Udemy - PyTorch Deep Learning and Artificial Intelligence (12.2024)

Creation Time	July 7, 2025, 1:24 a.m.
Last Access Time	July 7, 2025, 1:51 p.m.
File Size	7.8 GB
Keywords	and 12 Intelligence - Deep Udemy 2024 PyTorch Artificial Learning
Total Requests	14
Total Files	306
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19 - Setting up your Environment (FAQ by Student Request)/3 -Anaconda Environment Setup.mp4 180.8 MB
19 - Setting up your Environment (FAQ by Student Request)/4 -Installing NVIDIA GPU-Accelerated Deep Learning Libraries on your Home Computer.mp4 167.2 MB
19 - Setting up your Environment (FAQ by Student Request)/2 -How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4 150.7 MB
8 - Natural Language Processing (NLP)/7 -Text Classification with LSTMs (V2).mp4 117.2 MB
7 - Recurrent Neural Networks, Time Series, and Sequence Data/1 -Sequence Data.mp4 114.2 MB
21 - Effective Learning Strategies for Machine Learning (FAQ by Student Request)/4 -Machine Learning and AI Prerequisite Roadmap (pt 2).mp4 108.1 MB
5 - Feedforward Artificial Neural Networks/9 -ANN for Image Classification.mp4 106.4 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 105.6 MB
12 - Deep Reinforcement Learning (Theory)/2 -Elements of a Reinforcement Learning Problem.mp4 105.0 MB
7 - Recurrent Neural Networks, Time Series, and Sequence Data/5 -Recurrent Neural Networks.mp4 92.6 MB
11 - GANs (Generative Adversarial Networks)/1 -GAN Theory.mp4 92.0 MB
3 - Google Colab/2 -Uploading your own data to Google Colab.mp4 90.5 MB
6 - Convolutional Neural Networks/5 -CNN Architecture.mp4 89.5 MB
5 - Feedforward Artificial Neural Networks/4 -Activation Functions.mp4 89.2 MB
7 - Recurrent Neural Networks, Time Series, and Sequence Data/11 -A More Challenging Sequence.mp4 87.2 MB
7 - Recurrent Neural Networks, Time Series, and Sequence Data/3 -Autoregressive Linear Model for Time Series Prediction.mp4 81.2 MB
5 - Feedforward Artificial Neural Networks/10 -ANN for Regression.mp4 80.1 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
21 - Effective Learning Strategies for Machine Learning (FAQ by Student Request)/3 -Machine Learning and AI Prerequisite Roadmap (pt 1).mp4 79.8 MB
6 - Convolutional Neural Networks/1 -What is Convolution (part 1).mp4 79.7 MB
1 - Introduction/2 -Overview and Outline.mp4 79.6 MB
4 - Machine Learning and Neurons/6 -Moore's Law Notebook.mp4 78.9 MB
4 - Machine Learning and Neurons/9 -Classification Notebook.mp4 78.3 MB
10 - Transfer Learning for Computer Vision/5 -Transfer Learning Code (pt 1).mp4 77.8 MB
7 - Recurrent Neural Networks, Time Series, and Sequence Data/14 -Stock Return Predictions using LSTMs (pt 1).mp4 77.8 MB
9 - Recommender Systems/4 -Recommender Systems with Deep Learning Code (pt 2).mp4 76.8 MB
6 - Convolutional Neural Networks/13 -Improving CIFAR-10 Results.mp4 75.7 MB
20 - Extra Help With Python Coding for Beginners (FAQ by Student Request)/1 -Beginner's Coding Tips.mp4 75.7 MB
6 - Convolutional Neural Networks/4 -Convolution on Color Images.mp4 75.6 MB
5 - Feedforward Artificial Neural Networks/6 -How to Represent Images.mp4 75.4 MB
6 - Convolutional Neural Networks/9 -CNN for Fashion MNIST.mp4 73.8 MB
4 - Machine Learning and Neurons/2 -Regression Basics.mp4 73.0 MB
4 - Machine Learning and Neurons/4 -Regression Notebook.mp4 71.9 MB
20 - Extra Help With Python Coding for Beginners (FAQ by Student Request)/2 -How to Code Yourself (part 1).mp4 71.8 MB
7 - Recurrent Neural Networks, Time Series, and Sequence Data/7 -RNN for Time Series Prediction.mp4 71.7 MB
7 - Recurrent Neural Networks, Time Series, and Sequence Data/16 -Stock Return Predictions using LSTMs (pt 3).mp4 71.1 MB
4 - Machine Learning and Neurons/1 -What is Machine Learning.mp4 70.5 MB
13 - Stock Trading Project with Deep Reinforcement Learning/6 -Code pt 2.mp4 70.0 MB
9 - Recommender Systems/3 -Recommender Systems with Deep Learning Code (pt 1).mp4 69.5 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 69.4 MB
12 - Deep Reinforcement Learning (Theory)/11 -Q-Learning.mp4 66.7 MB
13 - Stock Trading Project with Deep Reinforcement Learning/5 -Code pt 1.mp4 66.3 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
9 - Recommender Systems/1 -Recommender Systems with Deep Learning Theory.mp4 64.7 MB
8 - Natural Language Processing (NLP)/4 -Beginner Blues - PyTorch NLP Version.mp4 64.1 MB
20 - Extra Help With Python Coding for Beginners (FAQ by Student Request)/6 -How to use Github & Extra Coding Tips (Optional).mp4 63.9 MB
11 - GANs (Generative Adversarial Networks)/3 -GAN Code.mp4 61.5 MB
3 - Google Colab/1 -Intro to Google Colab, how to use a GPU or TPU for free.mp4 60.4 MB
12 - Deep Reinforcement Learning (Theory)/12 -Deep Q-Learning DQN (pt 1).mp4 60.1 MB
8 - Natural Language Processing (NLP)/1 -Embeddings.mp4 59.9 MB
13 - Stock Trading Project with Deep Reinforcement Learning/7 -Code pt 3.mp4 58.6 MB
8 - Natural Language Processing (NLP)/8 -CNNs for Text.mp4 58.4 MB
10 - Transfer Learning for Computer Vision/1 -Transfer Learning Theory.mp4 58.1 MB
3 - Google Colab/3 -Where can I learn about Numpy, Scipy, Matplotlib, Pandas, and Scikit-Learn.mp4 57.0 MB
5 - Feedforward Artificial Neural Networks/3 -The Geometrical Picture.mp4 56.4 MB
7 - Recurrent Neural Networks, Time Series, and Sequence Data/8 -Paying Attention to Shapes.mp4 56.3 MB
10 - Transfer Learning for Computer Vision/6 -Transfer Learning Code (pt 2).mp4 56.3 MB
13 - Stock Trading Project with Deep Reinforcement Learning/2 -Data and Environment.mp4 55.7 MB
12 - Deep Reinforcement Learning (Theory)/9 -Solving the Bellman Equation with Reinforcement Learning (pt 2).mp4 55.5 MB
6 - Convolutional Neural Networks/10 -CNN for CIFAR-10.mp4 55.3 MB
7 - Recurrent Neural Networks, Time Series, and Sequence Data/6 -RNN Code Preparation.mp4 55.3 MB
17 - In-Depth Gradient Descent/5 -Adam (pt 1).mp4 55.1 MB
8 - Natural Language Processing (NLP)/9 -Text Classification with CNNs (V2).mp4 54.5 MB
17 - In-Depth Gradient Descent/6 -Adam (pt 2).mp4 52.8 MB
13 - Stock Trading Project with Deep Reinforcement Learning/8 -Code pt 4.mp4 52.6 MB
8 - Natural Language Processing (NLP)/3 -Text Preprocessing Concepts.mp4 52.3 MB
4 - Machine Learning and Neurons/14 -Train Sets vs. Validation Sets vs. Test Sets.mp4 52.2 MB
12 - Deep Reinforcement Learning (Theory)/13 -Deep Q-Learning DQN (pt 2).mp4 52.1 MB
15 - VIP Facial Recognition/9 -Accuracy and imbalanced classes.mp4 51.2 MB
12 - Deep Reinforcement Learning (Theory)/4 -Markov Decision Processes (MDPs).mp4 50.4 MB
7 - Recurrent Neural Networks, Time Series, and Sequence Data/10 -GRU and LSTM (pt 2).mp4 50.4 MB
15 - VIP Facial Recognition/2 -Siamese Networks.mp4 50.4 MB
4 - Machine Learning and Neurons/12 -How does a model learn.mp4 50.1 MB
20 - Extra Help With Python Coding for Beginners (FAQ by Student Request)/3 -How to Code Yourself (part 2).mp4 49.1 MB
8 - Natural Language Processing (NLP)/10 -(Legacy) VIP Making Predictions with a Trained NLP Model.mp4 48.8 MB
5 - Feedforward Artificial Neural Networks/5 -Multiclass Classification.mp4 48.6 MB
7 - Recurrent Neural Networks, Time Series, and Sequence Data/2 -Forecasting.mp4 48.4 MB
8 - Natural Language Processing (NLP)/6 -(Legacy) Text Preprocessing Code Example.mp4 47.8 MB
12 - Deep Reinforcement Learning (Theory)/6 -Value Functions and the Bellman Equation.mp4 47.7 MB
5 - Feedforward Artificial Neural Networks/2 -Forward Propagation.mp4 47.0 MB
12 - Deep Reinforcement Learning (Theory)/8 -Solving the Bellman Equation with Reinforcement Learning (pt 1).mp4 45.8 MB
4 - Machine Learning and Neurons/3 -Regression Code Preparation.mp4 45.5 MB
4 - Machine Learning and Neurons/11 -A Short Neuroscience Primer.mp4 44.6 MB
6 - Convolutional Neural Networks/11 -Data Augmentation.mp4 44.4 MB
8 - Natural Language Processing (NLP)/5 -(Legacy) Text Preprocessing Code Preparation.mp4 44.3 MB
12 - Deep Reinforcement Learning (Theory)/3 -States, Actions, Rewards, Policies.mp4 44.1 MB
20 - Extra Help With Python Coding for Beginners (FAQ by Student Request)/5 -Get Your Hands Dirty, Practical Coding Experience, Data Links.mp4 43.6 MB
14 - VIP Uncertainty Estimation/1 -Custom Loss and Estimating Prediction Uncertainty.mp4 43.5 MB
7 - Recurrent Neural Networks, Time Series, and Sequence Data/15 -Stock Return Predictions using LSTMs (pt 2).mp4 43.2 MB
14 - VIP Uncertainty Estimation/2 -Estimating Prediction Uncertainty Code.mp4 42.7 MB
12 - Deep Reinforcement Learning (Theory)/10 -Epsilon-Greedy.mp4 41.5 MB
10 - Transfer Learning for Computer Vision/3 -Large Datasets.mp4 41.2 MB
12 - Deep Reinforcement Learning (Theory)/1 -Deep Reinforcement Learning Section Introduction.mp4 40.7 MB
22 - Appendix FAQ Finale/2 -BONUS.mp4 40.4 MB
12 - Deep Reinforcement Learning (Theory)/14 -How to Learn Reinforcement Learning.mp4 40.3 MB
9 - Recommender Systems/2 -Recommender Systems with Deep Learning Code Preparation.mp4 40.1 MB
5 - Feedforward Artificial Neural Networks/11 -How to Choose Hyperparameters.mp4 39.5 MB
6 - Convolutional Neural Networks/7 -CNN Code Preparation (part 2).mp4 36.7 MB
1 - Introduction/1 -Welcome.mp4 35.7 MB
21 - Effective Learning Strategies for Machine Learning (FAQ by Student Request)/1 -How to Succeed in this Course (Long Version).mp4 35.2 MB
15 - VIP Facial Recognition/4 -Loading in the data.mp4 35.0 MB
17 - In-Depth Gradient Descent/1 -Gradient Descent.mp4 34.9 MB
17 - In-Depth Gradient Descent/4 -Variable and Adaptive Learning Rates.mp4 34.8 MB
17 - In-Depth Gradient Descent/3 -Momentum.mp4 34.3 MB
16 - In-Depth Loss Functions/1 -Mean Squared Error.mp4 33.8 MB
6 - Convolutional Neural Networks/8 -CNN Code Preparation (part 3).mp4 33.7 MB
5 - Feedforward Artificial Neural Networks/1 -Artificial Neural Networks Section Introduction.mp4 33.4 MB
9 - Recommender Systems/5 -VIP Making Predictions with a Trained Recommender Model.mp4 32.7 MB
15 - VIP Facial Recognition/7 -Generating Generators.mp4 32.5 MB
7 - Recurrent Neural Networks, Time Series, and Sequence Data/12 -RNN for Image Classification (Theory).mp4 32.2 MB
16 - In-Depth Loss Functions/3 -Categorical Cross Entropy.mp4 31.7 MB
12 - Deep Reinforcement Learning (Theory)/7 -What does it mean to “learn”.mp4 31.6 MB
4 - Machine Learning and Neurons/5 -Moore's Law.mp4 30.6 MB
15 - VIP Facial Recognition/6 -Converting the data into pairs.mp4 30.4 MB
6 - Convolutional Neural Networks/3 -What is Convolution (part 3).mp4 29.8 MB
15 - VIP Facial Recognition/8 -Creating the model and loss.mp4 29.4 MB
13 - Stock Trading Project with Deep Reinforcement Learning/1 -Reinforcement Learning Stock Trader Introduction.mp4 28.8 MB
4 - Machine Learning and Neurons/10 -Saving and Loading a Model.mp4 28.8 MB
7 - Recurrent Neural Networks, Time Series, and Sequence Data/17 -Other Ways to Forecast.mp4 28.3 MB
11 - GANs (Generative Adversarial Networks)/2 -GAN Code Preparation.mp4 28.1 MB
4 - Machine Learning and Neurons/13 -Model With Logits.mp4 27.2 MB
4 - Machine Learning and Neurons/15 -Suggestion Box.mp4 27.1 MB
2 - Getting Set Up/1 -Where to get the code, notebooks, and data.mp4 26.9 MB
13 - Stock Trading Project with Deep Reinforcement Learning/4 -Program Design and Layout.mp4 26.8 MB
4 - Machine Learning and Neurons/8 -Classification Code Preparation.mp4 26.6 MB
15 - VIP Facial Recognition/5 -Splitting the data into train and test.mp4 26.3 MB
18 - Extras/1 -Where Are The Exercises.mp4 25.9 MB
8 - Natural Language Processing (NLP)/11 -VIP Making Predictions with a Trained NLP Model (V2).mp4 25.5 MB
13 - Stock Trading Project with Deep Reinforcement Learning/3 -Replay Buffer.mp4 24.9 MB
15 - VIP Facial Recognition/1 -Facial Recognition Section Introduction.mp4 24.3 MB
6 - Convolutional Neural Networks/2 -What is Convolution (part 2).mp4 24.1 MB
15 - VIP Facial Recognition/3 -Code Outline.mp4 23.9 MB
16 - In-Depth Loss Functions/2 -Binary Cross Entropy.mp4 23.6 MB
6 - Convolutional Neural Networks/12 -Batch Normalization.mp4 23.4 MB
12 - Deep Reinforcement Learning (Theory)/5 -The Return.mp4 23.4 MB
17 - In-Depth Gradient Descent/2 -Stochastic Gradient Descent.mp4 23.0 MB
19 - Setting up your Environment (FAQ by Student Request)/1 -Pre-Installation Check.mp4 22.7 MB
2 - Getting Set Up/3 -Temporary 403 Errors.mp4 22.0 MB
10 - Transfer Learning for Computer Vision/4 -2 Approaches to Transfer Learning.mp4 21.7 MB
10 - Transfer Learning for Computer Vision/2 -Some Pre-trained Models (VGG, ResNet, Inception, MobileNet).mp4 21.6 MB
7 - Recurrent Neural Networks, Time Series, and Sequence Data/13 -RNN for Image Classification (Code).mp4 20.5 MB
15 - VIP Facial Recognition/10 -Facial Recognition Section Summary.mp4 18.3 MB
7 - Recurrent Neural Networks, Time Series, and Sequence Data/4 -Proof that the Linear Model Works.mp4 17.8 MB
13 - Stock Trading Project with Deep Reinforcement Learning/9 -Reinforcement Learning Stock Trader Discussion.mp4 17.2 MB
22 - Appendix FAQ Finale/1 -What is the Appendix.mp4 16.4 MB
2 - Getting Set Up/2 -How to Succeed in This Course.mp4 16.2 MB
8 - Natural Language Processing (NLP)/2 -Neural Networks with Embeddings.mp4 15.6 MB
5 - Feedforward Artificial Neural Networks/7 -Color Mixing Clarification.mp4 4.9 MB
19 - Setting up your Environment (FAQ by Student Request)/4 -Installing NVIDIA GPU-Accelerated Deep Learning Libraries on your Home Computer.vtt 27.9 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.vtt 27.7 KB
7 - Recurrent Neural Networks, Time Series, and Sequence Data/1 -Sequence Data.vtt 25.8 KB
6 - Convolutional Neural Networks/5 -CNN Architecture.vtt 24.3 KB
12 - Deep Reinforcement Learning (Theory)/2 -Elements of a Reinforcement Learning Problem.vtt 22.9 KB
7 - Recurrent Neural Networks, Time Series, and Sequence Data/5 -Recurrent Neural Networks.vtt 22.3 KB
6 - Convolutional Neural Networks/6 -CNN Code Preparation (part 1).vtt 21.5 KB
20 - Extra Help With Python Coding for Beginners (FAQ by Student Request)/2 -How to Code Yourself (part 1).vtt 20.2 KB
21 - Effective Learning Strategies for Machine Learning (FAQ by Student Request)/4 -Machine Learning and AI Prerequisite Roadmap (pt 2).vtt 20.2 KB
7 - Recurrent Neural Networks, Time Series, and Sequence Data/9 -GRU and LSTM (pt 1).vtt 19.9 KB
5 - Feedforward Artificial Neural Networks/4 -Activation Functions.vtt 19.8 KB
5 - Feedforward Artificial Neural Networks/9 -ANN for Image Classification.vtt 19.8 KB
11 - GANs (Generative Adversarial Networks)/1 -GAN Theory.vtt 18.4 KB
6 - Convolutional Neural Networks/1 -What is Convolution (part 1).vtt 18.4 KB
6 - Convolutional Neural Networks/4 -Convolution on Color Images.vtt 18.3 KB
8 - Natural Language Processing (NLP)/7 -Text Classification with LSTMs (V2).vtt 17.9 KB
5 - Feedforward Artificial Neural Networks/8 -Code Preparation (ANN).vtt 17.9 KB
4 - Machine Learning and Neurons/7 -Linear Classification Basics.vtt 17.9 KB
4 - Machine Learning and Neurons/2 -Regression Basics.vtt 17.5 KB
19 - Setting up your Environment (FAQ by Student Request)/3 -Anaconda Environment Setup.vtt 17.3 KB
20 - Extra Help With Python Coding for Beginners (FAQ by Student Request)/1 -Beginner's Coding Tips.vtt 16.6 KB
4 - Machine Learning and Neurons/1 -What is Machine Learning.vtt 16.2 KB
1 - Introduction/2 -Overview and Outline.vtt 15.7 KB
8 - Natural Language Processing (NLP)/3 -Text Preprocessing Concepts.vtt 15.6 KB
12 - Deep Reinforcement Learning (Theory)/11 -Q-Learning.vtt 15.6 KB
7 - Recurrent Neural Networks, Time Series, and Sequence Data/6 -RNN Code Preparation.vtt 15.6 KB
4 - Machine Learning and Neurons/4 -Regression Notebook.vtt 15.3 KB
9 - Recommender Systems/4 -Recommender Systems with Deep Learning Code (pt 2).vtt 15.2 KB
17 - In-Depth Gradient Descent/5 -Adam (pt 1).vtt 14.6 KB
12 - Deep Reinforcement Learning (Theory)/12 -Deep Q-Learning DQN (pt 1).vtt 14.4 KB
4 - Machine Learning and Neurons/3 -Regression Code Preparation.vtt 14.3 KB
21 - Effective Learning Strategies for Machine Learning (FAQ by Student Request)/3 -Machine Learning and AI Prerequisite Roadmap (pt 1).vtt 14.2 KB
8 - Natural Language Processing (NLP)/1 -Embeddings.vtt 14.1 KB
7 - Recurrent Neural Networks, Time Series, and Sequence Data/14 -Stock Return Predictions using LSTMs (pt 1).vtt 13.9 KB
8 - Natural Language Processing (NLP)/8 -CNNs for Text.vtt 13.8 KB
4 - Machine Learning and Neurons/6 -Moore's Law Notebook.vtt 13.8 KB
20 - Extra Help With Python Coding for Beginners (FAQ by Student Request)/6 -How to use Github & Extra Coding Tips (Optional).vtt 13.8 KB
13 - Stock Trading Project with Deep Reinforcement Learning/2 -Data and Environment.vtt 13.8 KB
8 - Natural Language Processing (NLP)/5 -(Legacy) Text Preprocessing Code Preparation.vtt 13.5 KB
12 - Deep Reinforcement Learning (Theory)/9 -Solving the Bellman Equation with Reinforcement Learning (pt 2).vtt 13.5 KB
5 - Feedforward Artificial Neural Networks/6 -How to Represent Images.vtt 13.4 KB
3 - Google Colab/3 -Where can I learn about Numpy, Scipy, Matplotlib, Pandas, and Scikit-Learn.vtt 13.4 KB
17 - In-Depth Gradient Descent/4 -Variable and Adaptive Learning Rates.vtt 13.3 KB
7 - Recurrent Neural Networks, Time Series, and Sequence Data/10 -GRU and LSTM (pt 2).vtt 13.1 KB
21 - Effective Learning Strategies for Machine Learning (FAQ by Student Request)/1 -How to Succeed in this Course (Long Version).vtt 12.8 KB
4 - Machine Learning and Neurons/9 -Classification Notebook.vtt 12.8 KB
8 - Natural Language Processing (NLP)/4 -Beginner Blues - PyTorch NLP Version.vtt 12.8 KB
7 - Recurrent Neural Networks, Time Series, and Sequence Data/3 -Autoregressive Linear Model for Time Series Prediction.vtt 12.8 KB
17 - In-Depth Gradient Descent/6 -Adam (pt 2).vtt 12.7 KB
3 - Google Colab/2 -Uploading your own data to Google Colab.vtt 12.6 KB
7 - Recurrent Neural Networks, Time Series, and Sequence Data/16 -Stock Return Predictions using LSTMs (pt 3).vtt 12.6 KB
19 - Setting up your Environment (FAQ by Student Request)/2 -How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.vtt 12.6 KB
3 - Google Colab/1 -Intro to Google Colab, how to use a GPU or TPU for free.vtt 12.5 KB
4 - Machine Learning and Neurons/14 -Train Sets vs. Validation Sets vs. Test Sets.vtt 12.5 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.vtt 12.3 KB
4 - Machine Learning and Neurons/12 -How does a model learn.vtt 12.1 KB
7 - Recurrent Neural Networks, Time Series, and Sequence Data/2 -Forecasting.vtt 12.1 KB
9 - Recommender Systems/1 -Recommender Systems with Deep Learning Theory.vtt 11.9 KB
6 - Convolutional Neural Networks/9 -CNN for Fashion MNIST.vtt 11.8 KB
12 - Deep Reinforcement Learning (Theory)/13 -Deep Q-Learning DQN (pt 2).vtt 11.6 KB
20 - Extra Help With Python Coding for Beginners (FAQ by Student Request)/3 -How to Code Yourself (part 2).vtt 11.4 KB
5 - Feedforward Artificial Neural Networks/10 -ANN for Regression.vtt 11.4 KB
14 - VIP Uncertainty Estimation/1 -Custom Loss and Estimating Prediction Uncertainty.vtt 11.3 KB
12 - Deep Reinforcement Learning (Theory)/8 -Solving the Bellman Equation with Reinforcement Learning (pt 1).vtt 11.2 KB
15 - VIP Facial Recognition/2 -Siamese Networks.vtt 11.2 KB
12 - Deep Reinforcement Learning (Theory)/4 -Markov Decision Processes (MDPs).vtt 11.2 KB
6 - Convolutional Neural Networks/13 -Improving CIFAR-10 Results.vtt 11.2 KB
9 - Recommender Systems/2 -Recommender Systems with Deep Learning Code Preparation.vtt 11.1 KB
6 - Convolutional Neural Networks/11 -Data Augmentation.vtt 11.0 KB
12 - Deep Reinforcement Learning (Theory)/6 -Value Functions and the Bellman Equation.vtt 10.9 KB
4 - Machine Learning and Neurons/11 -A Short Neuroscience Primer.vtt 10.8 KB
5 - Feedforward Artificial Neural Networks/5 -Multiclass Classification.vtt 10.8 KB
5 - Feedforward Artificial Neural Networks/2 -Forward Propagation.vtt 10.7 KB
13 - Stock Trading Project with Deep Reinforcement Learning/5 -Code pt 1.vtt 10.6 KB
20 - Extra Help With Python Coding for Beginners (FAQ by Student Request)/5 -Get Your Hands Dirty, Practical Coding Experience, Data Links.vtt 10.5 KB
13 - Stock Trading Project with Deep Reinforcement Learning/6 -Code pt 2.vtt 10.3 KB
10 - Transfer Learning for Computer Vision/5 -Transfer Learning Code (pt 1).vtt 10.2 KB
5 - Feedforward Artificial Neural Networks/3 -The Geometrical Picture.vtt 10.2 KB
12 - Deep Reinforcement Learning (Theory)/3 -States, Actions, Rewards, Policies.vtt 10.0 KB
16 - In-Depth Loss Functions/1 -Mean Squared Error.vtt 9.9 KB
7 - Recurrent Neural Networks, Time Series, and Sequence Data/8 -Paying Attention to Shapes.vtt 9.6 KB
9 - Recommender Systems/3 -Recommender Systems with Deep Learning Code (pt 1).vtt 9.6 KB
10 - Transfer Learning for Computer Vision/1 -Transfer Learning Theory.vtt 9.4 KB
11 - GANs (Generative Adversarial Networks)/3 -GAN Code.vtt 9.4 KB
7 - Recurrent Neural Networks, Time Series, and Sequence Data/11 -A More Challenging Sequence.vtt 9.3 KB
6 - Convolutional Neural Networks/7 -CNN Code Preparation (part 2).vtt 9.3 KB
7 - Recurrent Neural Networks, Time Series, and Sequence Data/7 -RNN for Time Series Prediction.vtt 8.6 KB
17 - In-Depth Gradient Descent/1 -Gradient Descent.vtt 8.6 KB
16 - In-Depth Loss Functions/3 -Categorical Cross Entropy.vtt 8.4 KB
15 - VIP Facial Recognition/9 -Accuracy and imbalanced classes.vtt 8.4 KB
4 - Machine Learning and Neurons/8 -Classification Code Preparation.vtt 8.3 KB
8 - Natural Language Processing (NLP)/6 -(Legacy) Text Preprocessing Code Example.vtt 8.2 KB
4 - Machine Learning and Neurons/5 -Moore's Law.vtt 8.0 KB
8 - Natural Language Processing (NLP)/10 -(Legacy) VIP Making Predictions with a Trained NLP Model.vtt 8.0 KB
10 - Transfer Learning for Computer Vision/3 -Large Datasets.vtt 8.0 KB
6 - Convolutional Neural Networks/10 -CNN for CIFAR-10.vtt 7.9 KB
12 - Deep Reinforcement Learning (Theory)/7 -What does it mean to “learn”.vtt 7.8 KB
14 - VIP Uncertainty Estimation/2 -Estimating Prediction Uncertainty Code.vtt 7.7 KB
10 - Transfer Learning for Computer Vision/6 -Transfer Learning Code (pt 2).vtt 7.7 KB
13 - Stock Trading Project with Deep Reinforcement Learning/4 -Program Design and Layout.vtt 7.6 KB
5 - Feedforward Artificial Neural Networks/11 -How to Choose Hyperparameters.vtt 7.5 KB
12 - Deep Reinforcement Learning (Theory)/1 -Deep Reinforcement Learning Section Introduction.vtt 7.5 KB
11 - GANs (Generative Adversarial Networks)/2 -GAN Code Preparation.vtt 7.4 KB
13 - Stock Trading Project with Deep Reinforcement Learning/7 -Code pt 3.vtt 7.4 KB
13 - Stock Trading Project with Deep Reinforcement Learning/8 -Code pt 4.vtt 7.4 KB
22 - Appendix FAQ Finale/2 -BONUS.vtt 7.1 KB
6 - Convolutional Neural Networks/3 -What is Convolution (part 3).vtt 7.0 KB
5 - Feedforward Artificial Neural Networks/1 -Artificial Neural Networks Section Introduction.vtt 6.9 KB
17 - In-Depth Gradient Descent/3 -Momentum.vtt 6.9 KB
12 - Deep Reinforcement Learning (Theory)/14 -How to Learn Reinforcement Learning.vtt 6.7 KB
12 - Deep Reinforcement Learning (Theory)/10 -Epsilon-Greedy.vtt 6.5 KB
16 - In-Depth Loss Functions/2 -Binary Cross Entropy.vtt 6.4 KB
6 - Convolutional Neural Networks/2 -What is Convolution (part 2).vtt 6.4 KB
7 - Recurrent Neural Networks, Time Series, and Sequence Data/17 -Other Ways to Forecast.vtt 6.3 KB
6 - Convolutional Neural Networks/8 -CNN Code Preparation (part 3).vtt 6.2 KB
8 - Natural Language Processing (NLP)/9 -Text Classification with CNNs (V2).vtt 6.2 KB
13 - Stock Trading Project with Deep Reinforcement Learning/3 -Replay Buffer.vtt 6.1 KB
15 - VIP Facial Recognition/4 -Loading in the data.vtt 6.0 KB
13 - Stock Trading Project with Deep Reinforcement Learning/1 -Reinforcement Learning Stock Trader Introduction.vtt 6.0 KB
7 - Recurrent Neural Networks, Time Series, and Sequence Data/15 -Stock Return Predictions using LSTMs (pt 2).vtt 6.0 KB
4 - Machine Learning and Neurons/10 -Saving and Loading a Model.vtt 5.8 KB
6 - Convolutional Neural Networks/12 -Batch Normalization.vtt 5.8 KB
19 - Setting up your Environment (FAQ by Student Request)/1 -Pre-Installation Check.vtt 5.7 KB
2 - Getting Set Up/1 -Where to get the code, notebooks, and data.vtt 5.5 KB
12 - Deep Reinforcement Learning (Theory)/5 -The Return.vtt 5.5 KB
9 - Recommender Systems/5 -VIP Making Predictions with a Trained Recommender Model.vtt 5.3 KB
10 - Transfer Learning for Computer Vision/4 -2 Approaches to Transfer Learning.vtt 5.2 KB
7 - Recurrent Neural Networks, Time Series, and Sequence Data/12 -RNN for Image Classification (Theory).vtt 5.2 KB
15 - VIP Facial Recognition/6 -Converting the data into pairs.vtt 5.1 KB
15 - VIP Facial Recognition/3 -Code Outline.vtt 5.1 KB
15 - VIP Facial Recognition/7 -Generating Generators.vtt 5.0 KB
1 - Introduction/1 -Welcome.vtt 5.0 KB
18 - Extras/1 -Where Are The Exercises.vtt 4.8 KB
17 - In-Depth Gradient Descent/2 -Stochastic Gradient Descent.vtt 4.8 KB
4 - Machine Learning and Neurons/13 -Model With Logits.vtt 4.7 KB
8 - Natural Language Processing (NLP)/11 -VIP Making Predictions with a Trained NLP Model (V2).vtt 4.7 KB
15 - VIP Facial Recognition/8 -Creating the model and loss.vtt 4.7 KB
10 - Transfer Learning for Computer Vision/2 -Some Pre-trained Models (VGG, ResNet, Inception, MobileNet).vtt 4.6 KB
15 - VIP Facial Recognition/5 -Splitting the data into train and test.vtt 4.5 KB
4 - Machine Learning and Neurons/15 -Suggestion Box.vtt 4.1 KB
15 - VIP Facial Recognition/1 -Facial Recognition Section Introduction.vtt 4.0 KB
7 - Recurrent Neural Networks, Time Series, and Sequence Data/4 -Proof that the Linear Model Works.vtt 4.0 KB
8 - Natural Language Processing (NLP)/2 -Neural Networks with Embeddings.vtt 4.0 KB
15 - VIP Facial Recognition/10 -Facial Recognition Section Summary.vtt 3.9 KB
2 - Getting Set Up/2 -How to Succeed in This Course.vtt 3.9 KB
13 - Stock Trading Project with Deep Reinforcement Learning/9 -Reinforcement Learning Stock Trader Discussion.vtt 3.9 KB
22 - Appendix FAQ Finale/1 -What is the Appendix.vtt 3.3 KB
2 - Getting Set Up/3 -Temporary 403 Errors.vtt 3.2 KB
7 - Recurrent Neural Networks, Time Series, and Sequence Data/13 -RNN for Image Classification (Code).vtt 2.9 KB
5 - Feedforward Artificial Neural Networks/7 -Color Mixing Clarification.vtt 1.0 KB
2 - Getting Set Up/1 -Data Links.url 119 bytes
20 - Extra Help With Python Coding for Beginners (FAQ by Student Request)/5 -Data Links.url 119 bytes
2 - Getting Set Up/1 -Github Link.url 102 bytes
20 - Extra Help With Python Coding for Beginners (FAQ by Student Request)/5 -Github Link.url 102 bytes
2 - Getting Set Up/1 -Code Link.url 87 bytes
8 - Natural Language Processing (NLP)/4 -Why bad programmers always need the latest version.url 51 bytes

Udemy - PyTorch Deep Learning and Artificial Intelligence (12.2024)

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