• 9. Data Augmentation.mp4 (35.0 MB)
• 9. Data Augmentation.srt (11.2 KB)
• [Tutorialsplanet.NET].url (0.1 KB)

• 1. Sequence Data.mp4 (90.1 MB)
• 1. Sequence Data.srt (24.0 KB)
• 10. GRU and LSTM (pt 2).mp4 (50.4 MB)
• 10. GRU and LSTM (pt 2).srt (14.3 KB)
• 11. A More Challenging Sequence.mp4 (64.6 MB)
• 11. A More Challenging Sequence.srt (9.6 KB)
• 12. Demo of the Long Distance Problem.mp4 (124.0 MB)
• 12. Demo of the Long Distance Problem.srt (23.1 KB)
• 13. RNN for Image Classification (Theory).mp4 (29.1 MB)
• 13. RNN for Image Classification (Theory).srt (6.0 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 (33.0 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. Forecasting.mp4 (46.8 MB)
• 2. Forecasting.srt (13.3 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)
• 4. Proof that the Linear Model Works.mp4 (16.2 MB)
• 4. Proof that the Linear Model Works.srt (4.6 KB)
• 5. Recurrent Neural Networks.mp4 (83.0 MB)
• 5. Recurrent Neural Networks.srt (25.6 KB)
• 6. RNN Code Preparation.mp4 (18.4 MB)
• 6. RNN Code Preparation.srt (7.1 KB)
• 7. RNN for Time Series Prediction.mp4 (74.1 MB)
• 7. RNN for Time Series Prediction.srt (11.2 KB)
• 8. Paying Attention to Shapes.mp4 (52.5 MB)
• 8. Paying Attention to Shapes.srt (9.9 KB)
• 9. GRU and LSTM (pt 1).mp4 (79.9 MB)
• 9. GRU and LSTM (pt 1).srt (22.8 KB)

• 1. Embeddings.mp4 (52.6 MB)
• 1. Embeddings.srt (16.2 KB)
• 2. Code Preparation (NLP).mp4 (57.0 MB)
• 2. Code Preparation (NLP).srt (16.8 KB)
• 3. Text Preprocessing.mp4 (28.8 MB)
• 3. Text Preprocessing.srt (6.2 KB)
• 4. Text Classification with LSTMs.mp4 (50.7 MB)
• 4. Text Classification with LSTMs.srt (9.8 KB)
• 5. CNNs for Text.mp4 (40.4 MB)
• 5. CNNs for Text.srt (10.1 KB)
• 6. Text Classification with CNNs.mp4 (39.6 MB)
• 6. Text Classification with CNNs.srt (6.6 KB)

• 1. Recommender Systems with Deep Learning Theory.mp4 (68.7 MB)
• 1. Recommender Systems with Deep Learning Theory.srt (17.4 KB)
• 2. Recommender Systems with Deep Learning Code.mp4 (58.8 MB)
• 2. Recommender Systems with Deep Learning Code.srt (11.7 KB)

• 1. Transfer Learning Theory.mp4 (55.1 MB)
• 1. Transfer Learning Theory.srt (10.7 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)
• 3. Large Datasets and Data Generators.mp4 (36.6 MB)
• 3. Large Datasets and Data Generators.srt (8.8 KB)
• 4. 2 Approaches to Transfer Learning.mp4 (20.6 MB)
• 4. 2 Approaches to Transfer Learning.srt (6.0 KB)
• 5. Transfer Learning Code (pt 1).mp4 (66.5 MB)
• 5. Transfer Learning Code (pt 1).srt (13.8 KB)
• 6. Transfer Learning Code (pt 2).mp4 (46.1 MB)
• 6. Transfer Learning Code (pt 2).srt (10.4 KB)

• [Tutorialsplanet.NET].url (0.1 KB)