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PyTorch: Deep Learning with PyTorch - Masterclass!: 2-in-1
Rating: 4.2 out of 5(81 ratings)
616 students

PyTorch: Deep Learning with PyTorch - Masterclass!: 2-in-1

Start your journey with PyTorch to build useful & effective models with the PyTorch Deep Learning framework from scratch
Created byPackt Publishing
Last updated 10/2018
English

What you'll learn

  • Build your neural network using Deep Learning techniques in PyTorch.
  • Build artificial neural networks in Python with GPU acceleration.
  • Use Auto-Encoders in PyTorch to remove noise from images.
  • Perform Reinforcement Learning to solve OpenAI'sCartpole task.
  • Extend your knowledge of Deep Learning by using PyTorch to solve your own machine learning problems.
  • Create a Convolutional Neural Network (CNN) for image recognition.
  • Predict share prices with Recurrent Neural Network and Long Short-Term Memory Network (LSTM).
  • Detect credit card fraud with autoencoders.
  • Develop a movie recommendation system using Boltzmann Machines.
  • Use AutoEncoders to develop recommendation systems to rate a movie.
  • Detect the shape and color of a given picture or an object using PyTorch

Course content

2 sections • 65 lectures • 7h 43m total length

  • The Course Overview6:29

    This video provides an overview of the entire course. 

  • Introduction to PyTorch6:18

    Get introduced to PyTorch.

    • Develop a mental model of the PyTorch Deep Learning framework

    • Understand the salient features of PyTorch

  • Installing PyTorch on Linux and Windows10:41

    Have your PyTorch based Linux and Windows environments ready.

    • Download the required software packages.

    • Setup the PyTorch based Linux environment.

    • Setup the PyTorch based Windows environment.

  • Installing CUDA4:41

    Get your GPU based system CUDA enabled.

    • Know about CUDA

    • Download the required software

    • Install and test

  • Introduction to Tensors and Variables16:17

    Familiarize yourself with Tensors and Variables.

    • Understand the role of a Tensor

    • Know what Variables are

    • Run PyTorch code to see them in action

  • Working with PyTorch and NumPy2:38

    Familiarize yourself with the bridge between PyTorch and NumPy.

    • Understand the connection between PyTorch and NumPy

    • Run PyTorch code to see it in action

  • Working with PyTorch and GPU3:07

    Execute computation on GPU.

    • Understand the PyTorch GPU model

    • See how data can be moved from CPU to GPU and back

    • Run PyTorch code to see it in action

  • Handling Datasets in PyTorch8:29

    Access datasets in PyTorch.

    • Understand concepts like Dataset, Epoch, Batch, and Iteration

    • See how popular datasets can be accessed using TorchVision

    • Run PyTorch code to see it in action

  • Deep Learning Using PyTorch8:17

    Get introduced to Deep Learning and know the PyTorch based projects we are going to execute in this course.

    • Understand the relation between Deep Learning and Machine Learning

    • Explore some use cases solved by Computer Vision and NLP

    • Get to know the topics and projects covered in rest of the course.

  • Building a Simple Neural Network13:26

    Learn how to build a simple neural network in PyTorch.

    • Know about concepts like nodes, edges, weights and biases in a neuron

    • Understand the role of activation functions

    • Create a 3-layer neural network in PyTorch

  • Loss Functions in PyTorch2:07

    Learn about the role of loss functions.

    • Understand the role of loss functions

    • Understand where loss functions fit in the training process

    • Know when to use Cross Entropy Loss

  • Optimizers in PyTorch3:50

    Learn about optimizers.

    • Understand the role of optimizers

    • Understand where loss functions fit in the training process

    • Know about gradients and how the optimizer uses it

  • Training the Neural Network6:36

    Learn how to train a model.

    • Know about the training loop

    • Understand the various operations within the training loop

    • Execute the training

  • Saving and Loading a Trained Neural Network1:27

    Learn how to save models to disk and read them back.

    • Know what APIs to use in PyTorch to accomplish this

  • Training the Neural Network on a GPU3:46

    Learn how to train your model on a GPU.

    • Know what changes to make in the model to accomplish this

  • Computer Vision Motivation4:57

    Get motivated towards the subject.

    • Know about the practical applications of computer vision in the industry

  • Convolutional Neural Networks8:08

    Understand Convolutional Neural Networks.

    • Get to know how images are represented and used

    • Understand the convolution operation

    • Understand the concept of pooling

  • The Convolution Operation9:28

    Know how to load the MNIST dataset.

    • Learn about the MNIST dataset

    • Use torchvision to get the MNIST dataset

    • Create the DataLoader to iterate through the MNIST dataset

  • Concepts - Strides, Padding, and Pooling9:28

    Learn some essential concepts around the convolution layers and the convolution operations.

    • Explain the concepts of strides, padding and pooling in the context of CNNs

    • Understand the effect of these on the activation maps

  • Loading and Using MNIST Dataset9:05

    Learn how to load the MNIST

    • Learn about the MNIST dataset

    • Use torchvision to get the MNIST dataset

    • Create the DataLoader to iterate through the MNIST dataset

  • Building the Model8:57

    Learn how to build CNN models

    • Use classes from PyTorch to construct the CNN model

    • Understand the dimensions of the activation map in each layer

  • Training and Testing11:53

    See how to train the model and test it

    • Build the training loop to train the model

    • Visualize the loss and accuracy of the training and validation data

    • Learn how to do inferencing on a single item

  • Sequence Models Motivation4:55

    Get motivated towards the subject.

    • Know about the practical applications of computer vision in the industry.

  • Word Embedding6:46

    Understand the way text is represented in neural networks.

    • Learn about one hot vector and it’s short comings

    • Learn how words are represented as meaningful vectors of numbers

    • Learn about Embedding layer and GloVe and Word2Vec models

  • Recurrent Neural Networks10:44

    Understand the neural network architecture to process sequential data.

    • Learn about the RNN architecture

    • Understand the various configurations in which RNNs can be used

    • Understand the problems fixed by LSTM and GRU cells

  • Building a Text Generation Model in PyTorch17:25

    Create a model to generate Shakespeare like text.

    • Have intuitive understanding of language model

  • Training and Testing7:27

    See how to train the model and test it.

    • Understand how to use the language model to generate text

    • Initialize and generate Shakespeare like text

  • Autoencoders Motivation4:32

    Get motivated towards the subject.

    • Know about the practical applications of Autoencoders in the industry.

  • How Autoencoders Work3:21

    Understand the technical details behind Autoencoders.

    • Learn about the typical architecture of Autoencoders

    • Understand what the bottleneck layer is

    • Understand how an Autoencoder is trained

  • Types of Autoencoders3:57

    Learn about the most used Autoencoder variants.

    • Know the various types of Autoencoders

    • Learn about their unique characteristics

  • Building Denoising Autoencoder Using PyTorch11:22

    Learn how to code an Autoencoder in PyTorch.

    • Learn the construction of an Autoencoder

    • Learn the way it needs to be trained

  • Training and Testing4:18

    Learn how to train and use an Autoencoder.

    • Visualize the training and validation loss

    • Learn how to use a Denoising Autoencoder on a single image

  • Reinforcement Learning Motivation6:10

    Get motivated towards the subject.

    • Know about the practical applications of reinforcement learning

  • Reinforcement Learning Concepts10:55

    Understand the key concepts in reinforcement learning.

    • Learn the actors in reinforcement learning

    • Understand terms like state, action, reward, policy

    • Understand the Q-learning algorithm to train agents

  • DQN, Experience Replay6:07

    Understand the Deep Q-Network algorithm and how to better train it.

    • Learn the construction of DQN

    • Understand how Experience replay helps train the network better

  • The OpenAI Gym Environment6:10

    Get introduced to the OpenAI gym environment.

    • Know about the OpenAI project

    • See how to setup and initialize the environment

  • Building the Cartpole Agent Using DQN8:26

    Build a reinforcement learning agent

    • Understand the various parts of the code to create the agent

    • Build everything using Python and PyTorch

  • Training and Testing9:51

    Train and test run the agent.

    • Train the agent and monitor it’s progress

    • Run the agent standalone and see how it performs

Requirements

  • A basic understanding of Deep Learning and Python programming knowledge is assumed.

Description

PyTorch: written in Python, is grabbing the attention of all data science professionals due to its ease of use over other libraries and its use of dynamic computation graphs.

PyTorch is a Deep Learning framework that is a boon for researchers and data scientists. It supports Graphic Processing Units and is a platform that provides maximum flexibility and speed. With PyTorch, you can dynamically build neural networks and easily perform advanced Artificial Intelligence tasks.

This comprehensive 2-in-1 course takes a practical approach and is filled with real-world examples to help you create your own application using PyTorch! Begin with exploring PyTorch and the impact it has made on Deep Learning. Design and implement powerful neural networks to solve some impressive problems in a step-by-step manner. Build a Convolutional Neural Network (CNN) for image recognition. Also, predict share prices with Recurrent Neural Network and Long Short-Term Memory Network (LSTM). You’ll learn how to detect credit card fraud with autoencoders and much more!

By the end of the course, you’ll conquer the world of PyTorch to build useful and effective Deep Learning models with the PyTorch Deep Learning framework with the help of real-world examples!

Contents and Overview

This training program includes 2 complete courses, carefully chosen to give you the most comprehensive training possible.

The first course, Deep Learning with PyTorch, covers building useful and effective deep learning models with the PyTorch Deep Learning framework. In this course, you will learn how to accomplish useful tasks using Convolutional Neural Networks to process spatial data such as images and using Recurrent Neural Networks to process sequential data such as texts. You will explore how you can make use of unlabeled data using Auto-Encoders. You will also be training a neural network to learn how to balance a pole all by itself, using Reinforcement Learning. Throughout this journey, you will implement various mechanisms of the PyTorch framework to do these tasks. By the end of the video course, you will have developed a good understanding of, and feeling for, the algorithms and techniques used. You'll have a good knowledge of how PyTorch works and how you can use it in to solve your daily machine learning problems.

The second course, Deep Learning Projects with PyTorch, covers creating deep learning models with the help of real-world examples. The course starts with the fundamentals of PyTorch and how to use basic commands. Next, you’ll learn about Convolutional Neural Networks (CNN) through an example of image recognition, where you’ll look into images from a machine perspective. The next project shows you how to predict character sequence using Recurrent Neural Networks (RNN) and Long Short Term Memory Network (LSTM). Then you’ll learn to work with autoencoders to detect credit card fraud. After that, it’s time to develop a system using Boltzmann Machines, where you’ll recommend whether to watch a movie or not. By the end of the course, you’ll be able to start using PyTorch to build Deep Learning models by implementing practical projects in the real world. So, grab this course as it will take you through interesting real-world projects to train your first neural nets.

By the end of the course, you’ll conquer the world of PyTorch to build useful and effective Deep Learning models with the PyTorch Deep Learning framework!

About the Authors

  • AnandSahais a software professional with 15 years' experience in developing enterprise products and services. Back in 2007, he worked with machine learning to predict call patterns at TATA Communications. At Symantec and Veritas, he worked on various features of an enterprise backup product used by Fortune 500 companies. Along the way, he nurtured his interests in Deep Learning by attending Coursera and Udacity MOOCs. He is passionate about Deep Learning and its applications; so much so that he quit Veritas at the beginning of 2017 to focus full time on Deep Learning practices. Anand built pipelines to detect and count endangered species from aerial images, trained a robotic arm to pick and place objects, and implemented NIPS papers. His interests lie in computer vision and model optimization.


  • AshishSingh Bhatia is a learner, reader, seeker, and developer at the core. He has over 10 years of IT experience in different domains, including banking, ERP, and education. He is persistently passionate about Python, Java, R, and web and mobile development. He is always ready to explore new technologies.

Who this course is for:

  • Python programmers, Data Science professionals who would like to practically implement PyTorch and explore its unique features in their Deep Learning projects.

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