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    PyTorch for Deep Learning and Computer Vision

    BY
    Simpliv Learning

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    Mode

    Online

    Fees

    $ 199 999

    Quick Facts

    particular details
    Medium of instructions English
    Mode of learning Self study
    Mode of Delivery Video and Text Based

    Course and certificate fees

    Fees information
    $ 199  $999
    certificate availability

    Yes

    certificate providing authority

    Simpliv Learning

    The syllabus

    Introduction

    Getting Started

    •  Finding the codes (Github)
    •  A Look at the Projects 

    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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