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种子名称： [DesireCourse.Net] Udemy - Tensorflow 2.0 Deep Learning and Artificial Intelligence
文件类型： 视频
文件数目： 121个文件
文件大小： 6.73 GB
收录时间： 2020-1-26 12:07
已经下载： 3次
资源热度： 163
最近下载： 2024-11-6 19:07

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[DesireCourse.Net] Udemy - Tensorflow 2.0 Deep Learning and Artificial Intelligence.torrent

1. Welcome/1. Introduction.mp439.15MB

1. Welcome/2. Outline.mp430.81MB

1. Welcome/3. Where to get the code.mp430.48MB

10. GANs (Generative Adversarial Networks)/1. GAN Theory.mp486.53MB

10. GANs (Generative Adversarial Networks)/2. GAN Code.mp478.19MB

11. Deep Reinforcement Learning (Theory)/1. Deep Reinforcement Learning Section Introduction.mp437.77MB

11. Deep Reinforcement Learning (Theory)/10. Epsilon-Greedy.mp437.58MB

11. Deep Reinforcement Learning (Theory)/11. Q-Learning.mp461.28MB

11. Deep Reinforcement Learning (Theory)/12. Deep Q-Learning DQN (pt 1).mp455.7MB

11. Deep Reinforcement Learning (Theory)/13. Deep Q-Learning DQN (pt 2).mp449.2MB

11. Deep Reinforcement Learning (Theory)/14. How to Learn Reinforcement Learning.mp437.5MB

11. Deep Reinforcement Learning (Theory)/2. Elements of a Reinforcement Learning Problem.mp497.78MB

11. Deep Reinforcement Learning (Theory)/3. States, Actions, Rewards, Policies.mp442.99MB

11. Deep Reinforcement Learning (Theory)/4. Markov Decision Processes (MDPs).mp448.95MB

11. Deep Reinforcement Learning (Theory)/5. The Return.mp420.94MB

11. Deep Reinforcement Learning (Theory)/6. Value Functions and the Bellman Equation.mp443.28MB

11. Deep Reinforcement Learning (Theory)/7. What does it mean to “learn”.mp430.35MB

11. Deep Reinforcement Learning (Theory)/8. Solving the Bellman Equation with Reinforcement Learning (pt 1).mp439.03MB

11. Deep Reinforcement Learning (Theory)/9. Solving the Bellman Equation with Reinforcement Learning (pt 2).mp452.54MB

12. Stock Trading Project with Deep Reinforcement Learning/1. Reinforcement Learning Stock Trader Introduction.mp429.66MB

12. Stock Trading Project with Deep Reinforcement Learning/2. Data and Environment.mp456.02MB

12. Stock Trading Project with Deep Reinforcement Learning/3. Replay Buffer.mp424.06MB

12. Stock Trading Project with Deep Reinforcement Learning/4. Program Design and Layout.mp429.77MB

12. Stock Trading Project with Deep Reinforcement Learning/5. Code pt 1.mp446.83MB

12. Stock Trading Project with Deep Reinforcement Learning/6. Code pt 2.mp483.39MB

12. Stock Trading Project with Deep Reinforcement Learning/7. Code pt 3.mp462.33MB

12. Stock Trading Project with Deep Reinforcement Learning/8. Code pt 4.mp459.15MB

12. Stock Trading Project with Deep Reinforcement Learning/9. Reinforcement Learning Stock Trader Discussion.mp418.18MB

13. Advanced Tensorflow Usage/1. What is a Web Service (Tensorflow Serving pt 1).mp431.56MB

13. Advanced Tensorflow Usage/2. Tensorflow Serving pt 2.mp4124.46MB

13. Advanced Tensorflow Usage/3. Tensorflow Lite (TFLite).mp442.39MB

13. Advanced Tensorflow Usage/4. Why is Google the King of Distributed Computing.mp450.81MB

13. Advanced Tensorflow Usage/5. Training with Distributed Strategies.mp450.06MB

14. Low-Level Tensorflow/1. Differences Between Tensorflow 1.x and Tensorflow 2.x.mp442.5MB

14. Low-Level Tensorflow/2. Constants and Basic Computation.mp450.23MB

14. Low-Level Tensorflow/3. Variables and Gradient Tape.mp470.61MB

14. Low-Level Tensorflow/4. Build Your Own Custom Model.mp470.18MB

15. In-Depth Loss Functions/1. Mean Squared Error.mp437.34MB

15. In-Depth Loss Functions/2. Binary Cross Entropy.mp421.5MB

15. In-Depth Loss Functions/3. Categorical Cross Entropy.mp435.43MB

16. In-Depth Gradient Descent/1. Gradient Descent.mp435.54MB

16. In-Depth Gradient Descent/2. Stochastic Gradient Descent.mp425.04MB

16. In-Depth Gradient Descent/3. Momentum.mp439.35MB

16. In-Depth Gradient Descent/4. Variable and Adaptive Learning Rates.mp438.52MB

16. In-Depth Gradient Descent/5. Adam.mp442.57MB

18. Appendix FAQ/1. What is the Appendix.mp418.04MB

18. Appendix FAQ/10. What order should I take your courses in (part 1).mp488.14MB

18. Appendix FAQ/11. What order should I take your courses in (part 2).mp4122.64MB

18. Appendix FAQ/12. Bonus Where to get discount coupons and FREE deep learning material.mp413.29MB

18. Appendix FAQ/2. Windows-Focused Environment Setup 2018.mp4193.99MB

18. Appendix FAQ/3. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4166.72MB

18. Appendix FAQ/4. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4117.07MB

18. Appendix FAQ/5. How to Code Yourself (part 1).mp482.12MB

18. Appendix FAQ/6. How to Code Yourself (part 2).mp456.41MB

18. Appendix FAQ/7. Proof that using Jupyter Notebook is the same as not using it.mp477.94MB

18. Appendix FAQ/8. How to Succeed in this Course (Long Version).mp438.92MB

18. Appendix FAQ/9. Is Theano Dead.mp444.38MB

2. Google Colab/1. Intro to Google Colab, how to use a GPU or TPU for free.mp465.17MB

2. Google Colab/2. Tensorflow 2.0 in Google Colab.mp451.14MB

2. Google Colab/3. Uploading your own data to Google Colab.mp489.09MB

2. Google Colab/4. Where can I learn about Numpy, Scipy, Matplotlib, Pandas, and Scikit-Learn.mp443.85MB

3. Machine Learning and Neurons/1. What is Machine Learning.mp473.16MB

3. Machine Learning and Neurons/2. Code Preparation (Classification Theory).mp468.5MB

3. Machine Learning and Neurons/3. Classification Notebook.mp466.3MB

3. Machine Learning and Neurons/4. Code Preparation (Regression Theory).mp431.33MB

3. Machine Learning and Neurons/5. Regression Notebook.mp471.75MB

3. Machine Learning and Neurons/6. The Neuron.mp449.43MB

3. Machine Learning and Neurons/7. How does a model learn.mp455.02MB

3. Machine Learning and Neurons/8. Making Predictions.mp441.95MB

3. Machine Learning and Neurons/9. Saving and Loading a Model.mp435.29MB

4. Feedforward Artificial Neural Networks/1. Artificial Neural Networks Section Introduction.mp432.54MB

4. Feedforward Artificial Neural Networks/2. Forward Propagation.mp449.32MB

4. Feedforward Artificial Neural Networks/3. The Geometrical Picture.mp454.81MB

4. Feedforward Artificial Neural Networks/4. Activation Functions.mp492.17MB

4. Feedforward Artificial Neural Networks/5. Multiclass Classification.mp446.87MB

4. Feedforward Artificial Neural Networks/6. How to Represent Images.mp480.85MB

4. Feedforward Artificial Neural Networks/7. Code Preparation (ANN).mp456.16MB

4. Feedforward Artificial Neural Networks/8. ANN for Image Classification.mp458.35MB

4. Feedforward Artificial Neural Networks/9. ANN for Regression.mp483.95MB

5. Convolutional Neural Networks/1. What is Convolution (part 1).mp483.58MB

5. Convolutional Neural Networks/10. Batch Normalization.mp423.45MB

5. Convolutional Neural Networks/11. Improving CIFAR-10 Results.mp486.35MB

5. Convolutional Neural Networks/2. What is Convolution (part 2).mp425.15MB

5. Convolutional Neural Networks/3. What is Convolution (part 3).mp428.86MB

5. Convolutional Neural Networks/4. Convolution on Color Images.mp477.03MB

5. Convolutional Neural Networks/5. CNN Architecture.mp490.94MB

5. Convolutional Neural Networks/6. CNN Code Preparation.mp486.3MB

5. Convolutional Neural Networks/7. CNN for Fashion MNIST.mp451.65MB

5. Convolutional Neural Networks/8. CNN for CIFAR-10.mp434.8MB

5. Convolutional Neural Networks/9. Data Augmentation.mp439.16MB

6. Recurrent Neural Networks, Time Series, and Sequence Data/1. Sequence Data.mp4103.19MB

6. Recurrent Neural Networks, Time Series, and Sequence Data/10. GRU and LSTM (pt 2).mp453.58MB

6. Recurrent Neural Networks, Time Series, and Sequence Data/11. A More Challenging Sequence.mp477.66MB

6. Recurrent Neural Networks, Time Series, and Sequence Data/12. Demo of the Long Distance Problem.mp4143.12MB

6. Recurrent Neural Networks, Time Series, and Sequence Data/13. RNN for Image Classification (Theory).mp431.5MB

6. Recurrent Neural Networks, Time Series, and Sequence Data/14. RNN for Image Classification (Code).mp427.44MB

6. Recurrent Neural Networks, Time Series, and Sequence Data/15. Stock Return Predictions using LSTMs (pt 1).mp480.04MB

6. Recurrent Neural Networks, Time Series, and Sequence Data/16. Stock Return Predictions using LSTMs (pt 2).mp438.19MB

6. Recurrent Neural Networks, Time Series, and Sequence Data/17. Stock Return Predictions using LSTMs (pt 3).mp476.74MB

6. Recurrent Neural Networks, Time Series, and Sequence Data/2. Forecasting.mp447.24MB

6. Recurrent Neural Networks, Time Series, and Sequence Data/3. Autoregressive Linear Model for Time Series Prediction.mp487.68MB

6. Recurrent Neural Networks, Time Series, and Sequence Data/4. Proof that the Linear Model Works.mp418.27MB

6. Recurrent Neural Networks, Time Series, and Sequence Data/5. Recurrent Neural Networks.mp492.04MB

6. Recurrent Neural Networks, Time Series, and Sequence Data/6. RNN Code Preparation.mp420.42MB

6. Recurrent Neural Networks, Time Series, and Sequence Data/7. RNN for Time Series Prediction.mp487.22MB

6. Recurrent Neural Networks, Time Series, and Sequence Data/8. Paying Attention to Shapes.mp464.34MB

6. Recurrent Neural Networks, Time Series, and Sequence Data/9. GRU and LSTM (pt 1).mp476.09MB

7. Natural Language Processing (NLP)/1. Embeddings.mp457.96MB

7. Natural Language Processing (NLP)/2. Code Preparation (NLP).mp462.93MB

7. Natural Language Processing (NLP)/3. Text Preprocessing.mp436.14MB

7. Natural Language Processing (NLP)/4. Text Classification with LSTMs.mp460.56MB

7. Natural Language Processing (NLP)/5. CNNs for Text.mp440.86MB

7. Natural Language Processing (NLP)/6. Text Classification with CNNs.mp446.4MB

8. Recommender Systems/1. Recommender Systems with Deep Learning Theory.mp468.73MB

8. Recommender Systems/2. Recommender Systems with Deep Learning Code.mp458.79MB

9. Transfer Learning for Computer Vision/1. Transfer Learning Theory.mp455.15MB

9. Transfer Learning for Computer Vision/2. Some Pre-trained Models (VGG, ResNet, Inception, MobileNet).mp431.55MB

9. Transfer Learning for Computer Vision/3. Large Datasets and Data Generators.mp436.56MB

9. Transfer Learning for Computer Vision/4. 2 Approaches to Transfer Learning.mp420.62MB

9. Transfer Learning for Computer Vision/5. Transfer Learning Code (pt 1).mp466.57MB

9. Transfer Learning for Computer Vision/6. Transfer Learning Code (pt 2).mp446.06MB