[FreeCourseSite.com] Udemy - PyTorch Deep Learning and Artificial Intelligence

磁力链接/BT种子名称

[FreeCourseSite.com] Udemy - PyTorch Deep Learning and Artificial Intelligence

磁力链接/BT种子简介

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收录时间：2023-12-25
最近下载：2025-08-09

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文件列表

  • 19. Setting up your Environment (FAQ by Student Request)/3. Anaconda Environment Setup.mp4 365.3 MB
  • 19. Setting up your Environment (FAQ by Student Request)/4. Installing NVIDIA GPU-Accelerated Deep Learning Libraries on your Home Computer.mp4 227.1 MB
  • 19. Setting up your Environment (FAQ by Student Request)/2. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4 220.4 MB
  • 8. Natural Language Processing (NLP)/7. Text Classification with LSTMs (V2).mp4 185.4 MB
  • 7. Recurrent Neural Networks, Time Series, and Sequence Data/11. A More Challenging Sequence.mp4 168.4 MB
  • 21. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/4. Machine Learning and AI Prerequisite Roadmap (pt 2).mp4 157.1 MB
  • 21. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/3. Machine Learning and AI Prerequisite Roadmap (pt 1).mp4 154.5 MB
  • 5. Feedforward Artificial Neural Networks/9. ANN for Image Classification.mp4 149.3 MB
  • 4. Machine Learning and Neurons/9. Classification Notebook.mp4 147.3 MB
  • 9. Recommender Systems/3. Recommender Systems with Deep Learning Code (pt 1).mp4 133.7 MB
  • 3. Google Colab/2. Uploading your own data to Google Colab.mp4 133.0 MB
  • 4. Machine Learning and Neurons/6. Moore's Law Notebook.mp4 132.8 MB
  • 20. 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 124.8 MB
  • 7. Recurrent Neural Networks, Time Series, and Sequence Data/3. Autoregressive Linear Model for Time Series Prediction.mp4 115.8 MB
  • 5. Feedforward Artificial Neural Networks/10. ANN for Regression.mp4 108.9 MB
  • 20. Extra Help With Python Coding for Beginners (FAQ by Student Request)/2. How to Code Yourself (part 1).mp4 108.7 MB
  • 10. Transfer Learning for Computer Vision/5. Transfer Learning Code (pt 1).mp4 104.8 MB
  • 9. Recommender Systems/4. Recommender Systems with Deep Learning Code (pt 2).mp4 101.7 MB
  • 7. Recurrent Neural Networks, Time Series, and Sequence Data/8. Paying Attention to Shapes.mp4 96.2 MB
  • 7. Recurrent Neural Networks, Time Series, and Sequence Data/16. Stock Return Predictions using LSTMs (pt 3).mp4 94.6 MB
  • 8. Natural Language Processing (NLP)/10. (Legacy) VIP Making Predictions with a Trained NLP Model.mp4 91.9 MB
  • 13. Stock Trading Project with Deep Reinforcement Learning/7. Code pt 3.mp4 87.2 MB
  • 11. GANs (Generative Adversarial Networks)/3. GAN Code.mp4 85.5 MB
  • 6. Convolutional Neural Networks/6. CNN Code Preparation (part 1).mp4 83.8 MB
  • 7. Recurrent Neural Networks, Time Series, and Sequence Data/9. GRU and LSTM (pt 1).mp4 83.7 MB
  • 6. Convolutional Neural Networks/1. What is Convolution (part 1).mp4 83.6 MB
  • 10. Transfer Learning for Computer Vision/1. Transfer Learning Theory.mp4 80.7 MB
  • 6. Convolutional Neural Networks/13. Improving CIFAR-10 Results.mp4 79.4 MB
  • 6. Convolutional Neural Networks/4. Convolution on Color Images.mp4 79.3 MB
  • 6. Convolutional Neural Networks/9. CNN for Fashion MNIST.mp4 77.3 MB
  • 8. Natural Language Processing (NLP)/4. Beginner Blues - PyTorch NLP Version.mp4 76.9 MB
  • 10. Transfer Learning for Computer Vision/6. Transfer Learning Code (pt 2).mp4 76.2 MB
  • 7. Recurrent Neural Networks, Time Series, and Sequence Data/1. Sequence Data.mp4 74.9 MB
  • 4. Machine Learning and Neurons/4. Regression Notebook.mp4 74.7 MB
  • 7. Recurrent Neural Networks, Time Series, and Sequence Data/14. Stock Return Predictions using LSTMs (pt 1).mp4 73.4 MB
  • 21. 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 72.5 MB
  • 11. GANs (Generative Adversarial Networks)/1. GAN Theory.mp4 72.1 MB
  • 5. Feedforward Artificial Neural Networks/8. Code Preparation (ANN).mp4 69.3 MB
  • 4. Machine Learning and Neurons/7. Linear Classification Basics.mp4 69.1 MB
  • 12. Deep Reinforcement Learning (Theory)/2. Elements of a Reinforcement Learning Problem.mp4 67.8 MB
  • 22. Appendix FAQ Finale/2. BONUS.mp4 67.7 MB
  • 2. Getting Set Up/2. How to use Github & Extra Coding Tips (Optional).mp4 67.0 MB
  • 15. VIP Facial Recognition/7. Generating Generators.mp4 63.2 MB
  • 7. Recurrent Neural Networks, Time Series, and Sequence Data/5. Recurrent Neural Networks.mp4 61.8 MB
  • 8. Natural Language Processing (NLP)/8. CNNs for Text.mp4 61.3 MB
  • 13. Stock Trading Project with Deep Reinforcement Learning/6. Code pt 2.mp4 60.6 MB
  • 6. Convolutional Neural Networks/5. CNN Architecture.mp4 60.4 MB
  • 4. Machine Learning and Neurons/2. Regression Basics.mp4 60.4 MB
  • 3. Google Colab/3. Where can I learn about Numpy, Scipy, Matplotlib, Pandas, and Scikit-Learn.mp4 59.8 MB
  • 3. Google Colab/1. Intro to Google Colab, how to use a GPU or TPU for free.mp4 58.7 MB
  • 12. Deep Reinforcement Learning (Theory)/9. Solving the Bellman Equation with Reinforcement Learning (pt 2).mp4 58.2 MB
  • 5. Feedforward Artificial Neural Networks/4. Activation Functions.mp4 58.0 MB
  • 6. Convolutional Neural Networks/10. CNN for CIFAR-10.mp4 58.0 MB
  • 17. In-Depth Gradient Descent/5. Adam (pt 1).mp4 57.8 MB
  • 7. Recurrent Neural Networks, Time Series, and Sequence Data/15. Stock Return Predictions using LSTMs (pt 2).mp4 56.9 MB
  • 4. Machine Learning and Neurons/1. What is Machine Learning.mp4 56.9 MB
  • 13. Stock Trading Project with Deep Reinforcement Learning/5. Code pt 1.mp4 56.3 MB
  • 7. Recurrent Neural Networks, Time Series, and Sequence Data/7. RNN for Time Series Prediction.mp4 55.6 MB
  • 13. Stock Trading Project with Deep Reinforcement Learning/8. Code pt 4.mp4 55.1 MB
  • 7. Recurrent Neural Networks, Time Series, and Sequence Data/10. GRU and LSTM (pt 2).mp4 52.9 MB
  • 4. Machine Learning and Neurons/10. Saving and Loading a Model.mp4 52.3 MB
  • 15. VIP Facial Recognition/4. Loading in the data.mp4 51.3 MB
  • 20. Extra Help With Python Coding for Beginners (FAQ by Student Request)/1. Beginner's Coding Tips.mp4 51.1 MB
  • 4. Machine Learning and Neurons/13. Model With Logits.mp4 50.8 MB
  • 7. Recurrent Neural Networks, Time Series, and Sequence Data/2. Forecasting.mp4 50.7 MB
  • 1. Introduction/2. Overview and Outline.mp4 50.6 MB
  • 8. Natural Language Processing (NLP)/9. Text Classification with CNNs (V2).mp4 50.6 MB
  • 9. Recommender Systems/1. Recommender Systems with Deep Learning Theory.mp4 48.7 MB
  • 8. Natural Language Processing (NLP)/6. (Legacy) Text Preprocessing Code Example.mp4 48.5 MB
  • 12. Deep Reinforcement Learning (Theory)/8. Solving the Bellman Equation with Reinforcement Learning (pt 1).mp4 48.1 MB
  • 15. VIP Facial Recognition/9. Accuracy and imbalanced classes.mp4 47.9 MB
  • 12. Deep Reinforcement Learning (Theory)/14. How to Learn Reinforcement Learning.mp4 47.7 MB
  • 5. Feedforward Artificial Neural Networks/6. How to Represent Images.mp4 47.6 MB
  • 9. Recommender Systems/5. VIP Making Predictions with a Trained Recommender Model.mp4 46.9 MB
  • 12. Deep Reinforcement Learning (Theory)/11. Q-Learning.mp4 44.4 MB
  • 17. In-Depth Gradient Descent/6. Adam (pt 2).mp4 44.3 MB
  • 15. VIP Facial Recognition/6. Converting the data into pairs.mp4 43.7 MB
  • 12. Deep Reinforcement Learning (Theory)/12. Deep Q-Learning DQN (pt 1).mp4 41.2 MB
  • 7. Recurrent Neural Networks, Time Series, and Sequence Data/6. RNN Code Preparation.mp4 40.9 MB
  • 8. Natural Language Processing (NLP)/1. Embeddings.mp4 39.0 MB
  • 13. Stock Trading Project with Deep Reinforcement Learning/2. Data and Environment.mp4 38.4 MB
  • 8. Natural Language Processing (NLP)/3. Text Preprocessing Concepts.mp4 37.0 MB
  • 14. VIP Uncertainty Estimation/2. Estimating Prediction Uncertainty Code.mp4 36.7 MB
  • 4. Machine Learning and Neurons/14. Train Sets vs. Validation Sets vs. Test Sets.mp4 35.3 MB
  • 2. Getting Set Up/5. Temporary 403 Errors.mp4 35.2 MB
  • 12. Deep Reinforcement Learning (Theory)/13. Deep Q-Learning DQN (pt 2).mp4 35.1 MB
  • 20. Extra Help With Python Coding for Beginners (FAQ by Student Request)/3. How to Code Yourself (part 2).mp4 35.0 MB
  • 4. Machine Learning and Neurons/12. How does a model learn.mp4 34.5 MB
  • 9. Recommender Systems/2. Recommender Systems with Deep Learning Code Preparation.mp4 34.4 MB
  • 5. Feedforward Artificial Neural Networks/3. The Geometrical Picture.mp4 34.4 MB
  • 8. Natural Language Processing (NLP)/11. VIP Making Predictions with a Trained NLP Model (V2).mp4 34.2 MB
  • 12. Deep Reinforcement Learning (Theory)/7. What does it mean to “learn”.mp4 33.2 MB
  • 4. Machine Learning and Neurons/3. Regression Code Preparation.mp4 33.0 MB
  • 5. Feedforward Artificial Neural Networks/5. Multiclass Classification.mp4 33.0 MB
  • 15. VIP Facial Recognition/2. Siamese Networks.mp4 32.9 MB
  • 12. Deep Reinforcement Learning (Theory)/4. Markov Decision Processes (MDPs).mp4 32.9 MB
  • 8. Natural Language Processing (NLP)/5. (Legacy) Text Preprocessing Code Preparation.mp4 32.4 MB
  • 4. Machine Learning and Neurons/11. A Short Neuroscience Primer.mp4 31.4 MB
  • 6. Convolutional Neural Networks/3. What is Convolution (part 3).mp4 31.3 MB
  • 5. Feedforward Artificial Neural Networks/2. Forward Propagation.mp4 31.1 MB
  • 5. Feedforward Artificial Neural Networks/11. How to Choose Hyperparameters.mp4 31.0 MB
  • 12. Deep Reinforcement Learning (Theory)/1. Deep Reinforcement Learning Section Introduction.mp4 30.9 MB
  • 12. Deep Reinforcement Learning (Theory)/6. Value Functions and the Bellman Equation.mp4 30.3 MB
  • 2. Getting Set Up/1. Get Your Hands Dirty, Practical Coding Experience, Data Links.mp4 30.3 MB
  • 1. Introduction/1. Welcome.mp4 30.3 MB
  • 6. Convolutional Neural Networks/11. Data Augmentation.mp4 30.3 MB
  • 17. In-Depth Gradient Descent/3. Momentum.mp4 30.1 MB
  • 14. VIP Uncertainty Estimation/1. Custom Loss and Estimating Prediction Uncertainty.mp4 29.7 MB
  • 12. Deep Reinforcement Learning (Theory)/3. States, Actions, Rewards, Policies.mp4 29.5 MB
  • 17. In-Depth Gradient Descent/2. Stochastic Gradient Descent.mp4 29.4 MB
  • 17. In-Depth Gradient Descent/4. Variable and Adaptive Learning Rates.mp4 28.8 MB
  • 4. Machine Learning and Neurons/5. Moore's Law.mp4 28.7 MB
  • 21. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/1. How to Succeed in this Course (Long Version).mp4 28.7 MB
  • 4. Machine Learning and Neurons/15. Suggestion Box.mp4 28.5 MB
  • 15. VIP Facial Recognition/8. Creating the model and loss.mp4 28.0 MB
  • 7. Recurrent Neural Networks, Time Series, and Sequence Data/13. RNN for Image Classification (Code).mp4 27.2 MB
  • 10. Transfer Learning for Computer Vision/3. Large Datasets.mp4 26.4 MB
  • 16. In-Depth Loss Functions/1. Mean Squared Error.mp4 25.4 MB
  • 6. Convolutional Neural Networks/2. What is Convolution (part 2).mp4 25.2 MB
  • 6. Convolutional Neural Networks/7. CNN Code Preparation (part 2).mp4 25.0 MB
  • 17. In-Depth Gradient Descent/1. Gradient Descent.mp4 25.0 MB
  • 12. Deep Reinforcement Learning (Theory)/10. Epsilon-Greedy.mp4 24.6 MB
  • 16. In-Depth Loss Functions/3. Categorical Cross Entropy.mp4 23.7 MB
  • 15. VIP Facial Recognition/5. Splitting the data into train and test.mp4 23.7 MB
  • 7. Recurrent Neural Networks, Time Series, and Sequence Data/12. RNN for Image Classification (Theory).mp4 21.3 MB
  • 5. Feedforward Artificial Neural Networks/1. Artificial Neural Networks Section Introduction.mp4 21.2 MB
  • 6. Convolutional Neural Networks/8. CNN Code Preparation (part 3).mp4 21.0 MB
  • 18. Extras/1. Where Are The Exercises.mp4 20.7 MB
  • 4. Machine Learning and Neurons/8. Classification Code Preparation.mp4 19.4 MB
  • 13. Stock Trading Project with Deep Reinforcement Learning/4. Program Design and Layout.mp4 19.2 MB
  • 7. Recurrent Neural Networks, Time Series, and Sequence Data/17. Other Ways to Forecast.mp4 19.0 MB
  • 11. GANs (Generative Adversarial Networks)/2. GAN Code Preparation.mp4 18.9 MB
  • 10. Transfer Learning for Computer Vision/2. Some Pre-trained Models (VGG, ResNet, Inception, MobileNet).mp4 18.9 MB
  • 13. Stock Trading Project with Deep Reinforcement Learning/1. Reinforcement Learning Stock Trader Introduction.mp4 18.8 MB
  • 2. Getting Set Up/3. Where to get the code, notebooks, and data.mp4 18.6 MB
  • 10. Transfer Learning for Computer Vision/4. 2 Approaches to Transfer Learning.mp4 18.0 MB
  • 2. Getting Set Up/4. How to Succeed in This Course.mp4 17.0 MB
  • 13. Stock Trading Project with Deep Reinforcement Learning/3. Replay Buffer.mp4 16.8 MB
  • 19. Setting up your Environment (FAQ by Student Request)/1. Pre-Installation Check.mp4 15.8 MB
  • 16. In-Depth Loss Functions/2. Binary Cross Entropy.mp4 15.7 MB
  • 6. Convolutional Neural Networks/12. Batch Normalization.mp4 15.4 MB
  • 12. Deep Reinforcement Learning (Theory)/5. The Return.mp4 15.0 MB
  • 15. VIP Facial Recognition/3. Code Outline.mp4 14.9 MB
  • 15. VIP Facial Recognition/1. Facial Recognition Section Introduction.mp4 14.5 MB
  • 7. Recurrent Neural Networks, Time Series, and Sequence Data/4. Proof that the Linear Model Works.mp4 12.0 MB
  • 15. VIP Facial Recognition/10. Facial Recognition Section Summary.mp4 11.8 MB
  • 8. Natural Language Processing (NLP)/2. Neural Networks with Embeddings.mp4 11.2 MB
  • 13. Stock Trading Project with Deep Reinforcement Learning/9. Reinforcement Learning Stock Trader Discussion.mp4 11.0 MB
  • 22. Appendix FAQ Finale/1. What is the Appendix.mp4 10.6 MB
  • 5. Feedforward Artificial Neural Networks/7. Color Mixing Clarification.mp4 3.2 MB
  • 19. Setting up your Environment (FAQ by Student Request)/4. Installing NVIDIA GPU-Accelerated Deep Learning Libraries on your Home Computer.srt 32.8 kB
  • 21. 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
  • 7. Recurrent Neural Networks, Time Series, and Sequence Data/1. Sequence Data.srt 30.3 kB
  • 6. Convolutional Neural Networks/5. CNN Architecture.srt 28.4 kB
  • 12. Deep Reinforcement Learning (Theory)/2. Elements of a Reinforcement Learning Problem.srt 26.9 kB
  • 7. Recurrent Neural Networks, Time Series, and Sequence Data/5. Recurrent Neural Networks.srt 26.3 kB
  • 6. Convolutional Neural Networks/6. CNN Code Preparation (part 1).srt 25.0 kB
  • 20. Extra Help With Python Coding for Beginners (FAQ by Student Request)/2. How to Code Yourself (part 1).srt 23.9 kB
  • 21. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/4. Machine Learning and AI Prerequisite Roadmap (pt 2).srt 23.6 kB
  • 7. Recurrent Neural Networks, Time Series, and Sequence Data/9. GRU and LSTM (pt 1).srt 23.3 kB
  • 5. Feedforward Artificial Neural Networks/4. Activation Functions.srt 23.2 kB
  • 5. Feedforward Artificial Neural Networks/9. ANN for Image Classification.srt 23.1 kB
  • 6. Convolutional Neural Networks/1. What is Convolution (part 1).srt 21.7 kB
  • 11. GANs (Generative Adversarial Networks)/1. GAN Theory.srt 21.6 kB
  • 6. Convolutional Neural Networks/4. Convolution on Color Images.srt 21.5 kB
  • 4. Machine Learning and Neurons/7. Linear Classification Basics.srt 21.0 kB
  • 5. Feedforward Artificial Neural Networks/8. Code Preparation (ANN).srt 21.0 kB
  • 8. Natural Language Processing (NLP)/7. Text Classification with LSTMs (V2).srt 21.0 kB
  • 4. Machine Learning and Neurons/2. Regression Basics.srt 20.6 kB
  • 19. Setting up your Environment (FAQ by Student Request)/3. Anaconda Environment Setup.srt 20.4 kB
  • 20. Extra Help With Python Coding for Beginners (FAQ by Student Request)/1. Beginner's Coding Tips.srt 19.5 kB
  • 4. Machine Learning and Neurons/1. What is Machine Learning.srt 18.9 kB
  • 12. Deep Reinforcement Learning (Theory)/11. Q-Learning.srt 18.3 kB
  • 8. Natural Language Processing (NLP)/3. Text Preprocessing Concepts.srt 18.3 kB
  • 1. Introduction/2. Overview and Outline.srt 18.2 kB
  • 7. Recurrent Neural Networks, Time Series, and Sequence Data/6. RNN Code Preparation.srt 18.1 kB
  • 4. Machine Learning and Neurons/4. Regression Notebook.srt 17.9 kB
  • 9. Recommender Systems/4. Recommender Systems with Deep Learning Code (pt 2).srt 17.8 kB
  • 17. In-Depth Gradient Descent/5. Adam (pt 1).srt 17.1 kB
  • 12. Deep Reinforcement Learning (Theory)/12. Deep Q-Learning DQN (pt 1).srt 16.8 kB
  • 4. Machine Learning and Neurons/3. Regression Code Preparation.srt 16.8 kB
  • 8. Natural Language Processing (NLP)/1. Embeddings.srt 16.5 kB
  • 21. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/3. Machine Learning and AI Prerequisite Roadmap (pt 1).srt 16.5 kB
  • 8. Natural Language Processing (NLP)/8. CNNs for Text.srt 16.4 kB
  • 7. Recurrent Neural Networks, Time Series, and Sequence Data/14. Stock Return Predictions using LSTMs (pt 1).srt 16.3 kB
  • 4. Machine Learning and Neurons/6. Moore's Law Notebook.srt 16.2 kB
  • 2. Getting Set Up/2. How to use Github & Extra Coding Tips (Optional).srt 16.1 kB
  • 13. Stock Trading Project with Deep Reinforcement Learning/2. Data and Environment.srt 16.1 kB
  • 12. Deep Reinforcement Learning (Theory)/9. Solving the Bellman Equation with Reinforcement Learning (pt 2).srt 15.8 kB
  • 3. Google Colab/3. Where can I learn about Numpy, Scipy, Matplotlib, Pandas, and Scikit-Learn.srt 15.8 kB
  • 8. Natural Language Processing (NLP)/5. (Legacy) Text Preprocessing Code Preparation.srt 15.7 kB
  • 5. Feedforward Artificial Neural Networks/6. How to Represent Images.srt 15.7 kB
  • 17. In-Depth Gradient Descent/4. Variable and Adaptive Learning Rates.srt 15.5 kB
  • 7. Recurrent Neural Networks, Time Series, and Sequence Data/10. GRU and LSTM (pt 2).srt 15.3 kB
  • 19. Setting up your Environment (FAQ by Student Request)/2. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.srt 15.0 kB
  • 7. Recurrent Neural Networks, Time Series, and Sequence Data/3. Autoregressive Linear Model for Time Series Prediction.srt 15.0 kB
  • 21. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/1. How to Succeed in this Course (Long Version).srt 15.0 kB
  • 4. Machine Learning and Neurons/9. Classification Notebook.srt 14.9 kB
  • 8. Natural Language Processing (NLP)/4. Beginner Blues - PyTorch NLP Version.srt 14.9 kB
  • 17. In-Depth Gradient Descent/6. Adam (pt 2).srt 14.8 kB
  • 3. Google Colab/2. Uploading your own data to Google Colab.srt 14.8 kB
  • 7. Recurrent Neural Networks, Time Series, and Sequence Data/16. Stock Return Predictions using LSTMs (pt 3).srt 14.7 kB
  • 3. Google Colab/1. Intro to Google Colab, how to use a GPU or TPU for free.srt 14.7 kB
  • 4. Machine Learning and Neurons/14. Train Sets vs. Validation Sets vs. Test Sets.srt 14.6 kB
  • 20. 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
  • 7. Recurrent Neural Networks, Time Series, and Sequence Data/2. Forecasting.srt 14.2 kB
  • 4. Machine Learning and Neurons/12. How does a model learn.srt 14.1 kB
  • 9. Recommender Systems/1. Recommender Systems with Deep Learning Theory.srt 14.0 kB
  • 6. Convolutional Neural Networks/9. CNN for Fashion MNIST.srt 13.7 kB
  • 12. Deep Reinforcement Learning (Theory)/13. Deep Q-Learning DQN (pt 2).srt 13.5 kB
  • 5. Feedforward Artificial Neural Networks/10. ANN for Regression.srt 13.3 kB
  • 20. Extra Help With Python Coding for Beginners (FAQ by Student Request)/3. How to Code Yourself (part 2).srt 13.3 kB
  • 12. Deep Reinforcement Learning (Theory)/8. Solving the Bellman Equation with Reinforcement Learning (pt 1).srt 13.2 kB
  • 15. VIP Facial Recognition/2. Siamese Networks.srt 13.1 kB
  • 6. Convolutional Neural Networks/13. Improving CIFAR-10 Results.srt 13.1 kB
  • 14. VIP Uncertainty Estimation/1. Custom Loss and Estimating Prediction Uncertainty.srt 13.1 kB
  • 9. Recommender Systems/2. Recommender Systems with Deep Learning Code Preparation.srt 13.0 kB
  • 12. Deep Reinforcement Learning (Theory)/4. Markov Decision Processes (MDPs).srt 13.0 kB
  • 6. Convolutional Neural Networks/11. Data Augmentation.srt 12.8 kB
  • 12. Deep Reinforcement Learning (Theory)/6. Value Functions and the Bellman Equation.srt 12.8 kB
  • 4. Machine Learning and Neurons/11. A Short Neuroscience Primer.srt 12.6 kB
  • 5. Feedforward Artificial Neural Networks/2. Forward Propagation.srt 12.5 kB
  • 5. Feedforward Artificial Neural Networks/5. Multiclass Classification.srt 12.5 kB
  • 13. Stock Trading Project with Deep Reinforcement Learning/5. Code pt 1.srt 12.4 kB
  • 2. Getting Set Up/1. Get Your Hands Dirty, Practical Coding Experience, Data Links.srt 12.3 kB
  • 13. Stock Trading Project with Deep Reinforcement Learning/6. Code pt 2.srt 12.0 kB
  • 10. Transfer Learning for Computer Vision/5. Transfer Learning Code (pt 1).srt 11.9 kB
  • 5. Feedforward Artificial Neural Networks/3. The Geometrical Picture.srt 11.8 kB
  • 12. Deep Reinforcement Learning (Theory)/3. States, Actions, Rewards, Policies.srt 11.6 kB
  • 16. In-Depth Loss Functions/1. Mean Squared Error.srt 11.5 kB
  • 7. Recurrent Neural Networks, Time Series, and Sequence Data/8. Paying Attention to Shapes.srt 11.3 kB
  • 9. Recommender Systems/3. Recommender Systems with Deep Learning Code (pt 1).srt 11.2 kB
  • 10. Transfer Learning for Computer Vision/1. Transfer Learning Theory.srt 11.0 kB
  • 7. Recurrent Neural Networks, Time Series, and Sequence Data/11. A More Challenging Sequence.srt 10.9 kB
  • 11. GANs (Generative Adversarial Networks)/3. GAN Code.srt 10.9 kB
  • 6. Convolutional Neural Networks/7. CNN Code Preparation (part 2).srt 10.7 kB
  • 7. Recurrent Neural Networks, Time Series, and Sequence Data/7. RNN for Time Series Prediction.srt 10.1 kB
  • 17. In-Depth Gradient Descent/1. Gradient Descent.srt 10.0 kB
  • 16. In-Depth Loss Functions/3. Categorical Cross Entropy.srt 9.9 kB
  • 15. VIP Facial Recognition/9. Accuracy and imbalanced classes.srt 9.8 kB
  • 8. Natural Language Processing (NLP)/6. (Legacy) Text Preprocessing Code Example.srt 9.7 kB
  • 4. Machine Learning and Neurons/8. Classification Code Preparation.srt 9.6 kB
  • 4. Machine Learning and Neurons/5. Moore's Law.srt 9.4 kB
  • 8. Natural Language Processing (NLP)/10. (Legacy) VIP Making Predictions with a Trained NLP Model.srt 9.4 kB
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