PyTorch Deep Learning in 7 Days

Video description

Boost your career in one week with the cutting-edge field of Deep Learning with PyTorch

About This Video

  • A systematic guide on Deep Learning to help you build smart applications
  • Cover core concepts and architectures of Deep Learning systems without getting bogged down in mathematical notation
  • Solve Machine Learning problems by applying Deep Learning architectures

In Detail

PyTorch is Facebook’s latest Python-based framework for Deep Learning. It has the ability to create dynamic Neural Networks on CPUs and GPUs, both with a significantly less code compared to other competing frameworks. PyTorch has a unique interface that makes it as easy to learn as NumPy.

This 7-day course is for those who are in a hurry to get started with PyTorch. You will be introduced to the most commonly used Deep Learning models, techniques, and algorithms through PyTorch code. This course is an attempt to break the myth that Deep Learning is complicated and show you that with the right choice of tools combined with a simple and intuitive explanation of core concepts, Deep Learning is as accessible as any other application development technologies out there. It’s a journey from diving deep into the fundamentals to getting acquainted with the advance concepts such as Transfer Learning, Natural Language Processing and implementation of Generative Adversarial Networks.

By the end of the course, you will be able to build Deep Learning applications with PyTorch.

Audience

This course is for software development professionals and machine learning enthusiasts, who have heard the hype of Deep Learning and want to learn it to stay relevant in their field. Basic knowledge of machine learning concepts and Python programming is required.

Publisher resources

Download Example Code

Table of contents

  1. Chapter 1 : Getting started with PyTorch
    1. The Course overview
    2. Quick Intro to PyTorch
    3. Installation and Jupyter Notebook Setup
    4. Tensors and Basic Tensor Operations
    5. Advanced Tensor Operations
    6. Loading and Saving Data
    7. Assignment
  2. Chapter 2 : Building a Neural Network
    1. Introduction to Neural Networks
    2. Creating a Neural Network with PyTorch Sequential
    3. Activations, Loss Functions, and Gradients
    4. Forward and Backward Passes
    5. Building a Network with nn.Module
    6. Assignment
  3. Chapter 3 : Regression and Classification
    1. Loading Structured Data for Classification
    2. Preprocessing Data
    3. Classification, Accuracy, and the Confusion Matrix
    4. Loading Structured Data for Regression
    5. Neural Networks for Regression
    6. Assignment
  4. Chapter 4 : Implementing Convolutional Neural Networks
    1. Convolutional Networks for Image Analysis
    2. Convolutional Concepts: Filters, Strides, Padding, and Pooling
    3. Implementing a Convolutional Network
    4. Visualizing Convolutional Network Layers
    5. Implementing an End-To-End Deep Convolutional Network
    6. Assignment
  5. Chapter 5 : Implementing Transfer Learning
    1. Transfer Learning and Prebuilt Models
    2. Deep Learning with VGG
    3. Transfer Learning with VGG
    4. Transfer Learning with ResNet
    5. Assignment
  6. Chapter 6 : LSTM and Embedding for Natural Language Models
    1. Recurrent Networks, RNN, and LSTM, GRU
    2. Text Modeling with Bag-of-Words
    3. Sentiment Analysis with Bag-of-Words
    4. Sentiment Analysis with Word Embeddings
    5. Assignment
  7. Chapter 7 : Deep Convolutional Generative Adversarial Networks
    1. Introduction to GANs and DCGANs
    2. Implementing DCGAN Model with PyTorch
    3. Training and Evaluating DCGAN on an Image Dataset
    4. Improving Performance
    5. Assignment

Product information

  • Title: PyTorch Deep Learning in 7 Days
  • Author(s): Will Ballard
  • Release date: March 2019
  • Publisher(s): Packt Publishing
  • ISBN: 9781789135367