Dive into neural network programming and deep learning with PyTorch in this comprehensive 9-hour course. Begin with PyTorch and CUDA basics, understanding GPU usage in neural networks. Progress through tensor fundamentals crucial for deep learning comprehension before exploring neural network architecture. Learn to train networks, analyze results, tune hyperparameters, and utilize TensorBoard for visual analytics. Cover topics such as tensor operations, CNN architecture, image preparation, datasets and DataLoaders, forward propagation, batch processing, and confusion matrices. Explore advanced concepts like hyperparameter tuning, GPU acceleration, dataset normalization, and batch normalization. Gain hands-on experience with code examples, debugging sessions, and practical projects throughout the course.
Overview
Syllabus
PyTorch Prerequisites - Syllabus for Neural Network Programming Course.
PyTorch Explained - Python Deep Learning Neural Network API.
PyTorch Install - Quick and Easy.
CUDA Explained - Why Deep Learning uses GPUs.
Tensors Explained - Data Structures of Deep Learning.
Rank, Axes, and Shape Explained - Tensors for Deep Learning.
CNN Tensor Shape Explained - Convolutional Neural Networks and Feature Maps.
PyTorch Tensors Explained - Neural Network Programming.
Creating PyTorch Tensors for Deep Learning - Best Options.
Flatten, Reshape, and Squeeze Explained - Tensors for Deep Learning with PyTorch.
CNN Flatten Operation Visualized - Tensor Batch Processing for Deep Learning.
Tensors for Deep Learning - Broadcasting and Element-wise Operations with PyTorch.
Code for Deep Learning - ArgMax and Reduction Tensor Ops.
Dataset for Deep Learning - Fashion MNIST.
CNN Image Preparation Code Project - Learn to Extract, Transform, Load (ETL).
PyTorch Datasets and DataLoaders - Training Set Exploration for Deep Learning and AI.
Build PyTorch CNN - Object Oriented Neural Networks.
CNN Layers - PyTorch Deep Neural Network Architecture.
CNN Weights - Learnable Parameters in PyTorch Neural Networks.
Callable Neural Networks - Linear Layers in Depth.
How to Debug PyTorch Source Code - Deep Learning in Python.
CNN Forward Method - PyTorch Deep Learning Implementation.
CNN Image Prediction with PyTorch - Forward Propagation Explained.
Neural Network Batch Processing - Pass Image Batch to PyTorch CNN.
CNN Output Size Formula - Bonus Neural Network Debugging Session.
CNN Training with Code Example - Neural Network Programming Course.
CNN Training Loop Explained - Neural Network Code Project.
CNN Confusion Matrix with PyTorch - Neural Network Programming.
Stack vs Concat in PyTorch, TensorFlow & NumPy - Deep Learning Tensor Ops.
TensorBoard with PyTorch - Visualize Deep Learning Metrics.
Hyperparameter Tuning and Experimenting - Training Deep Neural Networks.
Training Loop Run Builder - Neural Network Experimentation Code.
CNN Training Loop Refactoring - Simultaneous Hyperparameter Testing.
PyTorch DataLoader num_workers - Deep Learning Speed Limit Increase.
PyTorch on the GPU - Training Neural Networks with CUDA.
PyTorch Dataset Normalization - torchvision.transforms.Normalize().
PyTorch DataLoader Source Code - Debugging Session.
PyTorch Sequential Models - Neural Networks Made Easy.
Batch Norm in PyTorch - Add Normalization to Conv Net Layers.
Taught by
deeplizard
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Reviews
5.0 rating, based on 1 Class Central review
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Thank you for all the great information, the tutorials were very easy to follow. There is so much to learn I really appreciate these tutorials. I hope to see much more like this in the future.
