English | MP4 | AVC 1920×1080 | AAC 48KHz 2ch | 2h 31m | 509 MB
Journey into the world of deep learning using PyTorch. Recognize images, translate languages, and paint unique pictures
Are you ready to go on a journey into the world of deep learning? This course will be your guide through the joys and dangers of this new wave of machine learning. Why? Because, let’s face it, getting started with deep learning is difficult. Tasks such as choosing between multiple frameworks, understanding APIs, and debugging code are hard. Is there an another way? Yes. Meet PyTorch. Like Python, PyTorch has a clean and simple API, which makes building neural networks faster and easier. It’s also modular, and that makes debugging your code a breeze. This course will be one hell of an adventure into the world of deep learning!
You’ll start by using Convolutional Neural Networks (CNNs) to classify images; Recurrent Neural Networks (RNNs) to detect languages; and then translate them using Long-Term-Short Memory (LTSM). Finally, you’ll channel your inner Picasso by using Deep Neural Network (DNN) to paint unique images.
By the end of your adventure, you will be ready to use PyTorch proficiently in your real-world projects.
In this course, you will complete your deep-learning journey with a trusted guide and use PyTorch to build interesting and useful deep learning projects. In each example you will learn how to solve a specific, practical Machine Learning problem.
What You Will Learn
- Intuitive ways to build neural networks using the PyTorch API to make this deep learning ride enjoyable
- Master PyTorch’s unique features gradually as you work through projects that make PyTorch perfect for rapid prototyping
- Debug your PyTorch code using standard Python tools, so you can easily fix bugs
- Work with PyTorch and learn its advantages over other frameworks, and choose the right vehicle for your deep-learning ride
- Get practical, project-based experience with this popular and in-demand deep-learning library
Table of Contents
First Stop – A Quick Introduction to PyTorch
1 The Course Overview
2 What Makes PyTorch Special
3 Installing PyTorch
Sleeping Under the Stars – It’s a Bird…It’s a Plane…It’s a CNN
4 Problem – Detect a Specific Type of Object in an Image
5 Quick Win – Using a Pretrained AlexNet Model for Beaver Detection
6 Getting and Preparing Image Data
7 Building, Training, and Testing Your Model
8 Using Your Model to Detect Beavers and What’s Next
Going Abroad – Language Detection for Fun and Profit with RNN
9 Problem – Recognize the Language of a Specific Text
10 Understanding and Preparing Language Data
11 Building, Training, and Testing Your Model for Language Detection
12 Using Your Model to Detect Languages and What’s Next
Making Friends – Lost in Translation with LSTM
13 Problem – Translate a Specific Text from One Language to Another
14 Understanding and Preparing Dataset for Language Translation
15 Building, Training, and Testing Your Models for Language Translation
16 Using Your Models for Language Translation
Getting Some Culture – Becoming a Deep Neural Picasso with DNN
17 Problem – Extract Key Style Features from One Image and Use It on Another One
18 Preparing Images for Style Transfer
19 Building and Training Style Transfer Model
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