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

Creation Time	Aug. 12, 2025, 8:33 a.m.
Last Access Time	Aug. 12, 2025, 8:33 a.m.
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Keywords	Tutorialsplanet and Intelligence - Deep Udemy PyTorch Artificial Learning NET
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Total Files	307
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18. Setting up your Environment (FAQ by Student Request)/2. Windows-Focused Environment Setup 2018.mp4 180.7 MB
18. Setting up your Environment (FAQ by Student Request)/3. Installing NVIDIA GPU-Accelerated Deep Learning Libraries on your Home Computer.mp4 167.3 MB
18. Setting up your Environment (FAQ by Student Request)/1. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4 150.7 MB
6. Recurrent Neural Networks, Time Series, and Sequence Data/1. Sequence Data.mp4 114.3 MB
20. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/4. Machine Learning and AI Prerequisite Roadmap (pt 2).mp4 108.2 MB
4. Feedforward Artificial Neural Networks/8. ANN for Image Classification.mp4 106.3 MB
11. Deep Reinforcement Learning (Theory)/2. Elements of a Reinforcement Learning Problem.mp4 104.9 MB
6. Recurrent Neural Networks, Time Series, and Sequence Data/5. Recurrent Neural Networks.mp4 92.6 MB
10. GANs (Generative Adversarial Networks)/1. GAN Theory.mp4 92.1 MB
2. Google Colab/2. Uploading your own data to Google Colab.mp4 90.5 MB
5. Convolutional Neural Networks/5. CNN Architecture.mp4 89.5 MB
4. Feedforward Artificial Neural Networks/4. Activation Functions.mp4 89.2 MB
6. Recurrent Neural Networks, Time Series, and Sequence Data/11. A More Challenging Sequence.mp4 86.7 MB
6. Recurrent Neural Networks, Time Series, and Sequence Data/3. Autoregressive Linear Model for Time Series Prediction.mp4 81.2 MB
4. Feedforward Artificial Neural Networks/9. ANN for Regression.mp4 80.2 MB
1. Introduction/2. Overview and Outline.mp4 79.7 MB
5. Convolutional Neural Networks/1. What is Convolution (part 1).mp4 79.6 MB
20. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/3. Machine Learning and AI Prerequisite Roadmap (pt 1).mp4 79.6 MB
3. Machine Learning and Neurons/6. Moore's Law Notebook.mp4 78.9 MB
3. Machine Learning and Neurons/10. Classification Notebook.mp4 78.3 MB
6. Recurrent Neural Networks, Time Series, and Sequence Data/14. Stock Return Predictions using LSTMs (pt 1).mp4 77.8 MB
9. Transfer Learning for Computer Vision/5. Transfer Learning Code (pt 1).mp4 77.8 MB
5. Convolutional Neural Networks/13. Improving CIFAR-10 Results.mp4 77.4 MB
8. Recommender Systems/4. Recommender Systems with Deep Learning Code (pt 2).mp4 76.9 MB
5. Convolutional Neural Networks/6. CNN Code Preparation (part 1).mp4 76.7 MB
5. Convolutional Neural Networks/4. Convolution on Color Images.mp4 76.4 MB
6. Recurrent Neural Networks, Time Series, and Sequence Data/9. GRU and LSTM (pt 1).mp4 76.1 MB
4. Feedforward Artificial Neural Networks/6. How to Represent Images.mp4 75.4 MB
5. Convolutional Neural Networks/9. CNN for Fashion MNIST.mp4 74.5 MB
3. Machine Learning and Neurons/2. Regression Basics.mp4 73.0 MB
3. Machine Learning and Neurons/4. Regression Notebook.mp4 71.9 MB
19. Extra Help With Python Coding for Beginners (FAQ by Student Request)/1. How to Code Yourself (part 1).mp4 71.9 MB
6. Recurrent Neural Networks, Time Series, and Sequence Data/7. RNN for Time Series Prediction.mp4 71.9 MB
6. Recurrent Neural Networks, Time Series, and Sequence Data/16. Stock Return Predictions using LSTMs (pt 3).mp4 71.1 MB
3. Machine Learning and Neurons/1. What is Machine Learning.mp4 70.6 MB
12. Stock Trading Project with Deep Reinforcement Learning/6. Code pt 2.mp4 70.0 MB
8. Recommender Systems/3. Recommender Systems with Deep Learning Code (pt 1).mp4 69.6 MB
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.mp4 69.5 MB
3. Machine Learning and Neurons/8. Linear Classification Basics.mp4 67.2 MB
11. Deep Reinforcement Learning (Theory)/11. Q-Learning.mp4 66.8 MB
12. Stock Trading Project with Deep Reinforcement Learning/5. Code pt 1.mp4 66.3 MB
4. Feedforward Artificial Neural Networks/7. Code Preparation (ANN).mp4 66.1 MB
7. Natural Language Processing (NLP)/6. Text Classification with LSTMs.mp4 65.0 MB
8. Recommender Systems/1. Recommender Systems with Deep Learning Theory.mp4 64.7 MB
10. GANs (Generative Adversarial Networks)/3. GAN Code.mp4 61.4 MB
2. Google Colab/1. Intro to Google Colab, how to use a GPU or TPU for free.mp4 60.5 MB
11. Deep Reinforcement Learning (Theory)/12. Deep Q-Learning DQN (pt 1).mp4 60.2 MB
7. Natural Language Processing (NLP)/1. Embeddings.mp4 60.0 MB
12. Stock Trading Project with Deep Reinforcement Learning/7. Code pt 3.mp4 58.6 MB
7. Natural Language Processing (NLP)/7. CNNs for Text.mp4 58.5 MB
9. Transfer Learning for Computer Vision/1. Transfer Learning Theory.mp4 58.2 MB
11. Deep Reinforcement Learning (Theory)/9. Solving the Bellman Equation with Reinforcement Learning (pt 2).mp4 57.0 MB
5. Convolutional Neural Networks/10. CNN for CIFAR-10.mp4 56.7 MB
4. Feedforward Artificial Neural Networks/3. The Geometrical Picture.mp4 56.4 MB
6. Recurrent Neural Networks, Time Series, and Sequence Data/8. Paying Attention to Shapes.mp4 56.4 MB
9. Transfer Learning for Computer Vision/6. Transfer Learning Code (pt 2).mp4 56.3 MB
12. Stock Trading Project with Deep Reinforcement Learning/2. Data and Environment.mp4 55.7 MB
6. Recurrent Neural Networks, Time Series, and Sequence Data/6. RNN Code Preparation.mp4 55.3 MB
16. In-Depth Gradient Descent/5. Adam (pt 1).mp4 55.2 MB
16. In-Depth Gradient Descent/6. Adam (pt 2).mp4 52.8 MB
12. Stock Trading Project with Deep Reinforcement Learning/8. Code pt 4.mp4 52.6 MB
7. Natural Language Processing (NLP)/3. Text Preprocessing (pt 1).mp4 52.3 MB
11. Deep Reinforcement Learning (Theory)/13. Deep Q-Learning DQN (pt 2).mp4 52.2 MB
3. Machine Learning and Neurons/16. Train Sets vs. Validation Sets vs. Test Sets.mp4 52.1 MB
14. VIP Facial Recognition/9. Accuracy and imbalanced classes.mp4 51.1 MB
11. Deep Reinforcement Learning (Theory)/4. Markov Decision Processes (MDPs).mp4 50.5 MB
14. VIP Facial Recognition/2. Siamese Networks.mp4 50.5 MB
6. Recurrent Neural Networks, Time Series, and Sequence Data/10. GRU and LSTM (pt 2).mp4 50.4 MB
3. Machine Learning and Neurons/14. How does a model learn.mp4 50.1 MB
19. Extra Help With Python Coding for Beginners (FAQ by Student Request)/2. How to Code Yourself (part 2).mp4 49.2 MB
7. Natural Language Processing (NLP)/9. VIP Making Predictions with a Trained NLP Model.mp4 48.8 MB
4. Feedforward Artificial Neural Networks/5. Multiclass Classification.mp4 48.7 MB
6. Recurrent Neural Networks, Time Series, and Sequence Data/2. Forecasting.mp4 48.5 MB
7. Natural Language Processing (NLP)/5. Text Preprocessing (pt 3).mp4 47.7 MB
11. Deep Reinforcement Learning (Theory)/6. Value Functions and the Bellman Equation.mp4 47.7 MB
4. Feedforward Artificial Neural Networks/2. Forward Propagation.mp4 47.1 MB
3. Machine Learning and Neurons/3. Regression Code Preparation.mp4 45.5 MB
3. Machine Learning and Neurons/13. A Short Neuroscience Primer.mp4 44.6 MB
5. Convolutional Neural Networks/11. Data Augmentation.mp4 44.5 MB
7. Natural Language Processing (NLP)/4. Text Preprocessing (pt 2).mp4 44.4 MB
2. Google Colab/3. Where can I learn about Numpy, Scipy, Matplotlib, Pandas, and Scikit-Learn.mp4 44.4 MB
11. Deep Reinforcement Learning (Theory)/3. States, Actions, Rewards, Policies.mp4 44.1 MB
13. VIP Uncertainty Estimation/1. Custom Loss and Estimating Prediction Uncertainty.mp4 43.6 MB
6. Recurrent Neural Networks, Time Series, and Sequence Data/15. Stock Return Predictions using LSTMs (pt 2).mp4 43.2 MB
11. Deep Reinforcement Learning (Theory)/8. Solving the Bellman Equation with Reinforcement Learning (pt 1).mp4 42.9 MB
13. VIP Uncertainty Estimation/2. Estimating Prediction Uncertainty Code.mp4 42.7 MB
11. Deep Reinforcement Learning (Theory)/10. Epsilon-Greedy.mp4 41.5 MB
9. Transfer Learning for Computer Vision/3. Large Datasets.mp4 41.3 MB
11. Deep Reinforcement Learning (Theory)/1. Deep Reinforcement Learning Section Introduction.mp4 40.7 MB
11. Deep Reinforcement Learning (Theory)/14. How to Learn Reinforcement Learning.mp4 40.3 MB
8. Recommender Systems/2. Recommender Systems with Deep Learning Code Preparation.mp4 40.1 MB
7. Natural Language Processing (NLP)/8. Text Classification with CNNs.mp4 39.3 MB
21. Appendix FAQ Finale/2. BONUS Where to get discount coupons and FREE deep learning material.mp4 37.8 MB
5. Convolutional Neural Networks/7. CNN Code Preparation (part 2).mp4 36.7 MB
1. Introduction/1. Welcome.mp4 35.7 MB
20. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/1. How to Succeed in this Course (Long Version).mp4 35.3 MB
14. VIP Facial Recognition/4. Loading in the data.mp4 35.1 MB
16. In-Depth Gradient Descent/1. Gradient Descent.mp4 34.9 MB
16. In-Depth Gradient Descent/4. Variable and Adaptive Learning Rates.mp4 34.9 MB
16. In-Depth Gradient Descent/3. Momentum.mp4 34.2 MB
15. In-Depth Loss Functions/1. Mean Squared Error.mp4 33.8 MB
5. Convolutional Neural Networks/8. CNN Code Preparation (part 3).mp4 33.7 MB
4. Feedforward Artificial Neural Networks/1. Artificial Neural Networks Section Introduction.mp4 33.5 MB
8. Recommender Systems/5. VIP Making Predictions with a Trained Recommender Model.mp4 32.8 MB
14. VIP Facial Recognition/7. Generating Generators.mp4 32.4 MB
6. Recurrent Neural Networks, Time Series, and Sequence Data/12. RNN for Image Classification (Theory).mp4 32.3 MB
15. In-Depth Loss Functions/3. Categorical Cross Entropy.mp4 31.7 MB
11. Deep Reinforcement Learning (Theory)/7. What does it mean to “learn”.mp4 31.7 MB
3. Machine Learning and Neurons/5. Moore's Law.mp4 30.6 MB
14. VIP Facial Recognition/6. Converting the data into pairs.mp4 30.4 MB
1. Introduction/3. Where to get the Code.mp4 29.5 MB
14. VIP Facial Recognition/8. Creating the model and loss.mp4 29.4 MB
3. Machine Learning and Neurons/12. Saving and Loading a Model.mp4 28.8 MB
12. Stock Trading Project with Deep Reinforcement Learning/1. Reinforcement Learning Stock Trader Introduction.mp4 28.8 MB
5. Convolutional Neural Networks/3. What is Convolution (part 3).mp4 28.7 MB
6. Recurrent Neural Networks, Time Series, and Sequence Data/17. Other Ways to Forecast.mp4 28.3 MB
10. GANs (Generative Adversarial Networks)/2. GAN Code Preparation.mp4 28.1 MB
3. Machine Learning and Neurons/15. Model With Logits.mp4 27.3 MB
12. Stock Trading Project with Deep Reinforcement Learning/4. Program Design and Layout.mp4 26.9 MB
3. Machine Learning and Neurons/9. Classification Code Preparation.mp4 26.5 MB
14. VIP Facial Recognition/5. Splitting the data into train and test.mp4 26.3 MB
12. Stock Trading Project with Deep Reinforcement Learning/3. Replay Buffer.mp4 25.0 MB
5. Convolutional Neural Networks/2. What is Convolution (part 2).mp4 24.5 MB
14. VIP Facial Recognition/1. Facial Recognition Section Introduction.mp4 24.3 MB
14. VIP Facial Recognition/3. Code Outline.mp4 23.9 MB
15. In-Depth Loss Functions/2. Binary Cross Entropy.mp4 23.7 MB
5. Convolutional Neural Networks/12. Batch Normalization.mp4 23.4 MB
11. Deep Reinforcement Learning (Theory)/5. The Return.mp4 23.4 MB
16. In-Depth Gradient Descent/2. Stochastic Gradient Descent.mp4 23.0 MB
9. Transfer Learning for Computer Vision/4. 2 Approaches to Transfer Learning.mp4 21.8 MB
9. Transfer Learning for Computer Vision/2. Some Pre-trained Models (VGG, ResNet, Inception, MobileNet).mp4 21.7 MB
6. Recurrent Neural Networks, Time Series, and Sequence Data/13. RNN for Image Classification (Code).mp4 20.5 MB
14. VIP Facial Recognition/10. Facial Recognition Section Summary.mp4 18.3 MB
6. Recurrent Neural Networks, Time Series, and Sequence Data/4. Proof that the Linear Model Works.mp4 17.9 MB
12. Stock Trading Project with Deep Reinforcement Learning/9. Reinforcement Learning Stock Trader Discussion.mp4 17.2 MB
21. Appendix FAQ Finale/1. What is the Appendix.mp4 16.4 MB
3. Machine Learning and Neurons/17. Suggestion Box.mp4 16.1 MB
7. Natural Language Processing (NLP)/2. Neural Networks with Embeddings.mp4 15.6 MB
10. GANs (Generative Adversarial Networks)/4. Exercise DCGAN (Deep Convolutional GAN).mp4 15.3 MB
3. Machine Learning and Neurons/11. Exercise Predicting Diabetes Onset.mp4 12.6 MB
4. Feedforward Artificial Neural Networks/10. Exercise E. Coli Protein Localization Sites.mp4 10.5 MB
7. Natural Language Processing (NLP)/10. Exercise Sentiment Analysis.mp4 9.1 MB
6. Recurrent Neural Networks, Time Series, and Sequence Data/18. Exercise More Forecasting.mp4 9.1 MB
5. Convolutional Neural Networks/14. Exercise Facial Expression Recognition.mp4 8.3 MB
12. Stock Trading Project with Deep Reinforcement Learning/10. Exercise Personalized Stock Trading Bot.mp4 7.9 MB
9. Transfer Learning for Computer Vision/7. Exercise Transfer Learning.mp4 7.0 MB
3. Machine Learning and Neurons/7. Exercise Real Estate Predictions.mp4 5.6 MB
8. Recommender Systems/6. Exercise Book Recommendations.mp4 4.1 MB
18. Setting up your Environment (FAQ by Student Request)/3. Installing NVIDIA GPU-Accelerated Deep Learning Libraries on your Home Computer.vtt 27.9 KB
6. Recurrent Neural Networks, Time Series, and Sequence Data/1. Sequence Data.vtt 25.8 KB
5. Convolutional Neural Networks/5. CNN Architecture.vtt 24.3 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
19. Extra Help With Python Coding for Beginners (FAQ by Student Request)/1. How to Code Yourself (part 1).vtt 20.2 KB
20. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/4. Machine Learning and AI Prerequisite Roadmap (pt 2).vtt 20.2 KB
5. Convolutional Neural Networks/6. CNN Code Preparation (part 1).vtt 20.1 KB
4. Feedforward Artificial Neural Networks/4. Activation Functions.vtt 19.8 KB
4. Feedforward Artificial Neural Networks/8. ANN for Image Classification.vtt 19.8 KB
10. GANs (Generative Adversarial Networks)/1. GAN Theory.vtt 18.4 KB
6. Recurrent Neural Networks, Time Series, and Sequence Data/9. GRU and LSTM (pt 1).vtt 18.4 KB
5. Convolutional Neural Networks/1. What is Convolution (part 1).vtt 18.4 KB
5. Convolutional Neural Networks/4. Convolution on Color Images.vtt 18.3 KB
4. Feedforward Artificial Neural Networks/7. Code Preparation (ANN).vtt 17.9 KB
3. Machine Learning and Neurons/2. Regression Basics.vtt 17.5 KB
3. Machine Learning and Neurons/8. Linear Classification Basics.vtt 17.4 KB
18. Setting up your Environment (FAQ by Student Request)/2. Windows-Focused Environment Setup 2018.vtt 17.3 KB
3. Machine Learning and Neurons/1. What is Machine Learning.vtt 16.2 KB
1. Introduction/2. Overview and Outline.vtt 15.7 KB
7. Natural Language Processing (NLP)/3. Text Preprocessing (pt 1).vtt 15.6 KB
11. Deep Reinforcement Learning (Theory)/11. Q-Learning.vtt 15.6 KB
6. Recurrent Neural Networks, Time Series, and Sequence Data/6. RNN Code Preparation.vtt 15.6 KB
3. Machine Learning and Neurons/4. Regression Notebook.vtt 15.3 KB
8. Recommender Systems/4. Recommender Systems with Deep Learning Code (pt 2).vtt 15.2 KB
16. In-Depth Gradient Descent/5. Adam (pt 1).vtt 14.6 KB
11. Deep Reinforcement Learning (Theory)/12. Deep Q-Learning DQN (pt 1).vtt 14.4 KB
3. Machine Learning and Neurons/3. Regression Code Preparation.vtt 14.3 KB
20. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/3. Machine Learning and AI Prerequisite Roadmap (pt 1).vtt 14.2 KB
7. Natural Language Processing (NLP)/1. Embeddings.vtt 14.1 KB
6. Recurrent Neural Networks, Time Series, and Sequence Data/14. Stock Return Predictions using LSTMs (pt 1).vtt 13.9 KB
7. Natural Language Processing (NLP)/7. CNNs for Text.vtt 13.8 KB
3. Machine Learning and Neurons/6. Moore's Law Notebook.vtt 13.8 KB
12. Stock Trading Project with Deep Reinforcement Learning/2. Data and Environment.vtt 13.8 KB
7. Natural Language Processing (NLP)/4. Text Preprocessing (pt 2).vtt 13.5 KB
4. Feedforward Artificial Neural Networks/6. How to Represent Images.vtt 13.4 KB
16. In-Depth Gradient Descent/4. Variable and Adaptive Learning Rates.vtt 13.3 KB
6. Recurrent Neural Networks, Time Series, and Sequence Data/10. GRU and LSTM (pt 2).vtt 13.1 KB
11. Deep Reinforcement Learning (Theory)/9. Solving the Bellman Equation with Reinforcement Learning (pt 2).vtt 13.0 KB
20. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/1. How to Succeed in this Course (Long Version).vtt 12.8 KB
3. Machine Learning and Neurons/10. Classification Notebook.vtt 12.8 KB
6. Recurrent Neural Networks, Time Series, and Sequence Data/3. Autoregressive Linear Model for Time Series Prediction.vtt 12.8 KB
16. In-Depth Gradient Descent/6. Adam (pt 2).vtt 12.7 KB
2. Google Colab/2. Uploading your own data to Google Colab.vtt 12.6 KB
6. Recurrent Neural Networks, Time Series, and Sequence Data/16. Stock Return Predictions using LSTMs (pt 3).vtt 12.6 KB
18. Setting up your Environment (FAQ by Student Request)/1. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.vtt 12.6 KB
2. Google Colab/1. Intro to Google Colab, how to use a GPU or TPU for free.vtt 12.5 KB
3. Machine Learning and Neurons/16. Train Sets vs. Validation Sets vs. Test Sets.vtt 12.5 KB
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.vtt 12.3 KB
3. Machine Learning and Neurons/14. How does a model learn.vtt 12.1 KB
6. Recurrent Neural Networks, Time Series, and Sequence Data/2. Forecasting.vtt 12.1 KB
8. Recommender Systems/1. Recommender Systems with Deep Learning Theory.vtt 11.9 KB
5. Convolutional Neural Networks/9. CNN for Fashion MNIST.vtt 11.8 KB
11. Deep Reinforcement Learning (Theory)/13. Deep Q-Learning DQN (pt 2).vtt 11.6 KB
19. Extra Help With Python Coding for Beginners (FAQ by Student Request)/2. How to Code Yourself (part 2).vtt 11.4 KB
4. Feedforward Artificial Neural Networks/9. ANN for Regression.vtt 11.4 KB
13. VIP Uncertainty Estimation/1. Custom Loss and Estimating Prediction Uncertainty.vtt 11.3 KB
14. VIP Facial Recognition/2. Siamese Networks.vtt 11.2 KB
11. Deep Reinforcement Learning (Theory)/4. Markov Decision Processes (MDPs).vtt 11.2 KB
5. Convolutional Neural Networks/13. Improving CIFAR-10 Results.vtt 11.2 KB
8. Recommender Systems/2. Recommender Systems with Deep Learning Code Preparation.vtt 11.1 KB
5. Convolutional Neural Networks/11. Data Augmentation.vtt 11.0 KB
11. Deep Reinforcement Learning (Theory)/6. Value Functions and the Bellman Equation.vtt 10.9 KB
11. Deep Reinforcement Learning (Theory)/8. Solving the Bellman Equation with Reinforcement Learning (pt 1).vtt 10.9 KB
3. Machine Learning and Neurons/13. A Short Neuroscience Primer.vtt 10.8 KB
4. Feedforward Artificial Neural Networks/5. Multiclass Classification.vtt 10.8 KB
4. Feedforward Artificial Neural Networks/2. Forward Propagation.vtt 10.7 KB
12. Stock Trading Project with Deep Reinforcement Learning/5. Code pt 1.vtt 10.6 KB
2. Google Colab/3. Where can I learn about Numpy, Scipy, Matplotlib, Pandas, and Scikit-Learn.vtt 10.6 KB
12. Stock Trading Project with Deep Reinforcement Learning/6. Code pt 2.vtt 10.3 KB
9. Transfer Learning for Computer Vision/5. Transfer Learning Code (pt 1).vtt 10.2 KB
4. Feedforward Artificial Neural Networks/3. The Geometrical Picture.vtt 10.2 KB
11. Deep Reinforcement Learning (Theory)/3. States, Actions, Rewards, Policies.vtt 10.0 KB
15. In-Depth Loss Functions/1. Mean Squared Error.vtt 9.9 KB
6. Recurrent Neural Networks, Time Series, and Sequence Data/8. Paying Attention to Shapes.vtt 9.6 KB
8. Recommender Systems/3. Recommender Systems with Deep Learning Code (pt 1).vtt 9.6 KB
9. Transfer Learning for Computer Vision/1. Transfer Learning Theory.vtt 9.4 KB
10. GANs (Generative Adversarial Networks)/3. GAN Code.vtt 9.4 KB
6. Recurrent Neural Networks, Time Series, and Sequence Data/11. A More Challenging Sequence.vtt 9.3 KB
5. Convolutional Neural Networks/7. CNN Code Preparation (part 2).vtt 9.3 KB
7. Natural Language Processing (NLP)/6. Text Classification with LSTMs.vtt 9.0 KB
6. Recurrent Neural Networks, Time Series, and Sequence Data/7. RNN for Time Series Prediction.vtt 8.6 KB
16. In-Depth Gradient Descent/1. Gradient Descent.vtt 8.6 KB
15. In-Depth Loss Functions/3. Categorical Cross Entropy.vtt 8.4 KB
14. VIP Facial Recognition/9. Accuracy and imbalanced classes.vtt 8.4 KB
3. Machine Learning and Neurons/9. Classification Code Preparation.vtt 8.3 KB
7. Natural Language Processing (NLP)/5. Text Preprocessing (pt 3).vtt 8.2 KB
3. Machine Learning and Neurons/5. Moore's Law.vtt 8.0 KB
5. Convolutional Neural Networks/10. CNN for CIFAR-10.vtt 8.0 KB
7. Natural Language Processing (NLP)/9. VIP Making Predictions with a Trained NLP Model.vtt 8.0 KB
9. Transfer Learning for Computer Vision/3. Large Datasets.vtt 8.0 KB
11. Deep Reinforcement Learning (Theory)/7. What does it mean to “learn”.vtt 7.8 KB
13. VIP Uncertainty Estimation/2. Estimating Prediction Uncertainty Code.vtt 7.7 KB
9. Transfer Learning for Computer Vision/6. Transfer Learning Code (pt 2).vtt 7.7 KB
12. Stock Trading Project with Deep Reinforcement Learning/4. Program Design and Layout.vtt 7.6 KB
11. Deep Reinforcement Learning (Theory)/1. Deep Reinforcement Learning Section Introduction.vtt 7.5 KB
10. GANs (Generative Adversarial Networks)/2. GAN Code Preparation.vtt 7.4 KB
12. Stock Trading Project with Deep Reinforcement Learning/7. Code pt 3.vtt 7.4 KB
12. Stock Trading Project with Deep Reinforcement Learning/8. Code pt 4.vtt 7.4 KB
17. Extras/1. Links To Colab Notebooks.html 7.2 KB
5. Convolutional Neural Networks/3. What is Convolution (part 3).vtt 7.0 KB
4. Feedforward Artificial Neural Networks/1. Artificial Neural Networks Section Introduction.vtt 6.9 KB
21. Appendix FAQ Finale/2. BONUS Where to get discount coupons and FREE deep learning material.vtt 6.9 KB
16. In-Depth Gradient Descent/3. Momentum.vtt 6.9 KB
1. Introduction/3. Where to get the Code.vtt 6.8 KB
11. Deep Reinforcement Learning (Theory)/14. How to Learn Reinforcement Learning.vtt 6.7 KB
11. Deep Reinforcement Learning (Theory)/10. Epsilon-Greedy.vtt 6.5 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
6. Recurrent Neural Networks, Time Series, and Sequence Data/17. Other Ways to Forecast.vtt 6.3 KB
5. Convolutional Neural Networks/8. CNN Code Preparation (part 3).vtt 6.2 KB
12. Stock Trading Project with Deep Reinforcement Learning/3. Replay Buffer.vtt 6.1 KB
14. VIP Facial Recognition/4. Loading in the data.vtt 6.0 KB
12. Stock Trading Project with Deep Reinforcement Learning/1. Reinforcement Learning Stock Trader Introduction.vtt 6.0 KB
6. Recurrent Neural Networks, Time Series, and Sequence Data/15. Stock Return Predictions using LSTMs (pt 2).vtt 6.0 KB
3. Machine Learning and Neurons/12. Saving and Loading a Model.vtt 5.8 KB
5. Convolutional Neural Networks/12. Batch Normalization.vtt 5.8 KB
11. Deep Reinforcement Learning (Theory)/5. The Return.vtt 5.5 KB
8. Recommender Systems/5. VIP Making Predictions with a Trained Recommender Model.vtt 5.3 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/12. RNN for Image Classification (Theory).vtt 5.2 KB
14. VIP Facial Recognition/6. Converting the data into pairs.vtt 5.1 KB
14. VIP Facial Recognition/3. Code Outline.vtt 5.1 KB
14. VIP Facial Recognition/7. Generating Generators.vtt 5.0 KB
1. Introduction/1. Welcome.vtt 5.0 KB
7. Natural Language Processing (NLP)/8. Text Classification with CNNs.vtt 4.9 KB
16. In-Depth Gradient Descent/2. Stochastic Gradient Descent.vtt 4.8 KB
3. Machine Learning and Neurons/15. Model With Logits.vtt 4.7 KB
14. VIP Facial Recognition/8. Creating the model and loss.vtt 4.7 KB
9. Transfer Learning for Computer Vision/2. Some Pre-trained Models (VGG, ResNet, Inception, MobileNet).vtt 4.6 KB
14. VIP Facial Recognition/5. Splitting the data into train and test.vtt 4.5 KB
3. Machine Learning and Neurons/17. Suggestion Box.vtt 4.1 KB
14. VIP Facial Recognition/1. Facial Recognition Section Introduction.vtt 4.0 KB
6. Recurrent Neural Networks, Time Series, and Sequence Data/4. Proof that the Linear Model Works.vtt 4.0 KB
7. Natural Language Processing (NLP)/2. Neural Networks with Embeddings.vtt 4.0 KB
14. VIP Facial Recognition/10. Facial Recognition Section Summary.vtt 3.9 KB
12. Stock Trading Project with Deep Reinforcement Learning/9. Reinforcement Learning Stock Trader Discussion.vtt 3.9 KB
21. Appendix FAQ Finale/1. What is the Appendix.vtt 3.3 KB
10. GANs (Generative Adversarial Networks)/4. Exercise DCGAN (Deep Convolutional GAN).vtt 3.1 KB
3. Machine Learning and Neurons/11. Exercise Predicting Diabetes Onset.vtt 2.9 KB
6. Recurrent Neural Networks, Time Series, and Sequence Data/13. RNN for Image Classification (Code).vtt 2.9 KB
4. Feedforward Artificial Neural Networks/10. Exercise E. Coli Protein Localization Sites.vtt 2.5 KB
7. Natural Language Processing (NLP)/10. Exercise Sentiment Analysis.vtt 2.3 KB
6. Recurrent Neural Networks, Time Series, and Sequence Data/18. Exercise More Forecasting.vtt 2.1 KB
12. Stock Trading Project with Deep Reinforcement Learning/10. Exercise Personalized Stock Trading Bot.vtt 2.0 KB
5. Convolutional Neural Networks/14. Exercise Facial Expression Recognition.vtt 1.7 KB
9. Transfer Learning for Computer Vision/7. Exercise Transfer Learning.vtt 1.6 KB
3. Machine Learning and Neurons/7. Exercise Real Estate Predictions.vtt 1.4 KB
8. Recommender Systems/6. Exercise Book Recommendations.vtt 1.2 KB
17. Extras/2. Links to VIP Notebooks.html 256 bytes
1. Introduction/[Tutorialsplanet.NET].url 128 bytes
10. GANs (Generative Adversarial Networks)/[Tutorialsplanet.NET].url 128 bytes
17. Extras/[Tutorialsplanet.NET].url 128 bytes
21. Appendix FAQ Finale/[Tutorialsplanet.NET].url 128 bytes
7. Natural Language Processing (NLP)/[Tutorialsplanet.NET].url 128 bytes
[Tutorialsplanet.NET].url 128 bytes
1. Introduction/3.1 Github Link.html 120 bytes
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.mp4 0 bytes
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.vtt 0 bytes

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