![]() To verify our PyTorch installation is all set and that we are ready to code, we'll do this in a notebook. Neither of these tools are necessary, but they do make our lives We automatically get Jupyter Notebook with the Anaconda installation. We won't use VS code until part two of the series, and most of our time will be spent inside Jupyter notebook. The navigation features for source code are pretty robust. ![]() It's also useful for exploring the PyTorch source code. VS code makes debugging our code and inspecting our objects pretty easy. We'll be using VS Code primarily for debugging our code. Once you have Visual Studio Code installed, you'll also want to install the Python plugin. Jupyter Notebook - Interactive environment.Visual Studio Code - Integrated development environment.In this series, we'll be using the following software for writing, debugging our code: # packages in environment at C:\Users\deeplizard\Anaconda3: # Name Version Build Channel All we need to do is select a version of CUDA if we have a supported Nvidia GPU on our system. The needed CUDA software comes installed with PyTorch if a CUDA version Also, there is no need to install CUDA separately. Notice that we are installing both PyTorch and torchvision. In this case, we have the following command:Ĭonda install pytorch torchvision torchaudio cudatoolkit= 10.2 -c pytorch Deep Learning with PyTorch - Course Conclusion.Max Pooling vs No Max Pooling - Deep Learning Course.Training Multiple Networks - Deep Learning Course.Reset Weights PyTorch Network - Deep Learning Course.Batch Norm in PyTorch - Add Normalization to Conv Net Layers.PyTorch Sequential Models - Neural Networks Made Easy.PyTorch DataLoader Source Code - Debugging Session.PyTorch on the GPU - Training Neural Networks with CUDA.PyTorch DataLoader num_workers - Deep Learning Speed Limit Increase.CNN Training Loop Refactoring - Simultaneous Hyperparameter Testing.Training Loop Run Builder - Neural Network Experimentation Code.Hyperparameter Tuning and Experimenting - Training Deep Neural Networks.TensorBoard with PyTorch - Visualize Deep Learning Metrics.Stack vs Concat in PyTorch, TensorFlow & NumPy - Deep Learning Tensor Ops.CNN Confusion Matrix with PyTorch - Neural Network Programming.CNN Training Loop Explained - Neural Network Code Project.CNN Training with Code Example - Neural Network Programming Course.CNN Output Size Formula - Bonus Neural Network Debugging Session.Neural Network Batch Processing - Pass Image Batch to PyTorch CNN.CNN Image Prediction with PyTorch - Forward Propagation Explained.CNN Forward Method - PyTorch Deep Learning Implementation.How to Debug PyTorch Source Code - Deep Learning in Python.Callable Neural Networks - Linear Layers in Depth.CNN Weights - Learnable Parameters in PyTorch Neural Networks.CNN Layers - PyTorch Deep Neural Network Architecture.Build PyTorch CNN - Object Oriented Neural Networks. ![]() PyTorch Datasets and DataLoaders - Training Set Exploration for Deep Learning and AI.CNN Image Preparation Code Project - Learn to Extract, Transform, Load (ETL).Dataset for Deep Learning - Fashion MNIST.Code for Deep Learning - ArgMax and Reduction Tensor Ops.Tensors for Deep Learning - Broadcasting and Element-wise Operations with PyTorch.CNN Flatten Operation Visualized - Tensor Batch Processing for Deep Learning.Flatten, Reshape, and Squeeze Explained - Tensors for Deep Learning with PyTorch.Creating PyTorch Tensors for Deep Learning - Best Options.PyTorch Tensors Explained - Neural Network Programming.CNN Tensor Shape Explained - Convolutional Neural Networks and Feature Maps.Rank, Axes, and Shape Explained - Tensors for Deep Learning.Tensors Explained - Data Structures of Deep Learning.CUDA Explained - Why Deep Learning uses GPUs.PyTorch Explained - Python Deep Learning Neural Network API.PyTorch Prerequisites - Syllabus for Neural Network Programming Course.Facebook Instagram Twitter Patreon YouTube Vlog Fitness Hivemind Neurohacker ![]()
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