English | MP4 | AVC 1920×1080 | AAC 48KHz 2ch | 3 Hours | 701 MB
Step into the world of PyTorch to create deep learning models with the help of real-world examples
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.
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.
This course takes a practical approach and is filled with real-world examples to help you create your own application using PyTorch.
What You Will Learn
- Strengthen your foundations by understanding PyTorch and its fundamentals
- Run your first basic commands using PyTorch
- See how to make 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
Table of Contents
Getting Ready with PyTorch
1 The Course Overview
2 Using PyTorch
3 Understanding Regression
4 Linear Regression and Logistic Regression
Convolutional Neural Network
5 Understanding Convolutional Neural Network
6 Looking into Images from a Machine Perspective
7 Making CNN
8 Pooling Layers
9 Output Layer
Understanding RNN and LSTM
10 Understanding Recurrent Neural Network
11 Making RNN for Prediction
12 Why LSTM
13 Moving to LSTM
Using Autoencoders for Fraud Detection
14 Getting Ready with Data
15 Developing a Model
16 Getting Output
Recommending a Movie with Boltzmann Machines
17 Introduction to Boltzmann Machines
18 Getting Ready for Recommender System
19 Making Boltzmann Machines
20 Getting Output
Movie Rating Using a Autoencoders
21 Introduction to Autoencoders
22 Getting Ready for Recommender System
23 Making Autoencoders
24 Getting Output
Making Model for Object Recognition
25 Getting Ready with Data
26 Developing a Model
27 Getting Output
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