Udemy - PyTorch Deep Learning and Artificial Intelligence (12.2024)

磁力链接/BT种子名称

Udemy - PyTorch Deep Learning and Artificial Intelligence (12.2024)

磁力链接/BT种子简介

种子哈希：714e6d8708da74301c32ab737a5c05f01e77246b
文件大小： 7.84G
已经下载：15次
下载速度：极快
收录时间：2025-08-02
最近下载：2025-08-14

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文件列表

  • 19 - Setting up your Environment (FAQ by Student Request)/3 -Anaconda Environment Setup.mp4 189.6 MB
  • 19 - Setting up your Environment (FAQ by Student Request)/4 -Installing NVIDIA GPU-Accelerated Deep Learning Libraries on your Home Computer.mp4 175.4 MB
  • 19 - Setting up your Environment (FAQ by Student Request)/2 -How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.mp4 158.0 MB
  • 8 - Natural Language Processing (NLP)/7 -Text Classification with LSTMs (V2).mp4 122.9 MB
  • 7 - Recurrent Neural Networks, Time Series, and Sequence Data/1 -Sequence Data.mp4 119.7 MB
  • 21 - Effective Learning Strategies for Machine Learning (FAQ by Student Request)/4 -Machine Learning and AI Prerequisite Roadmap (pt 2).mp4 113.4 MB
  • 5 - Feedforward Artificial Neural Networks/9 -ANN for Image Classification.mp4 111.5 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 110.7 MB
  • 12 - Deep Reinforcement Learning (Theory)/2 -Elements of a Reinforcement Learning Problem.mp4 110.1 MB
  • 7 - Recurrent Neural Networks, Time Series, and Sequence Data/5 -Recurrent Neural Networks.mp4 97.1 MB
  • 11 - GANs (Generative Adversarial Networks)/1 -GAN Theory.mp4 96.5 MB
  • 3 - Google Colab/2 -Uploading your own data to Google Colab.mp4 94.9 MB
  • 6 - Convolutional Neural Networks/5 -CNN Architecture.mp4 93.8 MB
  • 5 - Feedforward Artificial Neural Networks/4 -Activation Functions.mp4 93.6 MB
  • 7 - Recurrent Neural Networks, Time Series, and Sequence Data/11 -A More Challenging Sequence.mp4 91.4 MB
  • 7 - Recurrent Neural Networks, Time Series, and Sequence Data/3 -Autoregressive Linear Model for Time Series Prediction.mp4 85.2 MB
  • 5 - Feedforward Artificial Neural Networks/10 -ANN for Regression.mp4 84.0 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
  • 21 - Effective Learning Strategies for Machine Learning (FAQ by Student Request)/3 -Machine Learning and AI Prerequisite Roadmap (pt 1).mp4 83.6 MB
  • 6 - Convolutional Neural Networks/1 -What is Convolution (part 1).mp4 83.6 MB
  • 1 - Introduction/2 -Overview and Outline.mp4 83.5 MB
  • 4 - Machine Learning and Neurons/6 -Moore's Law Notebook.mp4 82.8 MB
  • 4 - Machine Learning and Neurons/9 -Classification Notebook.mp4 82.1 MB
  • 10 - Transfer Learning for Computer Vision/5 -Transfer Learning Code (pt 1).mp4 81.6 MB
  • 7 - Recurrent Neural Networks, Time Series, and Sequence Data/14 -Stock Return Predictions using LSTMs (pt 1).mp4 81.5 MB
  • 9 - Recommender Systems/4 -Recommender Systems with Deep Learning Code (pt 2).mp4 80.5 MB
  • 6 - Convolutional Neural Networks/13 -Improving CIFAR-10 Results.mp4 79.4 MB
  • 20 - Extra Help With Python Coding for Beginners (FAQ by Student Request)/1 -Beginner's Coding Tips.mp4 79.4 MB
  • 6 - Convolutional Neural Networks/4 -Convolution on Color Images.mp4 79.3 MB
  • 5 - Feedforward Artificial Neural Networks/6 -How to Represent Images.mp4 79.1 MB
  • 6 - Convolutional Neural Networks/9 -CNN for Fashion MNIST.mp4 77.3 MB
  • 4 - Machine Learning and Neurons/2 -Regression Basics.mp4 76.6 MB
  • 4 - Machine Learning and Neurons/4 -Regression Notebook.mp4 75.4 MB
  • 20 - Extra Help With Python Coding for Beginners (FAQ by Student Request)/2 -How to Code Yourself (part 1).mp4 75.3 MB
  • 7 - Recurrent Neural Networks, Time Series, and Sequence Data/7 -RNN for Time Series Prediction.mp4 75.2 MB
  • 7 - Recurrent Neural Networks, Time Series, and Sequence Data/16 -Stock Return Predictions using LSTMs (pt 3).mp4 74.6 MB
  • 4 - Machine Learning and Neurons/1 -What is Machine Learning.mp4 73.9 MB
  • 13 - Stock Trading Project with Deep Reinforcement Learning/6 -Code pt 2.mp4 73.4 MB
  • 9 - Recommender Systems/3 -Recommender Systems with Deep Learning Code (pt 1).mp4 72.9 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 72.8 MB
  • 12 - Deep Reinforcement Learning (Theory)/11 -Q-Learning.mp4 70.0 MB
  • 13 - Stock Trading Project with Deep Reinforcement Learning/5 -Code pt 1.mp4 69.6 MB
  • 5 - Feedforward Artificial Neural Networks/8 -Code Preparation (ANN).mp4 69.3 MB
  • 4 - Machine Learning and Neurons/7 -Linear Classification Basics.mp4 69.0 MB
  • 9 - Recommender Systems/1 -Recommender Systems with Deep Learning Theory.mp4 67.9 MB
  • 8 - Natural Language Processing (NLP)/4 -Beginner Blues - PyTorch NLP Version.mp4 67.2 MB
  • 20 - Extra Help With Python Coding for Beginners (FAQ by Student Request)/6 -How to use Github & Extra Coding Tips (Optional).mp4 67.0 MB
  • 11 - GANs (Generative Adversarial Networks)/3 -GAN Code.mp4 64.5 MB
  • 3 - Google Colab/1 -Intro to Google Colab, how to use a GPU or TPU for free.mp4 63.4 MB
  • 12 - Deep Reinforcement Learning (Theory)/12 -Deep Q-Learning DQN (pt 1).mp4 63.0 MB
  • 8 - Natural Language Processing (NLP)/1 -Embeddings.mp4 62.8 MB
  • 13 - Stock Trading Project with Deep Reinforcement Learning/7 -Code pt 3.mp4 61.4 MB
  • 8 - Natural Language Processing (NLP)/8 -CNNs for Text.mp4 61.2 MB
  • 10 - Transfer Learning for Computer Vision/1 -Transfer Learning Theory.mp4 60.9 MB
  • 3 - Google Colab/3 -Where can I learn about Numpy, Scipy, Matplotlib, Pandas, and Scikit-Learn.mp4 59.8 MB
  • 5 - Feedforward Artificial Neural Networks/3 -The Geometrical Picture.mp4 59.2 MB
  • 7 - Recurrent Neural Networks, Time Series, and Sequence Data/8 -Paying Attention to Shapes.mp4 59.1 MB
  • 10 - Transfer Learning for Computer Vision/6 -Transfer Learning Code (pt 2).mp4 59.0 MB
  • 13 - Stock Trading Project with Deep Reinforcement Learning/2 -Data and Environment.mp4 58.4 MB
  • 12 - Deep Reinforcement Learning (Theory)/9 -Solving the Bellman Equation with Reinforcement Learning (pt 2).mp4 58.2 MB
  • 6 - Convolutional Neural Networks/10 -CNN for CIFAR-10.mp4 58.0 MB
  • 7 - Recurrent Neural Networks, Time Series, and Sequence Data/6 -RNN Code Preparation.mp4 57.9 MB
  • 17 - In-Depth Gradient Descent/5 -Adam (pt 1).mp4 57.8 MB
  • 8 - Natural Language Processing (NLP)/9 -Text Classification with CNNs (V2).mp4 57.1 MB
  • 17 - In-Depth Gradient Descent/6 -Adam (pt 2).mp4 55.3 MB
  • 13 - Stock Trading Project with Deep Reinforcement Learning/8 -Code pt 4.mp4 55.1 MB
  • 8 - Natural Language Processing (NLP)/3 -Text Preprocessing Concepts.mp4 54.8 MB
  • 4 - Machine Learning and Neurons/14 -Train Sets vs. Validation Sets vs. Test Sets.mp4 54.7 MB
  • 12 - Deep Reinforcement Learning (Theory)/13 -Deep Q-Learning DQN (pt 2).mp4 54.6 MB
  • 15 - VIP Facial Recognition/9 -Accuracy and imbalanced classes.mp4 53.6 MB
  • 12 - Deep Reinforcement Learning (Theory)/4 -Markov Decision Processes (MDPs).mp4 52.9 MB
  • 7 - Recurrent Neural Networks, Time Series, and Sequence Data/10 -GRU and LSTM (pt 2).mp4 52.9 MB
  • 15 - VIP Facial Recognition/2 -Siamese Networks.mp4 52.9 MB
  • 4 - Machine Learning and Neurons/12 -How does a model learn.mp4 52.5 MB
  • 20 - Extra Help With Python Coding for Beginners (FAQ by Student Request)/3 -How to Code Yourself (part 2).mp4 51.5 MB
  • 8 - Natural Language Processing (NLP)/10 -(Legacy) VIP Making Predictions with a Trained NLP Model.mp4 51.2 MB
  • 5 - Feedforward Artificial Neural Networks/5 -Multiclass Classification.mp4 51.0 MB
  • 7 - Recurrent Neural Networks, Time Series, and Sequence Data/2 -Forecasting.mp4 50.7 MB
  • 8 - Natural Language Processing (NLP)/6 -(Legacy) Text Preprocessing Code Example.mp4 50.1 MB
  • 12 - Deep Reinforcement Learning (Theory)/6 -Value Functions and the Bellman Equation.mp4 50.0 MB
  • 5 - Feedforward Artificial Neural Networks/2 -Forward Propagation.mp4 49.3 MB
  • 12 - Deep Reinforcement Learning (Theory)/8 -Solving the Bellman Equation with Reinforcement Learning (pt 1).mp4 48.1 MB
  • 4 - Machine Learning and Neurons/3 -Regression Code Preparation.mp4 47.7 MB
  • 4 - Machine Learning and Neurons/11 -A Short Neuroscience Primer.mp4 46.8 MB
  • 6 - Convolutional Neural Networks/11 -Data Augmentation.mp4 46.6 MB
  • 8 - Natural Language Processing (NLP)/5 -(Legacy) Text Preprocessing Code Preparation.mp4 46.5 MB
  • 12 - Deep Reinforcement Learning (Theory)/3 -States, Actions, Rewards, Policies.mp4 46.2 MB
  • 20 - Extra Help With Python Coding for Beginners (FAQ by Student Request)/5 -Get Your Hands Dirty, Practical Coding Experience, Data Links.mp4 45.7 MB
  • 14 - VIP Uncertainty Estimation/1 -Custom Loss and Estimating Prediction Uncertainty.mp4 45.6 MB
  • 7 - Recurrent Neural Networks, Time Series, and Sequence Data/15 -Stock Return Predictions using LSTMs (pt 2).mp4 45.3 MB
  • 14 - VIP Uncertainty Estimation/2 -Estimating Prediction Uncertainty Code.mp4 44.7 MB
  • 12 - Deep Reinforcement Learning (Theory)/10 -Epsilon-Greedy.mp4 43.5 MB
  • 10 - Transfer Learning for Computer Vision/3 -Large Datasets.mp4 43.2 MB
  • 12 - Deep Reinforcement Learning (Theory)/1 -Deep Reinforcement Learning Section Introduction.mp4 42.7 MB
  • 22 - Appendix FAQ Finale/2 -BONUS.mp4 42.4 MB
  • 12 - Deep Reinforcement Learning (Theory)/14 -How to Learn Reinforcement Learning.mp4 42.2 MB
  • 9 - Recommender Systems/2 -Recommender Systems with Deep Learning Code Preparation.mp4 42.0 MB
  • 5 - Feedforward Artificial Neural Networks/11 -How to Choose Hyperparameters.mp4 41.4 MB
  • 6 - Convolutional Neural Networks/7 -CNN Code Preparation (part 2).mp4 38.5 MB
  • 1 - Introduction/1 -Welcome.mp4 37.4 MB
  • 21 - Effective Learning Strategies for Machine Learning (FAQ by Student Request)/1 -How to Succeed in this Course (Long Version).mp4 36.9 MB
  • 15 - VIP Facial Recognition/4 -Loading in the data.mp4 36.7 MB
  • 17 - In-Depth Gradient Descent/1 -Gradient Descent.mp4 36.6 MB
  • 17 - In-Depth Gradient Descent/4 -Variable and Adaptive Learning Rates.mp4 36.5 MB
  • 17 - In-Depth Gradient Descent/3 -Momentum.mp4 35.9 MB
  • 16 - In-Depth Loss Functions/1 -Mean Squared Error.mp4 35.4 MB
  • 6 - Convolutional Neural Networks/8 -CNN Code Preparation (part 3).mp4 35.3 MB
  • 5 - Feedforward Artificial Neural Networks/1 -Artificial Neural Networks Section Introduction.mp4 35.0 MB
  • 9 - Recommender Systems/5 -VIP Making Predictions with a Trained Recommender Model.mp4 34.3 MB
  • 15 - VIP Facial Recognition/7 -Generating Generators.mp4 34.0 MB
  • 7 - Recurrent Neural Networks, Time Series, and Sequence Data/12 -RNN for Image Classification (Theory).mp4 33.8 MB
  • 16 - In-Depth Loss Functions/3 -Categorical Cross Entropy.mp4 33.3 MB
  • 12 - Deep Reinforcement Learning (Theory)/7 -What does it mean to “learn”.mp4 33.2 MB
  • 4 - Machine Learning and Neurons/5 -Moore's Law.mp4 32.0 MB
  • 15 - VIP Facial Recognition/6 -Converting the data into pairs.mp4 31.8 MB
  • 6 - Convolutional Neural Networks/3 -What is Convolution (part 3).mp4 31.3 MB
  • 15 - VIP Facial Recognition/8 -Creating the model and loss.mp4 30.8 MB
  • 13 - Stock Trading Project with Deep Reinforcement Learning/1 -Reinforcement Learning Stock Trader Introduction.mp4 30.2 MB
  • 4 - Machine Learning and Neurons/10 -Saving and Loading a Model.mp4 30.2 MB
  • 7 - Recurrent Neural Networks, Time Series, and Sequence Data/17 -Other Ways to Forecast.mp4 29.7 MB
  • 11 - GANs (Generative Adversarial Networks)/2 -GAN Code Preparation.mp4 29.5 MB
  • 4 - Machine Learning and Neurons/13 -Model With Logits.mp4 28.5 MB
  • 4 - Machine Learning and Neurons/15 -Suggestion Box.mp4 28.5 MB
  • 2 - Getting Set Up/1 -Where to get the code, notebooks, and data.mp4 28.2 MB
  • 13 - Stock Trading Project with Deep Reinforcement Learning/4 -Program Design and Layout.mp4 28.1 MB
  • 4 - Machine Learning and Neurons/8 -Classification Code Preparation.mp4 27.8 MB
  • 15 - VIP Facial Recognition/5 -Splitting the data into train and test.mp4 27.5 MB
  • 18 - Extras/1 -Where Are The Exercises.mp4 27.2 MB
  • 8 - Natural Language Processing (NLP)/11 -VIP Making Predictions with a Trained NLP Model (V2).mp4 26.7 MB
  • 13 - Stock Trading Project with Deep Reinforcement Learning/3 -Replay Buffer.mp4 26.1 MB
  • 15 - VIP Facial Recognition/1 -Facial Recognition Section Introduction.mp4 25.5 MB
  • 6 - Convolutional Neural Networks/2 -What is Convolution (part 2).mp4 25.2 MB
  • 15 - VIP Facial Recognition/3 -Code Outline.mp4 25.0 MB
  • 16 - In-Depth Loss Functions/2 -Binary Cross Entropy.mp4 24.8 MB
  • 6 - Convolutional Neural Networks/12 -Batch Normalization.mp4 24.5 MB
  • 12 - Deep Reinforcement Learning (Theory)/5 -The Return.mp4 24.5 MB
  • 17 - In-Depth Gradient Descent/2 -Stochastic Gradient Descent.mp4 24.1 MB
  • 19 - Setting up your Environment (FAQ by Student Request)/1 -Pre-Installation Check.mp4 23.8 MB
  • 2 - Getting Set Up/3 -Temporary 403 Errors.mp4 23.0 MB
  • 10 - Transfer Learning for Computer Vision/4 -2 Approaches to Transfer Learning.mp4 22.8 MB
  • 10 - Transfer Learning for Computer Vision/2 -Some Pre-trained Models (VGG, ResNet, Inception, MobileNet).mp4 22.7 MB
  • 7 - Recurrent Neural Networks, Time Series, and Sequence Data/13 -RNN for Image Classification (Code).mp4 21.5 MB
  • 15 - VIP Facial Recognition/10 -Facial Recognition Section Summary.mp4 19.2 MB
  • 7 - Recurrent Neural Networks, Time Series, and Sequence Data/4 -Proof that the Linear Model Works.mp4 18.7 MB
  • 13 - Stock Trading Project with Deep Reinforcement Learning/9 -Reinforcement Learning Stock Trader Discussion.mp4 18.0 MB
  • 22 - Appendix FAQ Finale/1 -What is the Appendix.mp4 17.2 MB
  • 2 - Getting Set Up/2 -How to Succeed in This Course.mp4 17.0 MB
  • 8 - Natural Language Processing (NLP)/2 -Neural Networks with Embeddings.mp4 16.4 MB
  • 5 - Feedforward Artificial Neural Networks/7 -Color Mixing Clarification.mp4 5.1 MB
  • 19 - Setting up your Environment (FAQ by Student Request)/4 -Installing NVIDIA GPU-Accelerated Deep Learning Libraries on your Home Computer.vtt 28.6 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 28.3 kB
  • 7 - Recurrent Neural Networks, Time Series, and Sequence Data/1 -Sequence Data.vtt 26.4 kB
  • 6 - Convolutional Neural Networks/5 -CNN Architecture.vtt 24.9 kB
  • 12 - Deep Reinforcement Learning (Theory)/2 -Elements of a Reinforcement Learning Problem.vtt 23.4 kB
  • 7 - Recurrent Neural Networks, Time Series, and Sequence Data/5 -Recurrent Neural Networks.vtt 22.9 kB
  • 6 - Convolutional Neural Networks/6 -CNN Code Preparation (part 1).vtt 22.0 kB
  • 20 - Extra Help With Python Coding for Beginners (FAQ by Student Request)/2 -How to Code Yourself (part 1).vtt 20.7 kB
  • 21 - Effective Learning Strategies for Machine Learning (FAQ by Student Request)/4 -Machine Learning and AI Prerequisite Roadmap (pt 2).vtt 20.7 kB
  • 7 - Recurrent Neural Networks, Time Series, and Sequence Data/9 -GRU and LSTM (pt 1).vtt 20.4 kB
  • 5 - Feedforward Artificial Neural Networks/4 -Activation Functions.vtt 20.3 kB
  • 5 - Feedforward Artificial Neural Networks/9 -ANN for Image Classification.vtt 20.3 kB
  • 11 - GANs (Generative Adversarial Networks)/1 -GAN Theory.vtt 18.9 kB
  • 6 - Convolutional Neural Networks/1 -What is Convolution (part 1).vtt 18.8 kB
  • 6 - Convolutional Neural Networks/4 -Convolution on Color Images.vtt 18.8 kB
  • 8 - Natural Language Processing (NLP)/7 -Text Classification with LSTMs (V2).vtt 18.4 kB
  • 5 - Feedforward Artificial Neural Networks/8 -Code Preparation (ANN).vtt 18.3 kB
  • 4 - Machine Learning and Neurons/7 -Linear Classification Basics.vtt 18.3 kB
  • 4 - Machine Learning and Neurons/2 -Regression Basics.vtt 17.9 kB
  • 19 - Setting up your Environment (FAQ by Student Request)/3 -Anaconda Environment Setup.vtt 17.8 kB
  • 20 - Extra Help With Python Coding for Beginners (FAQ by Student Request)/1 -Beginner's Coding Tips.vtt 17.0 kB
  • 4 - Machine Learning and Neurons/1 -What is Machine Learning.vtt 16.6 kB
  • 1 - Introduction/2 -Overview and Outline.vtt 16.1 kB
  • 8 - Natural Language Processing (NLP)/3 -Text Preprocessing Concepts.vtt 16.0 kB
  • 12 - Deep Reinforcement Learning (Theory)/11 -Q-Learning.vtt 16.0 kB
  • 7 - Recurrent Neural Networks, Time Series, and Sequence Data/6 -RNN Code Preparation.vtt 15.9 kB
  • 4 - Machine Learning and Neurons/4 -Regression Notebook.vtt 15.7 kB
  • 9 - Recommender Systems/4 -Recommender Systems with Deep Learning Code (pt 2).vtt 15.6 kB
  • 17 - In-Depth Gradient Descent/5 -Adam (pt 1).vtt 15.0 kB
  • 12 - Deep Reinforcement Learning (Theory)/12 -Deep Q-Learning DQN (pt 1).vtt 14.7 kB
  • 4 - Machine Learning and Neurons/3 -Regression Code Preparation.vtt 14.6 kB
  • 21 - Effective Learning Strategies for Machine Learning (FAQ by Student Request)/3 -Machine Learning and AI Prerequisite Roadmap (pt 1).vtt 14.5 kB
  • 8 - Natural Language Processing (NLP)/1 -Embeddings.vtt 14.4 kB
  • 7 - Recurrent Neural Networks, Time Series, and Sequence Data/14 -Stock Return Predictions using LSTMs (pt 1).vtt 14.3 kB
  • 8 - Natural Language Processing (NLP)/8 -CNNs for Text.vtt 14.2 kB
  • 4 - Machine Learning and Neurons/6 -Moore's Law Notebook.vtt 14.2 kB
  • 20 - Extra Help With Python Coding for Beginners (FAQ by Student Request)/6 -How to use Github & Extra Coding Tips (Optional).vtt 14.1 kB
  • 13 - Stock Trading Project with Deep Reinforcement Learning/2 -Data and Environment.vtt 14.1 kB
  • 8 - Natural Language Processing (NLP)/5 -(Legacy) Text Preprocessing Code Preparation.vtt 13.8 kB
  • 12 - Deep Reinforcement Learning (Theory)/9 -Solving the Bellman Equation with Reinforcement Learning (pt 2).vtt 13.8 kB
  • 5 - Feedforward Artificial Neural Networks/6 -How to Represent Images.vtt 13.8 kB
  • 3 - Google Colab/3 -Where can I learn about Numpy, Scipy, Matplotlib, Pandas, and Scikit-Learn.vtt 13.7 kB
  • 17 - In-Depth Gradient Descent/4 -Variable and Adaptive Learning Rates.vtt 13.6 kB
  • 7 - Recurrent Neural Networks, Time Series, and Sequence Data/10 -GRU and LSTM (pt 2).vtt 13.4 kB
  • 21 - Effective Learning Strategies for Machine Learning (FAQ by Student Request)/1 -How to Succeed in this Course (Long Version).vtt 13.1 kB
  • 4 - Machine Learning and Neurons/9 -Classification Notebook.vtt 13.1 kB
  • 8 - Natural Language Processing (NLP)/4 -Beginner Blues - PyTorch NLP Version.vtt 13.1 kB
  • 7 - Recurrent Neural Networks, Time Series, and Sequence Data/3 -Autoregressive Linear Model for Time Series Prediction.vtt 13.1 kB
  • 17 - In-Depth Gradient Descent/6 -Adam (pt 2).vtt 13.0 kB
  • 3 - Google Colab/2 -Uploading your own data to Google Colab.vtt 12.9 kB
  • 7 - Recurrent Neural Networks, Time Series, and Sequence Data/16 -Stock Return Predictions using LSTMs (pt 3).vtt 12.9 kB
  • 19 - Setting up your Environment (FAQ by Student Request)/2 -How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow.vtt 12.9 kB
  • 3 - Google Colab/1 -Intro to Google Colab, how to use a GPU or TPU for free.vtt 12.8 kB
  • 4 - Machine Learning and Neurons/14 -Train Sets vs. Validation Sets vs. Test Sets.vtt 12.8 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 12.6 kB
  • 4 - Machine Learning and Neurons/12 -How does a model learn.vtt 12.4 kB
  • 7 - Recurrent Neural Networks, Time Series, and Sequence Data/2 -Forecasting.vtt 12.4 kB
  • 9 - Recommender Systems/1 -Recommender Systems with Deep Learning Theory.vtt 12.2 kB
  • 6 - Convolutional Neural Networks/9 -CNN for Fashion MNIST.vtt 12.0 kB
  • 12 - Deep Reinforcement Learning (Theory)/13 -Deep Q-Learning DQN (pt 2).vtt 11.9 kB
  • 20 - Extra Help With Python Coding for Beginners (FAQ by Student Request)/3 -How to Code Yourself (part 2).vtt 11.7 kB
  • 5 - Feedforward Artificial Neural Networks/10 -ANN for Regression.vtt 11.6 kB
  • 14 - VIP Uncertainty Estimation/1 -Custom Loss and Estimating Prediction Uncertainty.vtt 11.5 kB
  • 12 - Deep Reinforcement Learning (Theory)/8 -Solving the Bellman Equation with Reinforcement Learning (pt 1).vtt 11.5 kB
  • 15 - VIP Facial Recognition/2 -Siamese Networks.vtt 11.5 kB
  • 12 - Deep Reinforcement Learning (Theory)/4 -Markov Decision Processes (MDPs).vtt 11.5 kB
  • 6 - Convolutional Neural Networks/13 -Improving CIFAR-10 Results.vtt 11.5 kB
  • 9 - Recommender Systems/2 -Recommender Systems with Deep Learning Code Preparation.vtt 11.4 kB
  • 6 - Convolutional Neural Networks/11 -Data Augmentation.vtt 11.2 kB
  • 12 - Deep Reinforcement Learning (Theory)/6 -Value Functions and the Bellman Equation.vtt 11.2 kB
  • 4 - Machine Learning and Neurons/11 -A Short Neuroscience Primer.vtt 11.0 kB
  • 5 - Feedforward Artificial Neural Networks/5 -Multiclass Classification.vtt 11.0 kB
  • 5 - Feedforward Artificial Neural Networks/2 -Forward Propagation.vtt 10.9 kB
  • 13 - Stock Trading Project with Deep Reinforcement Learning/5 -Code pt 1.vtt 10.8 kB
  • 20 - Extra Help With Python Coding for Beginners (FAQ by Student Request)/5 -Get Your Hands Dirty, Practical Coding Experience, Data Links.vtt 10.8 kB
  • 13 - Stock Trading Project with Deep Reinforcement Learning/6 -Code pt 2.vtt 10.5 kB
  • 10 - Transfer Learning for Computer Vision/5 -Transfer Learning Code (pt 1).vtt 10.4 kB
  • 5 - Feedforward Artificial Neural Networks/3 -The Geometrical Picture.vtt 10.4 kB
  • 12 - Deep Reinforcement Learning (Theory)/3 -States, Actions, Rewards, Policies.vtt 10.2 kB
  • 16 - In-Depth Loss Functions/1 -Mean Squared Error.vtt 10.1 kB
  • 7 - Recurrent Neural Networks, Time Series, and Sequence Data/8 -Paying Attention to Shapes.vtt 9.9 kB
  • 9 - Recommender Systems/3 -Recommender Systems with Deep Learning Code (pt 1).vtt 9.8 kB
  • 10 - Transfer Learning for Computer Vision/1 -Transfer Learning Theory.vtt 9.7 kB
  • 11 - GANs (Generative Adversarial Networks)/3 -GAN Code.vtt 9.6 kB
  • 7 - Recurrent Neural Networks, Time Series, and Sequence Data/11 -A More Challenging Sequence.vtt 9.5 kB
  • 6 - Convolutional Neural Networks/7 -CNN Code Preparation (part 2).vtt 9.5 kB
  • 7 - Recurrent Neural Networks, Time Series, and Sequence Data/7 -RNN for Time Series Prediction.vtt 8.9 kB
  • 17 - In-Depth Gradient Descent/1 -Gradient Descent.vtt 8.8 kB
  • 16 - In-Depth Loss Functions/3 -Categorical Cross Entropy.vtt 8.6 kB
  • 15 - VIP Facial Recognition/9 -Accuracy and imbalanced classes.vtt 8.6 kB
  • 4 - Machine Learning and Neurons/8 -Classification Code Preparation.vtt 8.5 kB
  • 8 - Natural Language Processing (NLP)/6 -(Legacy) Text Preprocessing Code Example.vtt 8.4 kB
  • 4 - Machine Learning and Neurons/5 -Moore's Law.vtt 8.2 kB
  • 8 - Natural Language Processing (NLP)/10 -(Legacy) VIP Making Predictions with a Trained NLP Model.vtt 8.2 kB
  • 10 - Transfer Learning for Computer Vision/3 -Large Datasets.vtt 8.2 kB
  • 6 - Convolutional Neural Networks/10 -CNN for CIFAR-10.vtt 8.1 kB
  • 12 - Deep Reinforcement Learning (Theory)/7 -What does it mean to “learn”.vtt 8.0 kB
  • 14 - VIP Uncertainty Estimation/2 -Estimating Prediction Uncertainty Code.vtt 7.9 kB
  • 10 - Transfer Learning for Computer Vision/6 -Transfer Learning Code (pt 2).vtt 7.9 kB
  • 13 - Stock Trading Project with Deep Reinforcement Learning/4 -Program Design and Layout.vtt 7.8 kB
  • 5 - Feedforward Artificial Neural Networks/11 -How to Choose Hyperparameters.vtt 7.7 kB
  • 12 - Deep Reinforcement Learning (Theory)/1 -Deep Reinforcement Learning Section Introduction.vtt 7.7 kB
  • 11 - GANs (Generative Adversarial Networks)/2 -GAN Code Preparation.vtt 7.6 kB
  • 13 - Stock Trading Project with Deep Reinforcement Learning/7 -Code pt 3.vtt 7.6 kB
  • 13 - Stock Trading Project with Deep Reinforcement Learning/8 -Code pt 4.vtt 7.5 kB
  • 22 - Appendix FAQ Finale/2 -BONUS.vtt 7.2 kB
  • 6 - Convolutional Neural Networks/3 -What is Convolution (part 3).vtt 7.2 kB
  • 5 - Feedforward Artificial Neural Networks/1 -Artificial Neural Networks Section Introduction.vtt 7.1 kB
  • 17 - In-Depth Gradient Descent/3 -Momentum.vtt 7.1 kB
  • 12 - Deep Reinforcement Learning (Theory)/14 -How to Learn Reinforcement Learning.vtt 6.9 kB
  • 12 - Deep Reinforcement Learning (Theory)/10 -Epsilon-Greedy.vtt 6.7 kB
  • 16 - In-Depth Loss Functions/2 -Binary Cross Entropy.vtt 6.5 kB
  • 6 - Convolutional Neural Networks/2 -What is Convolution (part 2).vtt 6.5 kB
  • 7 - Recurrent Neural Networks, Time Series, and Sequence Data/17 -Other Ways to Forecast.vtt 6.5 kB
  • 6 - Convolutional Neural Networks/8 -CNN Code Preparation (part 3).vtt 6.4 kB
  • 8 - Natural Language Processing (NLP)/9 -Text Classification with CNNs (V2).vtt 6.4 kB
  • 13 - Stock Trading Project with Deep Reinforcement Learning/3 -Replay Buffer.vtt 6.2 kB
  • 15 - VIP Facial Recognition/4 -Loading in the data.vtt 6.2 kB
  • 13 - Stock Trading Project with Deep Reinforcement Learning/1 -Reinforcement Learning Stock Trader Introduction.vtt 6.1 kB
  • 7 - Recurrent Neural Networks, Time Series, and Sequence Data/15 -Stock Return Predictions using LSTMs (pt 2).vtt 6.1 kB
  • 4 - Machine Learning and Neurons/10 -Saving and Loading a Model.vtt 6.0 kB
  • 6 - Convolutional Neural Networks/12 -Batch Normalization.vtt 5.9 kB
  • 19 - Setting up your Environment (FAQ by Student Request)/1 -Pre-Installation Check.vtt 5.9 kB
  • 2 - Getting Set Up/1 -Where to get the code, notebooks, and data.vtt 5.7 kB
  • 12 - Deep Reinforcement Learning (Theory)/5 -The Return.vtt 5.6 kB
  • 9 - Recommender Systems/5 -VIP Making Predictions with a Trained Recommender Model.vtt 5.4 kB
  • 10 - Transfer Learning for Computer Vision/4 -2 Approaches to Transfer Learning.vtt 5.4 kB
  • 7 - Recurrent Neural Networks, Time Series, and Sequence Data/12 -RNN for Image Classification (Theory).vtt 5.3 kB
  • 15 - VIP Facial Recognition/6 -Converting the data into pairs.vtt 5.3 kB
  • 15 - VIP Facial Recognition/3 -Code Outline.vtt 5.2 kB
  • 15 - VIP Facial Recognition/7 -Generating Generators.vtt 5.2 kB
  • 1 - Introduction/1 -Welcome.vtt 5.2 kB
  • 18 - Extras/1 -Where Are The Exercises.vtt 4.9 kB
  • 17 - In-Depth Gradient Descent/2 -Stochastic Gradient Descent.vtt 4.9 kB
  • 4 - Machine Learning and Neurons/13 -Model With Logits.vtt 4.8 kB
  • 8 - Natural Language Processing (NLP)/11 -VIP Making Predictions with a Trained NLP Model (V2).vtt 4.8 kB
  • 15 - VIP Facial Recognition/8 -Creating the model and loss.vtt 4.8 kB
  • 10 - Transfer Learning for Computer Vision/2 -Some Pre-trained Models (VGG, ResNet, Inception, MobileNet).vtt 4.7 kB
  • 15 - VIP Facial Recognition/5 -Splitting the data into train and test.vtt 4.6 kB
  • 4 - Machine Learning and Neurons/15 -Suggestion Box.vtt 4.2 kB
  • 15 - VIP Facial Recognition/1 -Facial Recognition Section Introduction.vtt 4.1 kB
  • 7 - Recurrent Neural Networks, Time Series, and Sequence Data/4 -Proof that the Linear Model Works.vtt 4.1 kB
  • 8 - Natural Language Processing (NLP)/2 -Neural Networks with Embeddings.vtt 4.1 kB
  • 15 - VIP Facial Recognition/10 -Facial Recognition Section Summary.vtt 4.0 kB
  • 2 - Getting Set Up/2 -How to Succeed in This Course.vtt 4.0 kB
  • 13 - Stock Trading Project with Deep Reinforcement Learning/9 -Reinforcement Learning Stock Trader Discussion.vtt 4.0 kB
  • 22 - Appendix FAQ Finale/1 -What is the Appendix.vtt 3.4 kB
  • 2 - Getting Set Up/3 -Temporary 403 Errors.vtt 3.3 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

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