Deep Learning - Computer Vision for Beginners Using PyTorch

Deep Learning - Computer Vision for Beginners Using PyTorch

by Manifold AI Learning
Released March 2023
Publisher(s): Packt Publishing
ISBN: 9781837634286

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Video description

Note: The course is primarily focused on teaching PyTorch and deep learning for computer vision, but it also includes a few sections on the fundamentals of Python (Sections 8–12). These optional learning sections are designed for individuals who may be new to Python or who want to refresh their knowledge of Python basics.

In this course, we will take a step-by-step method by first grasping PyTorch’s fundamentals. Then, using a guide to getting free GPU for learning, you will learn how to code in GPU. You will then learn about PyTorch’s AutoGrad feature and how to use it. Later, you will learn how to use PyTorch to create deep learning models and understand the fundamentals of convolutional neural networks (CNN). You will also learn how to use CNN with a real-world dataset.

Additionally, the course will emphasize the fundamentals and lay the groundwork for an understanding of Python. We will also talk about the three significant Python libraries known as NumPy, Pandas, and Matplotlib. In this part of the course, we will also build a mini project where we will be building a hangman game in Python.

By the end of this course, we will be able to perform Computer Vision tasks with deep learning.

What You Will Learn

  • Learn how to work with PyTorch
  • Build intuition on convolution operation on images
  • Implement gradient descent using AutoGrad
  • Learn about LeNet architecture
  • Create a mini-Python project game
  • Understand how to use NumPy, Pandas, and Matplotlib libraries

Audience

Software developers, machine learning practitioners, data scientists, and anybody else interested in understanding PyTorch and deep learning should take this course. While a basic knowledge of Python would be beneficial, it is not a prerequisite as we will be covering the necessary fundamentals during the course.

About The Author

Manifold AI Learning: Manifold AI Learning® is an online academy with the goal to empower students with the knowledge and skills that can be directly applied to solving real-world problems in data science, machine learning, and artificial intelligence.

With a curated curriculum and a hands-on guide, you will always be an industry-ready professional.

Table of contents

  1. Chapter 1 : Welcome Aboard
    1. Course Introduction
    2. Why Is PyTorch Powerful?
  2. Chapter 2 : Introduction to PyTorch and Tensors
    1. What Is PyTorch
  3. Chapter 3 : Diving into PyTorch
    1. Installing PyTorch
    2. Create Tensors in PyTorch
    3. Tensor Slicing and Reshape
    4. Mathematical Operations on Tensors
    5. NumPy in PyTorch
    6. What Is CUDA
    7. PyTorch on GPU
  4. Chapter 4 : AutoGrad in PyTorch
    1. AutoGrad in PyTorch
    2. AutoGrad in a Loop
  5. Chapter 5 : Creating Deep Neural Networks in PyTorch
    1. Building the First Neural Network
    2. Writing a Deep Neural Network
    3. Writing a Custom NN Module
  6. Chapter 6 : CNN in PyTorch
    1. Data Loading - CIFAR10
    2. Data Visualization
    3. CNN Recap
    4. First CNN
    5. CNN Deep Layers
  7. Chapter 7 : LeNet Architecture in PyTorch
    1. LeNet Overview
    2. LeNet Model in PyTorch
    3. Preparation and Evaluation
  8. Chapter 8 : Optional Learning - Python Basics
    1. Why Learn Any Programming Language
    2. Why Choose Python
    3. Installing Jupyter Notebook
    4. Jupyter Notebook - Tips and Tricks
    5. What We Will Cover in This Section
    6. Variables in Python
    7. Print Function
    8. Numerical Data Types and Arithmetic Operations in Python
    9. String Data Type
    10. Boolean Data Type
    11. Type Conversion and Type Casting
    12. Adding Comments in Python Programming Language
    13. Data Structures in Python
    14. Tuples and Sets in Python
    15. Python Dictionaries
    16. Conditional Statements in Python - if
    17. Conditional Statements in Python - While
    18. Inbuilt Functions in Python - range and input
    19. For Loops
    20. Functions in Python
    21. Classes in Python
  9. Chapter 9 : Optional Learning - Mini Project with Python Basics
    1. Mini Project - Hangman
    2. Writing a Class
    3. Mini Project - Continued
    4. Logic Building
    5. Logic for Single-Letter input
    6. Final Testing
  10. Chapter 10 : Optional Learning - Python for Data Science – with NumPy
    1. NumPy
    2. Resize and Reshape Arrays
    3. Slicing
    4. Broadcasting
    5. Mathematical Operations and Functions in NumPy
  11. Chapter 11 : Optional Learning - Python for Data Science – with Pandas
    1. Pandas Library
    2. Pandas Dataframe
    3. Pandas Dataframe - Load from External File
    4. Working with Null Values
    5. Slicing Pandas Dataframe
    6. Imputation
  12. Chapter 12 : Optional Learning - Python for Data Science – with Matplotlib
    1. Matplotlib Introduction
    2. Format the Plot
    3. Plot Formatting and Scatter Plot
    4. Histplot

Product information

  • Title: Deep Learning - Computer Vision for Beginners Using PyTorch
  • Author(s): Manifold AI Learning
  • Release date: March 2023
  • Publisher(s): Packt Publishing
  • ISBN: 9781837634286