by Ashish Singh Bhatia | Released June 2018 | ISBN: 9781788997591

Step into the world of PyTorch to create deep learning models with the help of real-world examplesAbout This VideoLearn to use PyTorch Open Source Deep Learning frameworkDive into specific Deep Learning concepts using real-world projectsBuild and train neural networks to make them more efficientIn DetailPyTorch 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.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.We'll continue with Boltzmann Machines, where you'll learn to give movie ratings using AutoEncoders. In the end, you'll get to develop and train a model to recognize a picture or an object from a given image using Deep Learning, where we'll not only detect the shape, but also the color of the object.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. Show and hide more

1. Chapter 1 : Getting Ready with PyTorch
   • The Course Overview 00:02:11
   • Using PyTorch 00:11:08
   • Understanding Regression 00:05:22
   • Linear Regression and Logistic Regression 00:09:37
2. Chapter 2 : Convolutional Neural Network
   • Understanding Convolutional Neural Network 00:07:55
   • Looking into Images from a Machine Perspective 00:06:16
   • Making CNN 00:09:04
   • Pooling Layers 00:05:03
   • Output Layer 00:06:05
3. Chapter 3 : Understanding RNN and LSTM
   • Understanding Recurrent Neural Network 00:11:14
   • Making RNN for Prediction 00:08:31
   • Why LSTM? 00:14:04
   • Moving to LSTM 00:02:56
4. Chapter 4 : Using Autoencoders for Fraud Detection
   • Getting Ready with Data 00:06:56
   • Developing a Model 00:07:30
   • Getting Output 00:02:18
5. Chapter 5 : Recommending a Movie with Boltzmann Machines
   • Introduction to Boltzmann Machines 00:11:00
   • Getting Ready for Recommender System 00:04:46
   • Making Boltzmann Machines 00:16:25
   • Getting Output 00:01:04
6. Chapter 6 : Movie Rating Using a Autoencoders
   • Introduction to Autoencoders 00:08:41
   • Getting Ready for Recommender System 00:03:29
   • Making Autoencoders 00:06:22
   • Getting Output 00:01:01
7. Chapter 7 : Making Model for Object Recognition
   • Getting Ready with Data 00:04:05
   • Developing a Model 00:04:35
   • Getting Output 00:02:47
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