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Dataset for Deep Learning - Fashion MNIST
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Classroom Contents
Neural Network Programming - Deep Learning with PyTorch
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- 1 PyTorch Prerequisites - Syllabus for Neural Network Programming Course
- 2 PyTorch Explained - Python Deep Learning Neural Network API
- 3 PyTorch Install - Quick and Easy
- 4 CUDA Explained - Why Deep Learning uses GPUs
- 5 Tensors Explained - Data Structures of Deep Learning
- 6 Rank, Axes, and Shape Explained - Tensors for Deep Learning
- 7 CNN Tensor Shape Explained - Convolutional Neural Networks and Feature Maps
- 8 PyTorch Tensors Explained - Neural Network Programming
- 9 Creating PyTorch Tensors for Deep Learning - Best Options
- 10 Flatten, Reshape, and Squeeze Explained - Tensors for Deep Learning with PyTorch
- 11 CNN Flatten Operation Visualized - Tensor Batch Processing for Deep Learning
- 12 Tensors for Deep Learning - Broadcasting and Element-wise Operations with PyTorch
- 13 Code for Deep Learning - ArgMax and Reduction Tensor Ops
- 14 Dataset for Deep Learning - Fashion MNIST
- 15 CNN Image Preparation Code Project - Learn to Extract, Transform, Load (ETL)
- 16 PyTorch Datasets and DataLoaders - Training Set Exploration for Deep Learning and AI
- 17 Build PyTorch CNN - Object Oriented Neural Networks
- 18 CNN Layers - PyTorch Deep Neural Network Architecture
- 19 CNN Weights - Learnable Parameters in PyTorch Neural Networks
- 20 Callable Neural Networks - Linear Layers in Depth
- 21 How to Debug PyTorch Source Code - Deep Learning in Python
- 22 CNN Forward Method - PyTorch Deep Learning Implementation
- 23 CNN Image Prediction with PyTorch - Forward Propagation Explained
- 24 Neural Network Batch Processing - Pass Image Batch to PyTorch CNN
- 25 CNN Output Size Formula - Bonus Neural Network Debugging Session
- 26 CNN Training with Code Example - Neural Network Programming Course
- 27 CNN Training Loop Explained - Neural Network Code Project
- 28 CNN Confusion Matrix with PyTorch - Neural Network Programming
- 29 Stack vs Concat in PyTorch, TensorFlow & NumPy - Deep Learning Tensor Ops
- 30 TensorBoard with PyTorch - Visualize Deep Learning Metrics
- 31 Hyperparameter Tuning and Experimenting - Training Deep Neural Networks
- 32 Training Loop Run Builder - Neural Network Experimentation Code
- 33 CNN Training Loop Refactoring - Simultaneous Hyperparameter Testing
- 34 PyTorch DataLoader num_workers - Deep Learning Speed Limit Increase
- 35 PyTorch on the GPU - Training Neural Networks with CUDA
- 36 PyTorch Dataset Normalization - torchvision.transforms.Normalize()
- 37 PyTorch DataLoader Source Code - Debugging Session
- 38 PyTorch Sequential Models - Neural Networks Made Easy
- 39 Batch Norm in PyTorch - Add Normalization to Conv Net Layers
