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

Creation Time	July 2, 2024, 5:46 p.m.
Last Access Time	Nov. 21, 2024, 6:59 a.m.
File Size	7.9 GB
Keywords	and FreeCourseSite Intelligence - Deep Udemy PyTorch Artificial Learning com
Total Requests	53
Total Files	327
Magnet Link	Please wait ...
	Download (Magnet Link) Play (Watch Online)

19. Setting up your Environment (FAQ by Student Request)/3. Anaconda Environment Setup.mp4 348.4 MB
19. Setting up your Environment (FAQ by Student Request)/4. Installing NVIDIA GPU-Accelerated Deep Learning Libraries on your Home Computer.mp4 216.6 MB
19. Setting up your Environment (FAQ by Student Request)/2. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4 210.2 MB
8. Natural Language Processing (NLP)/7. Text Classification with LSTMs (V2).mp4 176.8 MB
7. Recurrent Neural Networks, Time Series, and Sequence Data/11. A More Challenging Sequence.mp4 160.6 MB
21. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/4. Machine Learning and AI Prerequisite Roadmap (pt 2).mp4 149.9 MB
21. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/3. Machine Learning and AI Prerequisite Roadmap (pt 1).mp4 147.4 MB
5. Feedforward Artificial Neural Networks/9. ANN for Image Classification.mp4 142.4 MB
4. Machine Learning and Neurons/9. Classification Notebook.mp4 140.5 MB
9. Recommender Systems/3. Recommender Systems with Deep Learning Code (pt 1).mp4 127.5 MB
3. Google Colab/2. Uploading your own data to Google Colab.mp4 126.8 MB
4. Machine Learning and Neurons/6. Moore's Law Notebook.mp4 126.6 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 119.0 MB
7. Recurrent Neural Networks, Time Series, and Sequence Data/3. Autoregressive Linear Model for Time Series Prediction.mp4 110.5 MB
5. Feedforward Artificial Neural Networks/10. ANN for Regression.mp4 103.9 MB
20. Extra Help With Python Coding for Beginners (FAQ by Student Request)/2. How to Code Yourself (part 1).mp4 103.7 MB
10. Transfer Learning for Computer Vision/5. Transfer Learning Code (pt 1).mp4 99.9 MB
9. Recommender Systems/4. Recommender Systems with Deep Learning Code (pt 2).mp4 97.0 MB
7. Recurrent Neural Networks, Time Series, and Sequence Data/8. Paying Attention to Shapes.mp4 91.7 MB
7. Recurrent Neural Networks, Time Series, and Sequence Data/16. Stock Return Predictions using LSTMs (pt 3).mp4 90.2 MB
8. Natural Language Processing (NLP)/10. (Legacy) VIP Making Predictions with a Trained NLP Model.mp4 87.6 MB
13. Stock Trading Project with Deep Reinforcement Learning/7. Code pt 3.mp4 83.2 MB
11. GANs (Generative Adversarial Networks)/3. GAN Code.mp4 81.5 MB
6. Convolutional Neural Networks/6. CNN Code Preparation (part 1).mp4 79.9 MB
7. Recurrent Neural Networks, Time Series, and Sequence Data/9. GRU and LSTM (pt 1).mp4 79.8 MB
6. Convolutional Neural Networks/1. What is Convolution (part 1).mp4 79.7 MB
10. Transfer Learning for Computer Vision/1. Transfer Learning Theory.mp4 77.0 MB
6. Convolutional Neural Networks/13. Improving CIFAR-10 Results.mp4 75.7 MB
6. Convolutional Neural Networks/4. Convolution on Color Images.mp4 75.7 MB
6. Convolutional Neural Networks/9. CNN for Fashion MNIST.mp4 73.8 MB
8. Natural Language Processing (NLP)/4. Beginner Blues - PyTorch NLP Version.mp4 73.3 MB
10. Transfer Learning for Computer Vision/6. Transfer Learning Code (pt 2).mp4 72.7 MB
7. Recurrent Neural Networks, Time Series, and Sequence Data/1. Sequence Data.mp4 71.4 MB
4. Machine Learning and Neurons/4. Regression Notebook.mp4 71.2 MB
7. Recurrent Neural Networks, Time Series, and Sequence Data/14. Stock Return Predictions using LSTMs (pt 1).mp4 70.0 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 69.1 MB
11. GANs (Generative Adversarial Networks)/1. GAN Theory.mp4 68.8 MB
5. Feedforward Artificial Neural Networks/8. Code Preparation (ANN).mp4 66.1 MB
4. Machine Learning and Neurons/7. Linear Classification Basics.mp4 65.9 MB
12. Deep Reinforcement Learning (Theory)/2. Elements of a Reinforcement Learning Problem.mp4 64.7 MB
22. Appendix FAQ Finale/2. BONUS.mp4 64.6 MB
2. Getting Set Up/2. How to use Github & Extra Coding Tips (Optional).mp4 63.9 MB
15. VIP Facial Recognition/7. Generating Generators.mp4 60.3 MB
7. Recurrent Neural Networks, Time Series, and Sequence Data/5. Recurrent Neural Networks.mp4 58.9 MB
8. Natural Language Processing (NLP)/8. CNNs for Text.mp4 58.4 MB
13. Stock Trading Project with Deep Reinforcement Learning/6. Code pt 2.mp4 57.8 MB
6. Convolutional Neural Networks/5. CNN Architecture.mp4 57.6 MB
4. Machine Learning and Neurons/2. Regression Basics.mp4 57.6 MB
3. Google Colab/3. Where can I learn about Numpy, Scipy, Matplotlib, Pandas, and Scikit-Learn.mp4 57.0 MB
3. Google Colab/1. Intro to Google Colab, how to use a GPU or TPU for free.mp4 56.0 MB
12. Deep Reinforcement Learning (Theory)/9. Solving the Bellman Equation with Reinforcement Learning (pt 2).mp4 55.5 MB
5. Feedforward Artificial Neural Networks/4. Activation Functions.mp4 55.3 MB
6. Convolutional Neural Networks/10. CNN for CIFAR-10.mp4 55.3 MB
17. In-Depth Gradient Descent/5. Adam (pt 1).mp4 55.1 MB
7. Recurrent Neural Networks, Time Series, and Sequence Data/15. Stock Return Predictions using LSTMs (pt 2).mp4 54.3 MB
4. Machine Learning and Neurons/1. What is Machine Learning.mp4 54.2 MB
13. Stock Trading Project with Deep Reinforcement Learning/5. Code pt 1.mp4 53.7 MB
7. Recurrent Neural Networks, Time Series, and Sequence Data/7. RNN for Time Series Prediction.mp4 53.0 MB
13. Stock Trading Project with Deep Reinforcement Learning/8. Code pt 4.mp4 52.6 MB
7. Recurrent Neural Networks, Time Series, and Sequence Data/10. GRU and LSTM (pt 2).mp4 50.4 MB
4. Machine Learning and Neurons/10. Saving and Loading a Model.mp4 49.9 MB
15. VIP Facial Recognition/4. Loading in the data.mp4 48.9 MB
20. Extra Help With Python Coding for Beginners (FAQ by Student Request)/1. Beginner's Coding Tips.mp4 48.8 MB
4. Machine Learning and Neurons/13. Model With Logits.mp4 48.4 MB
7. Recurrent Neural Networks, Time Series, and Sequence Data/2. Forecasting.mp4 48.4 MB
1. Introduction/2. Overview and Outline.mp4 48.3 MB
8. Natural Language Processing (NLP)/9. Text Classification with CNNs (V2).mp4 48.3 MB
9. Recommender Systems/1. Recommender Systems with Deep Learning Theory.mp4 46.4 MB
8. Natural Language Processing (NLP)/6. (Legacy) Text Preprocessing Code Example.mp4 46.2 MB
12. Deep Reinforcement Learning (Theory)/8. Solving the Bellman Equation with Reinforcement Learning (pt 1).mp4 45.9 MB
15. VIP Facial Recognition/9. Accuracy and imbalanced classes.mp4 45.7 MB
12. Deep Reinforcement Learning (Theory)/14. How to Learn Reinforcement Learning.mp4 45.5 MB
5. Feedforward Artificial Neural Networks/6. How to Represent Images.mp4 45.4 MB
9. Recommender Systems/5. VIP Making Predictions with a Trained Recommender Model.mp4 44.8 MB
12. Deep Reinforcement Learning (Theory)/11. Q-Learning.mp4 42.3 MB
17. In-Depth Gradient Descent/6. Adam (pt 2).mp4 42.3 MB
15. VIP Facial Recognition/6. Converting the data into pairs.mp4 41.6 MB
12. Deep Reinforcement Learning (Theory)/12. Deep Q-Learning DQN (pt 1).mp4 39.3 MB
7. Recurrent Neural Networks, Time Series, and Sequence Data/6. RNN Code Preparation.mp4 39.0 MB
8. Natural Language Processing (NLP)/1. Embeddings.mp4 37.2 MB
13. Stock Trading Project with Deep Reinforcement Learning/2. Data and Environment.mp4 36.6 MB
8. Natural Language Processing (NLP)/3. Text Preprocessing Concepts.mp4 35.2 MB
14. VIP Uncertainty Estimation/2. Estimating Prediction Uncertainty Code.mp4 35.0 MB
4. Machine Learning and Neurons/14. Train Sets vs. Validation Sets vs. Test Sets.mp4 33.7 MB
2. Getting Set Up/5. Temporary 403 Errors.mp4 33.6 MB
12. Deep Reinforcement Learning (Theory)/13. Deep Q-Learning DQN (pt 2).mp4 33.4 MB
20. Extra Help With Python Coding for Beginners (FAQ by Student Request)/3. How to Code Yourself (part 2).mp4 33.4 MB
4. Machine Learning and Neurons/12. How does a model learn.mp4 32.9 MB
9. Recommender Systems/2. Recommender Systems with Deep Learning Code Preparation.mp4 32.8 MB
5. Feedforward Artificial Neural Networks/3. The Geometrical Picture.mp4 32.8 MB
8. Natural Language Processing (NLP)/11. VIP Making Predictions with a Trained NLP Model (V2).mp4 32.6 MB
12. Deep Reinforcement Learning (Theory)/7. What does it mean to “learn”.mp4 31.6 MB
4. Machine Learning and Neurons/3. Regression Code Preparation.mp4 31.5 MB
5. Feedforward Artificial Neural Networks/5. Multiclass Classification.mp4 31.4 MB
15. VIP Facial Recognition/2. Siamese Networks.mp4 31.4 MB
12. Deep Reinforcement Learning (Theory)/4. Markov Decision Processes (MDPs).mp4 31.4 MB
8. Natural Language Processing (NLP)/5. (Legacy) Text Preprocessing Code Preparation.mp4 30.9 MB
4. Machine Learning and Neurons/11. A Short Neuroscience Primer.mp4 29.9 MB
6. Convolutional Neural Networks/3. What is Convolution (part 3).mp4 29.8 MB
5. Feedforward Artificial Neural Networks/2. Forward Propagation.mp4 29.7 MB
5. Feedforward Artificial Neural Networks/11. How to Choose Hyperparameters.mp4 29.6 MB
12. Deep Reinforcement Learning (Theory)/1. Deep Reinforcement Learning Section Introduction.mp4 29.4 MB
12. Deep Reinforcement Learning (Theory)/6. Value Functions and the Bellman Equation.mp4 28.9 MB
2. Getting Set Up/1. Get Your Hands Dirty, Practical Coding Experience, Data Links.mp4 28.9 MB
1. Introduction/1. Welcome.mp4 28.9 MB
6. Convolutional Neural Networks/11. Data Augmentation.mp4 28.9 MB
17. In-Depth Gradient Descent/3. Momentum.mp4 28.7 MB
14. VIP Uncertainty Estimation/1. Custom Loss and Estimating Prediction Uncertainty.mp4 28.3 MB
12. Deep Reinforcement Learning (Theory)/3. States, Actions, Rewards, Policies.mp4 28.1 MB
17. In-Depth Gradient Descent/2. Stochastic Gradient Descent.mp4 28.0 MB
17. In-Depth Gradient Descent/4. Variable and Adaptive Learning Rates.mp4 27.4 MB
4. Machine Learning and Neurons/5. Moore's Law.mp4 27.4 MB
21. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/1. How to Succeed in this Course (Long Version).mp4 27.4 MB
4. Machine Learning and Neurons/15. Suggestion Box.mp4 27.2 MB
15. VIP Facial Recognition/8. Creating the model and loss.mp4 26.7 MB
7. Recurrent Neural Networks, Time Series, and Sequence Data/13. RNN for Image Classification (Code).mp4 26.0 MB
10. Transfer Learning for Computer Vision/3. Large Datasets.mp4 25.2 MB
16. In-Depth Loss Functions/1. Mean Squared Error.mp4 24.3 MB
6. Convolutional Neural Networks/2. What is Convolution (part 2).mp4 24.1 MB
6. Convolutional Neural Networks/7. CNN Code Preparation (part 2).mp4 23.9 MB
17. In-Depth Gradient Descent/1. Gradient Descent.mp4 23.8 MB
12. Deep Reinforcement Learning (Theory)/10. Epsilon-Greedy.mp4 23.4 MB
16. In-Depth Loss Functions/3. Categorical Cross Entropy.mp4 22.6 MB
15. VIP Facial Recognition/5. Splitting the data into train and test.mp4 22.6 MB
7. Recurrent Neural Networks, Time Series, and Sequence Data/12. RNN for Image Classification (Theory).mp4 20.3 MB
5. Feedforward Artificial Neural Networks/1. Artificial Neural Networks Section Introduction.mp4 20.3 MB
6. Convolutional Neural Networks/8. CNN Code Preparation (part 3).mp4 20.0 MB
18. Extras/1. Where Are The Exercises.mp4 19.7 MB
4. Machine Learning and Neurons/8. Classification Code Preparation.mp4 18.5 MB
13. Stock Trading Project with Deep Reinforcement Learning/4. Program Design and Layout.mp4 18.3 MB
7. Recurrent Neural Networks, Time Series, and Sequence Data/17. Other Ways to Forecast.mp4 18.1 MB
11. GANs (Generative Adversarial Networks)/2. GAN Code Preparation.mp4 18.1 MB
10. Transfer Learning for Computer Vision/2. Some Pre-trained Models (VGG, ResNet, Inception, MobileNet).mp4 18.0 MB
13. Stock Trading Project with Deep Reinforcement Learning/1. Reinforcement Learning Stock Trader Introduction.mp4 17.9 MB
2. Getting Set Up/3. Where to get the code, notebooks, and data.mp4 17.8 MB
10. Transfer Learning for Computer Vision/4. 2 Approaches to Transfer Learning.mp4 17.2 MB
2. Getting Set Up/4. How to Succeed in This Course.mp4 16.2 MB
13. Stock Trading Project with Deep Reinforcement Learning/3. Replay Buffer.mp4 16.0 MB
19. Setting up your Environment (FAQ by Student Request)/1. Pre-Installation Check.mp4 15.1 MB
16. In-Depth Loss Functions/2. Binary Cross Entropy.mp4 15.0 MB
6. Convolutional Neural Networks/12. Batch Normalization.mp4 14.7 MB
12. Deep Reinforcement Learning (Theory)/5. The Return.mp4 14.3 MB
15. VIP Facial Recognition/3. Code Outline.mp4 14.2 MB
15. VIP Facial Recognition/1. Facial Recognition Section Introduction.mp4 13.8 MB
7. Recurrent Neural Networks, Time Series, and Sequence Data/4. Proof that the Linear Model Works.mp4 11.4 MB
15. VIP Facial Recognition/10. Facial Recognition Section Summary.mp4 11.3 MB
8. Natural Language Processing (NLP)/2. Neural Networks with Embeddings.mp4 10.7 MB
13. Stock Trading Project with Deep Reinforcement Learning/9. Reinforcement Learning Stock Trader Discussion.mp4 10.5 MB
22. Appendix FAQ Finale/1. What is the Appendix.mp4 10.1 MB
5. Feedforward Artificial Neural Networks/7. Color Mixing Clarification.mp4 3.1 MB
19. Setting up your Environment (FAQ by Student Request)/4. Installing NVIDIA GPU-Accelerated Deep Learning Libraries on your Home Computer.srt 32.0 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 31.6 KB
7. Recurrent Neural Networks, Time Series, and Sequence Data/1. Sequence Data.srt 29.6 KB
6. Convolutional Neural Networks/5. CNN Architecture.srt 27.8 KB
12. Deep Reinforcement Learning (Theory)/2. Elements of a Reinforcement Learning Problem.srt 26.2 KB
7. Recurrent Neural Networks, Time Series, and Sequence Data/5. Recurrent Neural Networks.srt 25.7 KB
6. Convolutional Neural Networks/6. CNN Code Preparation (part 1).srt 24.5 KB
20. Extra Help With Python Coding for Beginners (FAQ by Student Request)/2. How to Code Yourself (part 1).srt 23.4 KB
21. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/4. Machine Learning and AI Prerequisite Roadmap (pt 2).srt 23.0 KB
7. Recurrent Neural Networks, Time Series, and Sequence Data/9. GRU and LSTM (pt 1).srt 22.8 KB
5. Feedforward Artificial Neural Networks/4. Activation Functions.srt 22.6 KB
5. Feedforward Artificial Neural Networks/9. ANN for Image Classification.srt 22.6 KB
6. Convolutional Neural Networks/1. What is Convolution (part 1).srt 21.2 KB
11. GANs (Generative Adversarial Networks)/1. GAN Theory.srt 21.1 KB
6. Convolutional Neural Networks/4. Convolution on Color Images.srt 21.0 KB
4. Machine Learning and Neurons/7. Linear Classification Basics.srt 20.5 KB
5. Feedforward Artificial Neural Networks/8. Code Preparation (ANN).srt 20.5 KB
8. Natural Language Processing (NLP)/7. Text Classification with LSTMs (V2).srt 20.5 KB
4. Machine Learning and Neurons/2. Regression Basics.srt 20.1 KB
19. Setting up your Environment (FAQ by Student Request)/3. Anaconda Environment Setup.srt 20.0 KB
20. Extra Help With Python Coding for Beginners (FAQ by Student Request)/1. Beginner's Coding Tips.srt 19.0 KB
4. Machine Learning and Neurons/1. What is Machine Learning.srt 18.4 KB
12. Deep Reinforcement Learning (Theory)/11. Q-Learning.srt 17.9 KB
8. Natural Language Processing (NLP)/3. Text Preprocessing Concepts.srt 17.9 KB
1. Introduction/2. Overview and Outline.srt 17.7 KB
7. Recurrent Neural Networks, Time Series, and Sequence Data/6. RNN Code Preparation.srt 17.6 KB
4. Machine Learning and Neurons/4. Regression Notebook.srt 17.5 KB
9. Recommender Systems/4. Recommender Systems with Deep Learning Code (pt 2).srt 17.4 KB
17. In-Depth Gradient Descent/5. Adam (pt 1).srt 16.7 KB
12. Deep Reinforcement Learning (Theory)/12. Deep Q-Learning DQN (pt 1).srt 16.4 KB
4. Machine Learning and Neurons/3. Regression Code Preparation.srt 16.4 KB
8. Natural Language Processing (NLP)/1. Embeddings.srt 16.1 KB
21. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/3. Machine Learning and AI Prerequisite Roadmap (pt 1).srt 16.1 KB
8. Natural Language Processing (NLP)/8. CNNs for Text.srt 16.0 KB
7. Recurrent Neural Networks, Time Series, and Sequence Data/14. Stock Return Predictions using LSTMs (pt 1).srt 16.0 KB
4. Machine Learning and Neurons/6. Moore's Law Notebook.srt 15.8 KB
2. Getting Set Up/2. How to use Github & Extra Coding Tips (Optional).srt 15.7 KB
13. Stock Trading Project with Deep Reinforcement Learning/2. Data and Environment.srt 15.7 KB
12. Deep Reinforcement Learning (Theory)/9. Solving the Bellman Equation with Reinforcement Learning (pt 2).srt 15.5 KB
3. Google Colab/3. Where can I learn about Numpy, Scipy, Matplotlib, Pandas, and Scikit-Learn.srt 15.4 KB
8. Natural Language Processing (NLP)/5. (Legacy) Text Preprocessing Code Preparation.srt 15.3 KB
5. Feedforward Artificial Neural Networks/6. How to Represent Images.srt 15.3 KB
17. In-Depth Gradient Descent/4. Variable and Adaptive Learning Rates.srt 15.2 KB
7. Recurrent Neural Networks, Time Series, and Sequence Data/10. GRU and LSTM (pt 2).srt 14.9 KB
19. Setting up your Environment (FAQ by Student Request)/2. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.srt 14.7 KB
7. Recurrent Neural Networks, Time Series, and Sequence Data/3. Autoregressive Linear Model for Time Series Prediction.srt 14.7 KB
21. Effective Learning Strategies for Machine Learning (FAQ by Student Request)/1. How to Succeed in this Course (Long Version).srt 14.6 KB
4. Machine Learning and Neurons/9. Classification Notebook.srt 14.6 KB
8. Natural Language Processing (NLP)/4. Beginner Blues - PyTorch NLP Version.srt 14.5 KB
17. In-Depth Gradient Descent/6. Adam (pt 2).srt 14.5 KB
3. Google Colab/2. Uploading your own data to Google Colab.srt 14.5 KB
7. Recurrent Neural Networks, Time Series, and Sequence Data/16. Stock Return Predictions using LSTMs (pt 3).srt 14.4 KB
3. Google Colab/1. Intro to Google Colab, how to use a GPU or TPU for free.srt 14.3 KB
4. Machine Learning and Neurons/14. Train Sets vs. Validation Sets vs. Test Sets.srt 14.3 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 14.2 KB
7. Recurrent Neural Networks, Time Series, and Sequence Data/2. Forecasting.srt 13.9 KB
4. Machine Learning and Neurons/12. How does a model learn.srt 13.8 KB
9. Recommender Systems/1. Recommender Systems with Deep Learning Theory.srt 13.7 KB
6. Convolutional Neural Networks/9. CNN for Fashion MNIST.srt 13.4 KB
12. Deep Reinforcement Learning (Theory)/13. Deep Q-Learning DQN (pt 2).srt 13.2 KB
5. Feedforward Artificial Neural Networks/10. ANN for Regression.srt 13.0 KB
20. Extra Help With Python Coding for Beginners (FAQ by Student Request)/3. How to Code Yourself (part 2).srt 13.0 KB
12. Deep Reinforcement Learning (Theory)/8. Solving the Bellman Equation with Reinforcement Learning (pt 1).srt 12.9 KB
15. VIP Facial Recognition/2. Siamese Networks.srt 12.8 KB
6. Convolutional Neural Networks/13. Improving CIFAR-10 Results.srt 12.8 KB
14. VIP Uncertainty Estimation/1. Custom Loss and Estimating Prediction Uncertainty.srt 12.8 KB
9. Recommender Systems/2. Recommender Systems with Deep Learning Code Preparation.srt 12.7 KB
12. Deep Reinforcement Learning (Theory)/4. Markov Decision Processes (MDPs).srt 12.7 KB
6. Convolutional Neural Networks/11. Data Augmentation.srt 12.5 KB
12. Deep Reinforcement Learning (Theory)/6. Value Functions and the Bellman Equation.srt 12.5 KB
4. Machine Learning and Neurons/11. A Short Neuroscience Primer.srt 12.3 KB
5. Feedforward Artificial Neural Networks/2. Forward Propagation.srt 12.2 KB
5. Feedforward Artificial Neural Networks/5. Multiclass Classification.srt 12.2 KB
13. Stock Trading Project with Deep Reinforcement Learning/5. Code pt 1.srt 12.1 KB
2. Getting Set Up/1. Get Your Hands Dirty, Practical Coding Experience, Data Links.srt 12.0 KB
13. Stock Trading Project with Deep Reinforcement Learning/6. Code pt 2.srt 11.8 KB
10. Transfer Learning for Computer Vision/5. Transfer Learning Code (pt 1).srt 11.6 KB
5. Feedforward Artificial Neural Networks/3. The Geometrical Picture.srt 11.5 KB
12. Deep Reinforcement Learning (Theory)/3. States, Actions, Rewards, Policies.srt 11.3 KB
16. In-Depth Loss Functions/1. Mean Squared Error.srt 11.2 KB
7. Recurrent Neural Networks, Time Series, and Sequence Data/8. Paying Attention to Shapes.srt 11.0 KB
9. Recommender Systems/3. Recommender Systems with Deep Learning Code (pt 1).srt 10.9 KB
10. Transfer Learning for Computer Vision/1. Transfer Learning Theory.srt 10.7 KB
7. Recurrent Neural Networks, Time Series, and Sequence Data/11. A More Challenging Sequence.srt 10.7 KB
11. GANs (Generative Adversarial Networks)/3. GAN Code.srt 10.7 KB
6. Convolutional Neural Networks/7. CNN Code Preparation (part 2).srt 10.4 KB
7. Recurrent Neural Networks, Time Series, and Sequence Data/7. RNN for Time Series Prediction.srt 9.9 KB
17. In-Depth Gradient Descent/1. Gradient Descent.srt 9.8 KB
16. In-Depth Loss Functions/3. Categorical Cross Entropy.srt 9.6 KB
15. VIP Facial Recognition/9. Accuracy and imbalanced classes.srt 9.5 KB
8. Natural Language Processing (NLP)/6. (Legacy) Text Preprocessing Code Example.srt 9.4 KB
4. Machine Learning and Neurons/8. Classification Code Preparation.srt 9.4 KB
4. Machine Learning and Neurons/5. Moore's Law.srt 9.1 KB
8. Natural Language Processing (NLP)/10. (Legacy) VIP Making Predictions with a Trained NLP Model.srt 9.1 KB
10. Transfer Learning for Computer Vision/3. Large Datasets.srt 9.1 KB
6. Convolutional Neural Networks/10. CNN for CIFAR-10.srt 8.9 KB
12. Deep Reinforcement Learning (Theory)/7. What does it mean to “learn”.srt 8.9 KB
14. VIP Uncertainty Estimation/2. Estimating Prediction Uncertainty Code.srt 8.8 KB
10. Transfer Learning for Computer Vision/6. Transfer Learning Code (pt 2).srt 8.8 KB
5. Feedforward Artificial Neural Networks/11. How to Choose Hyperparameters.srt 8.7 KB
13. Stock Trading Project with Deep Reinforcement Learning/4. Program Design and Layout.srt 8.6 KB
12. Deep Reinforcement Learning (Theory)/1. Deep Reinforcement Learning Section Introduction.srt 8.6 KB
11. GANs (Generative Adversarial Networks)/2. GAN Code Preparation.srt 8.5 KB
13. Stock Trading Project with Deep Reinforcement Learning/7. Code pt 3.srt 8.4 KB
13. Stock Trading Project with Deep Reinforcement Learning/8. Code pt 4.srt 8.4 KB
6. Convolutional Neural Networks/3. What is Convolution (part 3).srt 8.1 KB
5. Feedforward Artificial Neural Networks/1. Artificial Neural Networks Section Introduction.srt 7.9 KB
17. In-Depth Gradient Descent/3. Momentum.srt 7.8 KB
22. Appendix FAQ Finale/2. BONUS.srt 7.8 KB
12. Deep Reinforcement Learning (Theory)/14. How to Learn Reinforcement Learning.srt 7.6 KB
12. Deep Reinforcement Learning (Theory)/10. Epsilon-Greedy.srt 7.4 KB
16. In-Depth Loss Functions/2. Binary Cross Entropy.srt 7.3 KB
6. Convolutional Neural Networks/2. What is Convolution (part 2).srt 7.2 KB
6. Convolutional Neural Networks/8. CNN Code Preparation (part 3).srt 7.2 KB
7. Recurrent Neural Networks, Time Series, and Sequence Data/17. Other Ways to Forecast.srt 7.2 KB
8. Natural Language Processing (NLP)/9. Text Classification with CNNs (V2).srt 7.1 KB
13. Stock Trading Project with Deep Reinforcement Learning/3. Replay Buffer.srt 6.9 KB
15. VIP Facial Recognition/4. Loading in the data.srt 6.9 KB
7. Recurrent Neural Networks, Time Series, and Sequence Data/15. Stock Return Predictions using LSTMs (pt 2).srt 6.8 KB
13. Stock Trading Project with Deep Reinforcement Learning/1. Reinforcement Learning Stock Trader Introduction.srt 6.8 KB
4. Machine Learning and Neurons/10. Saving and Loading a Model.srt 6.6 KB
19. Setting up your Environment (FAQ by Student Request)/1. Pre-Installation Check.srt 6.6 KB
6. Convolutional Neural Networks/12. Batch Normalization.srt 6.6 KB
12. Deep Reinforcement Learning (Theory)/5. The Return.srt 6.3 KB
9. Recommender Systems/5. VIP Making Predictions with a Trained Recommender Model.srt 6.0 KB
7. Recurrent Neural Networks, Time Series, and Sequence Data/12. RNN for Image Classification (Theory).srt 6.0 KB
10. Transfer Learning for Computer Vision/4. 2 Approaches to Transfer Learning.srt 6.0 KB
15. VIP Facial Recognition/3. Code Outline.srt 5.8 KB
15. VIP Facial Recognition/6. Converting the data into pairs.srt 5.8 KB
15. VIP Facial Recognition/7. Generating Generators.srt 5.7 KB
1. Introduction/1. Welcome.srt 5.7 KB
18. Extras/1. Where Are The Exercises.srt 5.4 KB
17. In-Depth Gradient Descent/2. Stochastic Gradient Descent.srt 5.4 KB
15. VIP Facial Recognition/8. Creating the model and loss.srt 5.4 KB
8. Natural Language Processing (NLP)/11. VIP Making Predictions with a Trained NLP Model (V2).srt 5.3 KB
4. Machine Learning and Neurons/13. Model With Logits.srt 5.3 KB
10. Transfer Learning for Computer Vision/2. Some Pre-trained Models (VGG, ResNet, Inception, MobileNet).srt 5.2 KB
15. VIP Facial Recognition/5. Splitting the data into train and test.srt 5.1 KB
4. Machine Learning and Neurons/15. Suggestion Box.srt 4.7 KB
15. VIP Facial Recognition/1. Facial Recognition Section Introduction.srt 4.6 KB
7. Recurrent Neural Networks, Time Series, and Sequence Data/4. Proof that the Linear Model Works.srt 4.6 KB
8. Natural Language Processing (NLP)/2. Neural Networks with Embeddings.srt 4.5 KB
2. Getting Set Up/4. How to Succeed in This Course.srt 4.4 KB
15. VIP Facial Recognition/10. Facial Recognition Section Summary.srt 4.4 KB
13. Stock Trading Project with Deep Reinforcement Learning/9. Reinforcement Learning Stock Trader Discussion.srt 4.4 KB
2. Getting Set Up/3. Where to get the code, notebooks, and data.srt 4.3 KB
22. Appendix FAQ Finale/1. What is the Appendix.srt 3.7 KB
2. Getting Set Up/5. Temporary 403 Errors.srt 3.7 KB
7. Recurrent Neural Networks, Time Series, and Sequence Data/13. RNN for Image Classification (Code).srt 3.3 KB
5. Feedforward Artificial Neural Networks/7. Color Mixing Clarification.srt 1.1 KB
2. Getting Set Up/1.1 Data Links.html 157 bytes
2. Getting Set Up/3.2 Data Links.html 157 bytes
2. Getting Set Up/1.2 Github Link.html 140 bytes
2. Getting Set Up/3.3 Github Link.html 140 bytes
0. Websites you may like/[FreeCourseSite.com].url 127 bytes
13. Stock Trading Project with Deep Reinforcement Learning/0. Websites you may like/[FreeCourseSite.com].url 127 bytes
20. Extra Help With Python Coding for Beginners (FAQ by Student Request)/0. Websites you may like/[FreeCourseSite.com].url 127 bytes
3. Google Colab/0. Websites you may like/[FreeCourseSite.com].url 127 bytes
5. Feedforward Artificial Neural Networks/0. Websites you may like/[FreeCourseSite.com].url 127 bytes
7. Recurrent Neural Networks, Time Series, and Sequence Data/0. Websites you may like/[FreeCourseSite.com].url 127 bytes
9. Recommender Systems/0. Websites you may like/[FreeCourseSite.com].url 127 bytes
2. Getting Set Up/3.1 Code Link.html 125 bytes
0. Websites you may like/[CourseClub.Me].url 122 bytes
13. Stock Trading Project with Deep Reinforcement Learning/0. Websites you may like/[CourseClub.Me].url 122 bytes
20. Extra Help With Python Coding for Beginners (FAQ by Student Request)/0. Websites you may like/[CourseClub.Me].url 122 bytes
3. Google Colab/0. Websites you may like/[CourseClub.Me].url 122 bytes
5. Feedforward Artificial Neural Networks/0. Websites you may like/[CourseClub.Me].url 122 bytes
7. Recurrent Neural Networks, Time Series, and Sequence Data/0. Websites you may like/[CourseClub.Me].url 122 bytes
9. Recommender Systems/0. Websites you may like/[CourseClub.Me].url 122 bytes
8. Natural Language Processing (NLP)/4.1 Why bad programmers always need the latest version.html 89 bytes
0. Websites you may like/[GigaCourse.Com].url 49 bytes
13. Stock Trading Project with Deep Reinforcement Learning/0. Websites you may like/[GigaCourse.Com].url 49 bytes
20. Extra Help With Python Coding for Beginners (FAQ by Student Request)/0. Websites you may like/[GigaCourse.Com].url 49 bytes
3. Google Colab/0. Websites you may like/[GigaCourse.Com].url 49 bytes
5. Feedforward Artificial Neural Networks/0. Websites you may like/[GigaCourse.Com].url 49 bytes
7. Recurrent Neural Networks, Time Series, and Sequence Data/0. Websites you may like/[GigaCourse.Com].url 49 bytes
9. Recommender Systems/0. Websites you may like/[GigaCourse.Com].url 49 bytes

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

File List

Download Torrent

Related Resources

Copyright Infringement