Deep Learning with Python and PyTorch

Deep Learning with Python and PyTorch

Are you ready to go on a journey into the world of deep learning with the powerful Python and PyTorch? This course will be your guide through the joys and dangers of this new wave of machine learning.

Python is quickly becoming the technology of choice for deep learning and machine learning, because of its ease to develop powerful neural networks and intelligent machine learning applications. 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 comprehensive 2-in-1 course will teach you deep learning with Python and PyTorch in an easy-to-understand, practical manner with the help of use cases based on real-world datasets. To begin with, you will create neural networks and deep learning models to predict data and to solve some problems based on the scenarios in the use cases. Next, you will learn how to use 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 will learn to create Deep Neural Network (DNN) to paint unique images.

Contents and Overview

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

In the first course, Real-World Python Deep Learning Projects, you will start of by creating neural networks to predict the demand for airline travel in the future. You will then run through a scenario where you have to identify negative tweets for a celebrity by using Convolutional Neural Networks (CNN's). Next, you will create a neural network which will be able to identify smiles in your camera app. Finally, the last project will help you forecast a company's stock prices for the next day using deep learning.

 In the second course, Deep Learning Adventures with PyTorch, you will 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 will channel your inner Picasso by using Deep Neural Network (DNN) to paint unique images.

By the end of this course, you will be ready to use Python and PyTorch proficiently in your real-world deep learning projects.
Meet Your Expert(s):

We have the best work of the following esteemed author(s) to ensure that your learning journey is smooth:

Jakub Konczyk has enjoyed programming professionally since 1995. He is a Python and Django expert and has been involved in building complex systems since 2006. He loves to simplify and teach programming subjects and share them with others. He first discovered Machine Learning when he was trying to predict real estate prices in one of the early stage startups he was involved in. He failed miserably. Then he discovered a much more practical way to learn Machine Learning, which he would like to share with you in this course. It boils down to the Keep it simple! mantra.

Step into the world of Python and PyTorch to build useful and effective deep learning models for images, text, and more

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What you will learn

• Create neural networks to predict the demand for airline travel in the future
• Identify negative tweets on Twitter by using Convolutional Neural Networks (CNN's)
• Create a neural network which will identify smiles in your camera app

Rating: 3.91667

Level: Beginner Level

Duration: 6.5 hours

Instructor: Packt Publishing