Deep Learning Course
PyTorch - Python Deep Learning Neural Network API
Coding Focused Project Based Theory Based Hands-On Exploratory
Pytorch Python Jupyter Discord VS Code
Level: Intermediate
Instructor: Chris
$79.99 $99.99 20% Off - Limited Offer
Enrolled
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What's Included:
What you'll learn ...
Learn how to install and configure PyTorch
Intuitively understand tensors and how they are the data structures of deep learning
Learn about tensor attributes like axes, rank, and shape
Learn tensor operations that are heavily used in deep learning like flattening, reshaping, and squeezing
Learn tensor operations by categories like reshaping ops, element-wise ops, reduction ops, and access ops
Develop a fundamental understanding of datasets and dataset processing in PyTorch
Understand the data preprocessing pipeline
Learn about the Fashion MNIST dataset and how it relates to MNIST
Develop of a deeper understanding of network design in general and in PyTorch
Learn to debug PyTorch code using a debugger
How to build PyTorch networks and training Loops
Understand how to integrate TensorBoard with PyTorch
Gain experience building a custom framework for experimentation and testing
How to perform hyperparameter experimentation using a custom testing framework
How to experiment with multiple network architectures using a custom build framework
Part 1 - TENSORS AND OPERATIONS
Section 1 - Introducing PyTorch
Lesson #1
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PyTorch Prerequisites - Syllabus for Neural Network Programming Course
Lesson #2
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PyTorch Explained - Python Deep Learning Neural Network API
Lesson #3
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PyTorch Install - Quick and Easy
Lesson #4
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CUDA Explained - Why Deep Learning uses GPUs
Section 2 - Introduction to Tensors
Lesson #5
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Tensors Explained - Data Structures of Deep Learning
Lesson #6
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Rank, Axes, and Shape Explained - Tensors for Deep Learning
Lesson #7
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CNN Tensor Shape Explained - Convolutional Neural Networks and Feature Maps
Lesson #8
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PyTorch Tensors Explained - Neural Network Programming
Lesson #9
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Creating PyTorch Tensors for Deep Learning - Best Options
Section 3 - Introduction to Tensor Operations
Lesson #10
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Flatten, Reshape, and Squeeze Explained - Tensors for Deep Learning with PyTorch
Lesson #11
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CNN Flatten Operation Visualized - Tensor Batch Processing for Deep Learning
Lesson #12
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Tensors for Deep Learning - Broadcasting and Element-wise Operations with PyTorch
Lesson #13
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Code for Deep Learning - ArgMax and Reduction Tensor Ops
Part 2 - NEURAL NETWORK TRAINING
Section 1 - Data and Data Processing
Lesson #14
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Dataset for Deep Learning - Fashion MNIST
Lesson #15
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CNN Image Preparation Code Project - Learn to Extract, Transform, Load (ETL)
Lesson #16
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PyTorch Datasets and DataLoaders - Training Set Exploration for Deep Learning and AI
Section 2 - Neural Networks and PyTorch Design
Lesson #17
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Build PyTorch CNN - Object Oriented Neural Networks
Lesson #18
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CNN Layers - PyTorch Deep Neural Network Architecture
Lesson #19
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CNN Weights - Learnable Parameters in PyTorch Neural Networks
Lesson #20
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Callable Neural Networks - Linear Layers in Depth
Lesson #21
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How to Debug PyTorch Source Code - Deep Learning in Python
Lesson #22
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CNN Forward Method - PyTorch Deep Learning Implementation
Lesson #23
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CNN Image Prediction with PyTorch - Forward Propagation Explained
Lesson #24
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Neural Network Batch Processing - Pass Image Batch to PyTorch CNN
Lesson #25
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CNN Output Size Formula - Bonus Neural Network Debugging Session
Section 3 - Training Neural Networks
Lesson #26
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CNN Training with Code Example - Neural Network Programming Course
Lesson #27
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CNN Training Loop Explained - Neural Network Code Project
Lesson #28
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CNN Confusion Matrix with PyTorch - Neural Network Programming
Lesson #29
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Stack vs Concat in PyTorch, TensorFlow & NumPy - Deep Learning Tensor Ops
Lesson #30
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TensorBoard with PyTorch - Visualize Deep Learning Metrics
Lesson #31
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Hyperparameter Tuning and Experimenting - Training Deep Neural Networks
Section 4 - Neural Network Experimentation
Lesson #32
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Training Loop Run Builder - Neural Network Experimentation Code
Lesson #33
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CNN Training Loop Refactoring - Simultaneous Hyperparameter Testing
Lesson #34
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PyTorch DataLoader num_workers - Deep Learning Speed Limit Increase
Lesson #35
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PyTorch on the GPU - Training Neural Networks with CUDA
Lesson #36
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PyTorch Dataset Normalization - torchvision.transforms.Normalize()
Lesson #37
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PyTorch DataLoader Source Code - Debugging Session
Lesson #38
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PyTorch Sequential Models - Neural Networks Made Easy
Lesson #39
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Batch Norm in PyTorch - Add Normalization to Conv Net Layers
Lesson #40
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Reset Weights PyTorch Network - Deep Learning Course
Lesson #41
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Training Multiple Networks - Deep Learning Course
Lesson #42
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Max Pooling vs No Max Pooling - Deep Learning Course
Lesson #43
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