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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

Description:

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.

Neural Network Programming - Deep Learning with PyTorch

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#Computer Science #Deep Learning #PyTorch #Artificial Intelligence #Neural Networks #Programming #Software Development #CUDA #Machine Learning #Hyperparameter Tuning #Neural Network Architecture #TensorFlow #Tensorboard

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