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种子名称： [Tutorialsplanet.NET] Udemy - PyTorch Deep Learning and Artificial Intelligence
文件类型： 视频
文件数目： 149个文件
文件大小： 7.34 GB
收录时间： 2021-7-26 15:04
已经下载： 3次
资源热度： 196
最近下载： 2024-6-29 15:25

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[Tutorialsplanet.NET] Udemy - PyTorch Deep Learning and Artificial Intelligence.torrent

1. Introduction/1. Welcome.mp435.72MB

1. Introduction/2. Overview and Outline.mp479.67MB

1. Introduction/3. Where to get the Code.mp429.46MB

10. GANs (Generative Adversarial Networks)/1. GAN Theory.mp492.11MB

10. GANs (Generative Adversarial Networks)/2. GAN Code Preparation.mp428.08MB

10. GANs (Generative Adversarial Networks)/3. GAN Code.mp461.37MB

10. GANs (Generative Adversarial Networks)/4. Exercise DCGAN (Deep Convolutional GAN).mp415.31MB

11. Deep Reinforcement Learning (Theory)/1. Deep Reinforcement Learning Section Introduction.mp440.66MB

11. Deep Reinforcement Learning (Theory)/10. Epsilon-Greedy.mp441.46MB

11. Deep Reinforcement Learning (Theory)/11. Q-Learning.mp466.78MB

11. Deep Reinforcement Learning (Theory)/12. Deep Q-Learning DQN (pt 1).mp460.24MB

11. Deep Reinforcement Learning (Theory)/13. Deep Q-Learning DQN (pt 2).mp452.22MB

11. Deep Reinforcement Learning (Theory)/14. How to Learn Reinforcement Learning.mp440.25MB

11. Deep Reinforcement Learning (Theory)/2. Elements of a Reinforcement Learning Problem.mp4104.93MB

11. Deep Reinforcement Learning (Theory)/3. States, Actions, Rewards, Policies.mp444.13MB

11. Deep Reinforcement Learning (Theory)/4. Markov Decision Processes (MDPs).mp450.51MB

11. Deep Reinforcement Learning (Theory)/5. The Return.mp423.42MB

11. Deep Reinforcement Learning (Theory)/6. Value Functions and the Bellman Equation.mp447.72MB

11. Deep Reinforcement Learning (Theory)/7. What does it mean to “learn”.mp431.67MB

11. Deep Reinforcement Learning (Theory)/8. Solving the Bellman Equation with Reinforcement Learning (pt 1).mp442.92MB

11. Deep Reinforcement Learning (Theory)/9. Solving the Bellman Equation with Reinforcement Learning (pt 2).mp457.02MB

12. Stock Trading Project with Deep Reinforcement Learning/1. Reinforcement Learning Stock Trader Introduction.mp428.82MB

12. Stock Trading Project with Deep Reinforcement Learning/10. Exercise Personalized Stock Trading Bot.mp47.86MB

12. Stock Trading Project with Deep Reinforcement Learning/2. Data and Environment.mp455.69MB

12. Stock Trading Project with Deep Reinforcement Learning/3. Replay Buffer.mp424.97MB

12. Stock Trading Project with Deep Reinforcement Learning/4. Program Design and Layout.mp426.87MB

12. Stock Trading Project with Deep Reinforcement Learning/5. Code pt 1.mp466.33MB

12. Stock Trading Project with Deep Reinforcement Learning/6. Code pt 2.mp469.98MB

12. Stock Trading Project with Deep Reinforcement Learning/7. Code pt 3.mp458.59MB

12. Stock Trading Project with Deep Reinforcement Learning/8. Code pt 4.mp452.61MB

12. Stock Trading Project with Deep Reinforcement Learning/9. Reinforcement Learning Stock Trader Discussion.mp417.22MB

13. VIP Uncertainty Estimation/1. Custom Loss and Estimating Prediction Uncertainty.mp443.55MB

13. VIP Uncertainty Estimation/2. Estimating Prediction Uncertainty Code.mp442.75MB

14. VIP Facial Recognition/1. Facial Recognition Section Introduction.mp424.31MB

14. VIP Facial Recognition/10. Facial Recognition Section Summary.mp418.32MB

14. VIP Facial Recognition/2. Siamese Networks.mp450.51MB

14. VIP Facial Recognition/3. Code Outline.mp423.85MB

14. VIP Facial Recognition/4. Loading in the data.mp435.06MB

14. VIP Facial Recognition/5. Splitting the data into train and test.mp426.29MB

14. VIP Facial Recognition/6. Converting the data into pairs.mp430.38MB

14. VIP Facial Recognition/7. Generating Generators.mp432.44MB

14. VIP Facial Recognition/8. Creating the model and loss.mp429.38MB

14. VIP Facial Recognition/9. Accuracy and imbalanced classes.mp451.1MB

15. In-Depth Loss Functions/1. Mean Squared Error.mp433.79MB

15. In-Depth Loss Functions/2. Binary Cross Entropy.mp423.69MB

15. In-Depth Loss Functions/3. Categorical Cross Entropy.mp431.74MB

16. In-Depth Gradient Descent/1. Gradient Descent.mp434.9MB

16. In-Depth Gradient Descent/2. Stochastic Gradient Descent.mp422.99MB

16. In-Depth Gradient Descent/3. Momentum.mp434.25MB

16. In-Depth Gradient Descent/4. Variable and Adaptive Learning Rates.mp434.85MB

16. In-Depth Gradient Descent/5. Adam (pt 1).mp455.17MB

16. In-Depth Gradient Descent/6. Adam (pt 2).mp452.78MB

18. Setting up your Environment (FAQ by Student Request)/1. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4150.67MB

18. Setting up your Environment (FAQ by Student Request)/2. Windows-Focused Environment Setup 2018.mp4180.68MB

18. Setting up your Environment (FAQ by Student Request)/3. Installing NVIDIA GPU-Accelerated Deep Learning Libraries on your Home Computer.mp4167.33MB

19. Extra Help With Python Coding for Beginners (FAQ by Student Request)/1. How to Code Yourself (part 1).mp471.86MB

19. Extra Help With Python Coding for Beginners (FAQ by Student Request)/2. How to Code Yourself (part 2).mp449.15MB

19. Extra Help With Python Coding for Beginners (FAQ by Student Request)/3. Proof that using Jupyter Notebook is the same as not using it.mp469.49MB

2. Google Colab/1. Intro to Google Colab, how to use a GPU or TPU for free.mp460.46MB

2. Google Colab/2. Uploading your own data to Google Colab.mp490.54MB

2. Google Colab/3. Where can I learn about Numpy, Scipy, Matplotlib, Pandas, and Scikit-Learn.mp444.38MB

20. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/1. How to Succeed in this Course (Long Version).mp435.26MB

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.mp40B

20. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/3. Machine Learning and AI Prerequisite Roadmap (pt 1).mp479.59MB

20. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/4. Machine Learning and AI Prerequisite Roadmap (pt 2).mp4108.22MB

21. Appendix FAQ Finale/1. What is the Appendix.mp416.38MB

21. Appendix FAQ Finale/2. BONUS Where to get discount coupons and FREE deep learning material.mp437.81MB

3. Machine Learning and Neurons/1. What is Machine Learning.mp470.59MB

3. Machine Learning and Neurons/10. Classification Notebook.mp478.29MB

3. Machine Learning and Neurons/11. Exercise Predicting Diabetes Onset.mp412.56MB

3. Machine Learning and Neurons/12. Saving and Loading a Model.mp428.83MB

3. Machine Learning and Neurons/13. A Short Neuroscience Primer.mp444.65MB

3. Machine Learning and Neurons/14. How does a model learn.mp450.07MB

3. Machine Learning and Neurons/15. Model With Logits.mp427.32MB

3. Machine Learning and Neurons/16. Train Sets vs. Validation Sets vs. Test Sets.mp452.15MB

3. Machine Learning and Neurons/17. Suggestion Box.mp416.1MB

3. Machine Learning and Neurons/2. Regression Basics.mp473.02MB

3. Machine Learning and Neurons/3. Regression Code Preparation.mp445.54MB

3. Machine Learning and Neurons/4. Regression Notebook.mp471.93MB

3. Machine Learning and Neurons/5. Moore's Law.mp430.63MB

3. Machine Learning and Neurons/6. Moore's Law Notebook.mp478.91MB

3. Machine Learning and Neurons/7. Exercise Real Estate Predictions.mp45.58MB

3. Machine Learning and Neurons/8. Linear Classification Basics.mp467.22MB

3. Machine Learning and Neurons/9. Classification Code Preparation.mp426.54MB

4. Feedforward Artificial Neural Networks/1. Artificial Neural Networks Section Introduction.mp433.48MB

4. Feedforward Artificial Neural Networks/10. Exercise E. Coli Protein Localization Sites.mp410.46MB

4. Feedforward Artificial Neural Networks/2. Forward Propagation.mp447.1MB

4. Feedforward Artificial Neural Networks/3. The Geometrical Picture.mp456.42MB

4. Feedforward Artificial Neural Networks/4. Activation Functions.mp489.25MB

4. Feedforward Artificial Neural Networks/5. Multiclass Classification.mp448.69MB

4. Feedforward Artificial Neural Networks/6. How to Represent Images.mp475.43MB

4. Feedforward Artificial Neural Networks/7. Code Preparation (ANN).mp466.12MB

4. Feedforward Artificial Neural Networks/8. ANN for Image Classification.mp4106.32MB

4. Feedforward Artificial Neural Networks/9. ANN for Regression.mp480.19MB

5. Convolutional Neural Networks/1. What is Convolution (part 1).mp479.64MB

5. Convolutional Neural Networks/10. CNN for CIFAR-10.mp456.71MB

5. Convolutional Neural Networks/11. Data Augmentation.mp444.52MB

5. Convolutional Neural Networks/12. Batch Normalization.mp423.44MB

5. Convolutional Neural Networks/13. Improving CIFAR-10 Results.mp477.42MB

5. Convolutional Neural Networks/14. Exercise Facial Expression Recognition.mp48.25MB

5. Convolutional Neural Networks/2. What is Convolution (part 2).mp424.49MB

5. Convolutional Neural Networks/3. What is Convolution (part 3).mp428.7MB

5. Convolutional Neural Networks/4. Convolution on Color Images.mp476.38MB

5. Convolutional Neural Networks/5. CNN Architecture.mp489.54MB

5. Convolutional Neural Networks/6. CNN Code Preparation (part 1).mp476.73MB

5. Convolutional Neural Networks/7. CNN Code Preparation (part 2).mp436.71MB

5. Convolutional Neural Networks/8. CNN Code Preparation (part 3).mp433.69MB

5. Convolutional Neural Networks/9. CNN for Fashion MNIST.mp474.45MB

6. Recurrent Neural Networks, Time Series, and Sequence Data/1. Sequence Data.mp4114.29MB

6. Recurrent Neural Networks, Time Series, and Sequence Data/10. GRU and LSTM (pt 2).mp450.38MB

6. Recurrent Neural Networks, Time Series, and Sequence Data/11. A More Challenging Sequence.mp486.68MB

6. Recurrent Neural Networks, Time Series, and Sequence Data/12. RNN for Image Classification (Theory).mp432.27MB

6. Recurrent Neural Networks, Time Series, and Sequence Data/13. RNN for Image Classification (Code).mp420.53MB

6. Recurrent Neural Networks, Time Series, and Sequence Data/14. Stock Return Predictions using LSTMs (pt 1).mp477.82MB

6. Recurrent Neural Networks, Time Series, and Sequence Data/15. Stock Return Predictions using LSTMs (pt 2).mp443.22MB

6. Recurrent Neural Networks, Time Series, and Sequence Data/16. Stock Return Predictions using LSTMs (pt 3).mp471.07MB

6. Recurrent Neural Networks, Time Series, and Sequence Data/17. Other Ways to Forecast.mp428.28MB

6. Recurrent Neural Networks, Time Series, and Sequence Data/18. Exercise More Forecasting.mp49.06MB

6. Recurrent Neural Networks, Time Series, and Sequence Data/2. Forecasting.mp448.5MB

6. Recurrent Neural Networks, Time Series, and Sequence Data/3. Autoregressive Linear Model for Time Series Prediction.mp481.2MB

6. Recurrent Neural Networks, Time Series, and Sequence Data/4. Proof that the Linear Model Works.mp417.92MB

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

6. Recurrent Neural Networks, Time Series, and Sequence Data/6. RNN Code Preparation.mp455.31MB

6. Recurrent Neural Networks, Time Series, and Sequence Data/7. RNN for Time Series Prediction.mp471.85MB

6. Recurrent Neural Networks, Time Series, and Sequence Data/8. Paying Attention to Shapes.mp456.4MB

6. Recurrent Neural Networks, Time Series, and Sequence Data/9. GRU and LSTM (pt 1).mp476.06MB

7. Natural Language Processing (NLP)/1. Embeddings.mp459.98MB

7. Natural Language Processing (NLP)/10. Exercise Sentiment Analysis.mp49.12MB

7. Natural Language Processing (NLP)/2. Neural Networks with Embeddings.mp415.63MB

7. Natural Language Processing (NLP)/3. Text Preprocessing (pt 1).mp452.3MB

7. Natural Language Processing (NLP)/4. Text Preprocessing (pt 2).mp444.41MB

7. Natural Language Processing (NLP)/5. Text Preprocessing (pt 3).mp447.73MB

7. Natural Language Processing (NLP)/6. Text Classification with LSTMs.mp465.05MB

7. Natural Language Processing (NLP)/7. CNNs for Text.mp458.54MB

7. Natural Language Processing (NLP)/8. Text Classification with CNNs.mp439.33MB

7. Natural Language Processing (NLP)/9. VIP Making Predictions with a Trained NLP Model.mp448.81MB

8. Recommender Systems/1. Recommender Systems with Deep Learning Theory.mp464.74MB

8. Recommender Systems/2. Recommender Systems with Deep Learning Code Preparation.mp440.1MB

8. Recommender Systems/3. Recommender Systems with Deep Learning Code (pt 1).mp469.57MB

8. Recommender Systems/4. Recommender Systems with Deep Learning Code (pt 2).mp476.88MB

8. Recommender Systems/5. VIP Making Predictions with a Trained Recommender Model.mp432.75MB

8. Recommender Systems/6. Exercise Book Recommendations.mp44.08MB

9. Transfer Learning for Computer Vision/1. Transfer Learning Theory.mp458.19MB

9. Transfer Learning for Computer Vision/2. Some Pre-trained Models (VGG, ResNet, Inception, MobileNet).mp421.67MB

9. Transfer Learning for Computer Vision/3. Large Datasets.mp441.26MB

9. Transfer Learning for Computer Vision/4. 2 Approaches to Transfer Learning.mp421.8MB

9. Transfer Learning for Computer Vision/5. Transfer Learning Code (pt 1).mp477.78MB

9. Transfer Learning for Computer Vision/6. Transfer Learning Code (pt 2).mp456.32MB

9. Transfer Learning for Computer Vision/7. Exercise Transfer Learning.mp46.95MB