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- Simpliv Learning
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- PyTorch for Deep Learning and Computer Vision
Quick Facts
| particular | details | |||
|---|---|---|---|---|
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Medium of instructions
English
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Mode of learning
Self study
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Mode of Delivery
Video and Text Based
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Course and certificate fees
Fees information
$ 199 $999
certificate availability
Yes
certificate providing authority
Simpliv Learning
The syllabus
Introduction
Getting Started
Intro to Tensors – PyTorch
- Intro to Tensors – PyTorch - Intro
- 1 Dimensional Tensors
- Vector Operations
- 2 Dimensional Tensors
- Slicing 3D Tensors
- Matrix Multiplication
- Gradient with PyTorch
- Intro to Tensors – PyTorch: Outro
Linear Regression – PyTorch
- Linear Regression – PyTorch - Intro
- Making Predictions
- Linear Class
- Custom Modules
- Linear Regression – PyTorch : Creating Dataset
- Loss Function
- Gradient Descent
- Mean Squared Error
- Training - Code Implementation
- Linear Regression – PyTorch: Outro
Perceptrons – PyTorch
- Perceptrons – PyTorch: Intro
- What is Deep Learning
- Perceptrons – PyTorch : Creating Dataset
- Perceptron Model
- Model Setup
- Model Training
- Model Testing
- Perceptrons – PyTorch: Outro
Deep Neural Networks – PyTorch
- Deep Neural Networks – PyTorch: Intro
- Non-Linear Boundaries
- Architecture
- Feedforward Process
- Error Function
- Backpropagation
- Code Implementation
- Testing Model
- Deep Neural Networks – PyTorch: Outro
Image Recognition – PyTorch
- Image Recognition – PyTorch: Intro
- MNIST Dataset
- Training and Test Datasets
- Image Recognition – PyTorch : Image Transforms
- Neural Network Implementation
- Neural Network Validation
- Final Tests
- A note on adjusting batch size
- Image Recognition – PyTorch: Outro
Convolutional Neural Networks – PyTorch
- Convolutions and MNIST
- Convolutional Layer
- Convolutions II
- Pooling
- Fully Connected Network
- Neural Network Implementation with PyTorch
- Model Training with PyTorch
CIFAR 10 Classification – PyTorch
- The CIFAR 10 Dataset
- Testing LeNet
- Hyperparameter Tuning
- Data Augmentation
Transfer Learning – PyTorch
- Pre-trained Sophisticated Models
- AlexNet and VGG16
Style Transfer – PyTorch
- VGG 19
- Style Transfer – PyTorch : Image Transforms
- Feature Extraction
- The Gram Matrix
- Optimization
- Style Transfer with Video
Appendix A - Python Crash Course
- Appendix A - Python Crash Course: Overview
- Anaconda Installation (Mac)
- Anaconda Installation Windows
- Jupyter Notebooks
- Arithmetic Operators
- Variables
- Numeric Data Types
- String
- Booleans
- Methods
- Lists
- Slicing
- Membership Operator
- Mutability
- Mutability II
- Common Functions & Methods
- Tuples
- Sets
- Dictionaries
- Compound Data Structures
- Part 1 – Outro
- Part 2 - Control Flow
- If, else
- elseif
- Complex Comparisons
- For Loops
- For Loops II
- While Loops
- Break
- Part 2 – Outro
- Part 3 – Functions
- Functions
- Scope
- Doc Strings
- Lambda and Higher Order Functions
- Part 3 – Outro
Appendix B - NumPy Crash Course
- Appendix B - NumPy Crash Course: Overview
- Arrays vs Lists
- Multidimensional Arrays
- One Dimensional Slicing
- Reshaping
- Multidimensional Slicing
- Manipulating Array Shapes
- Appendix B - NumPy Crash Course - Matrix Multiplication
- Stacking
- Outro
Appendix B - NumPy Crash Course: Outro
- Softmax
- Cross Entropy
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