PyTorch for Deep Learning and Computer Vision

Video description

Learn to build highly sophisticated deep learning and Computer Vision applications with PyTorch

About This Video

This course is designed to help you become an accomplished deep learning developer even with no experience in programming or mathematics

In Detail

PyTorch has rapidly become one of the most transformative frameworks in the field of deep learning. Since its release, PyTorch has completely changed the landscape of the deep …

PyTorch for Deep Learning and Computer Vision

Video description

Learn to build highly sophisticated deep learning and Computer Vision applications with PyTorch

About This Video

This course is designed to help you become an accomplished deep learning developer even with no experience in programming or mathematics

In Detail

PyTorch has rapidly become one of the most transformative frameworks in the field of deep learning. Since its release, PyTorch has completely changed the landscape of the deep learning domain with its flexibility and has made building deep learning models easier. The development world offers some of the highest paying jobs in deep learning. In this exciting course, instructor Rayan Slim will help you learn and master deep learning with PyTorch. Having taught over 44,000 students, Rayan is a highly rated and experienced instructor who has followed a learning-by-doing style to create this course. You'll go from a beginner to deep learning expert with your instructor completing each step of the task with you. By the end of this course, you will have built state-of-the-art deep learning and Computer Vision applications with PyTorch. The projects built in this course will impress even the most senior developers and ensure you have hands-on skills that you can bring to any project or organization.

Who this book is for

This course is for you if you’re interested in deep learning and Computer Vision. Anyone (no matter the skill level) who wants to transition into the field of artificial intelligence and entrepreneurs with an interest in working on some of the most cutting-edge technologies will find this course useful.

Publisher resources

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Chapter 1 : Introduction

Introduction

Chapter 2 : Getting Started

Finding the codes (Github)

A Look at the Projects

Chapter 3 : Intro to Tensors – PyTorch

Intro

1 Dimensional Tensors

Vector Operations

2 Dimensional Tensors

Slicing 3D Tensors

Matrix Multiplication

Gradient with PyTorch

Outro

Chapter 4 : Linear Regression – PyTorch

Intro

Making Predictions

Linear Class

Custom Modules

Creating Dataset

Loss Function

Gradient Descent

Mean Squared Error

Training - Code Implementation

Outro

Chapter 5 : Perceptrons – PyTorch

Intro

What is Deep Learning

Creating Dataset

Perceptron Model

Model Setup

Model Training

Model Testing

Outro

Chapter 6 : Deep Neural Networks – PyTorch

Intro

Non-Linear Boundaries

Architecture

Feedforward Process

Error Function

Backpropagation

Code Implementation

Testing Model

Outro

Chapter 7 : Image Recognition – PyTorch

Intro

MNIST Dataset

Training and Test Datasets

Image Transforms

Neural Network Implementation

Neural Network Validation

Final Tests

A note on adjusting batch size

Outro

Chapter 8 : Convolutional Neural Networks – PyTorch

Convolutions and MNIST

Convolutional Layer

Convolutions II

Pooling

Fully Connected Network

Neural Network Implementation with PyTorch

Model Training with PyTorch

Chapter 9 : CIFAR 10 Classification – PyTorch

The CIFAR 10 Dataset

Testing LeNet

Hyperparameter Tuning

Data Augmentation

Chapter 10 : Transfer Learning – PyTorch

Pre-trained Sophisticated Models

AlexNet and VGG16

Chapter 11 : Style Transfer – PyTorch

VGG 19

Image Transforms

Feature Extraction

The Gram Matrix

Optimization

Style Transfer with Video

Chapter 12 : 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

Chapter 13 : Appendix B - NumPy Crash Course

Overview

Arrays vs Lists

Multidimensional Arrays

One Dimensional Slicing

Reshaping

Multidimensional Slicing

Manipulating Array Shapes

Matrix Multiplication

Stacking

Outro

Chapter 14 : Appendix C - Softmax Explanation

Softmax

Cross Entropy

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