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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.

All the code and supporting files for this course are available on GitHub at: https://github.com/PacktPublishing/PyTorch-Deep-Learning-in-7-Days

Downloading the example code for this course: You can download the example code files for all Packt video courses you have purchased from your account at http://www.PacktPub.com. If you purchased this course elsewhere, you can visit http://www.PacktPub.com/support and register to have the files e-mailed directly to you.

Table of Contents

  1. Chapter 1 : Getting started with PyTorch
    1. The Course overview 00:02:34
    2. Quick Intro to PyTorch 00:03:02
    3. Installation and Jupyter Notebook Setup 00:03:13
    4. Tensors and Basic Tensor Operations 00:03:44
    5. Advanced Tensor Operations 00:06:49
    6. Loading and Saving Data 00:03:50
    7. Assignment 00:00:41
  2. Chapter 2 : Building a Neural Network
    1. Introduction to Neural Networks 00:03:29
    2. Creating a Neural Network with PyTorch Sequential 00:02:28
    3. Activations, Loss Functions, and Gradients 00:03:30
    4. Forward and Backward Passes 00:03:06
    5. Building a Network with nn.Module 00:04:46
    6. Assignment 00:00:31
  3. Chapter 3 : Regression and Classification
    1. Loading Structured Data for Classification 00:04:20
    2. Preprocessing Data 00:03:35
    3. Classification, Accuracy, and the Confusion Matrix 00:04:45
    4. Loading Structured Data for Regression 00:04:41
    5. Neural Networks for Regression 00:03:28
    6. Assignment 00:00:18
  4. Chapter 4 : Implementing Convolutional Neural Networks
    1. Convolutional Networks for Image Analysis 00:03:26
    2. Convolutional Concepts: Filters, Strides, Padding, and Pooling 00:03:56
    3. Implementing a Convolutional Network 00:03:19
    4. Visualizing Convolutional Network Layers 00:03:42
    5. Implementing an End-To-End Deep Convolutional Network 00:03:16
    6. Assignment 00:00:20
  5. Chapter 5 : Implementing Transfer Learning
    1. Transfer Learning and Prebuilt Models 00:03:03
    2. Deep Learning with VGG 00:03:01
    3. Transfer Learning with VGG 00:04:10
    4. Transfer Learning with ResNet 00:06:28
    5. Assignment 00:00:21
  6. Chapter 6 : LSTM and Embedding for Natural Language Models
    1. Recurrent Networks, RNN, and LSTM, GRU 00:04:01
    2. Text Modeling with Bag-of-Words 00:01:33
    3. Sentiment Analysis with Bag-of-Words 00:03:29
    4. Sentiment Analysis with Word Embeddings 00:04:59
    5. Assignment 00:00:19
  7. Chapter 7 : Deep Convolutional Generative Adversarial Networks
    1. Introduction to GANs and DCGANs 00:04:22
    2. Implementing DCGAN Model with PyTorch 00:03:09
    3. Training and Evaluating DCGAN on an Image Dataset 00:05:28
    4. Improving Performance 00:02:43
    5. Assignment 00:01:41