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• Learn how to define your own network architecture in deep learning
• Implement helpful methods to create and train a model using PyTorch syntax
• Discover how intelligent applications using features like image recognition and speech recognition really process your data

Book Description

Want to get to grips with one of the most popular machine learning libraries for deep learning? The Deep Learning with PyTorch Workshop will help you do just that, jumpstarting your knowledge of using PyTorch for deep learning even if you're starting from scratch.

It's no surprise that deep learning's popularity has risen steeply in the past few years, thanks to intelligent applications such as self-driving vehicles, chatbots, and voice-activated assistants that are making our lives easier. This book will take you inside the world of deep learning, where you'll use PyTorch to understand the complexity of neural network architectures.

The Deep Learning with PyTorch Workshop starts with an introduction to deep learning and its applications. You'll explore the syntax of PyTorch and learn how to define a network architecture and train a model. Next, you'll learn about three main neural network architectures - convolutional, artificial, and recurrent - and even solve real-world data problems using these networks. Later chapters will show you how to create a style transfer model to develop a new image from two images, before finally taking you through how RNNs store memory to solve key data issues.

By the end of this book, you'll have mastered the essential concepts, tools, and libraries of PyTorch to develop your own deep neural networks and intelligent apps.

What you will learn

• Explore the different applications of deep learning
• Understand the PyTorch approach to building neural networks
• Create and train your very own perceptron using PyTorch
• Solve regression problems using artificial neural networks (ANNs)
• Handle computer vision problems with convolutional neural networks (CNNs)
• Perform language translation tasks using recurrent neural networks (RNNs)

Who this book is for

This deep learning book is ideal for anyone who wants to create and train deep learning models using PyTorch. A solid understanding of the Python programming language and its packages will help you grasp the topics covered in the book more quickly.

• Discover how to apply the scikit-learn uniform API in all types of machine learning models
• Understand the difference between supervised and unsupervised learning models
• Reinforce your understanding of machine learning concepts by working on real-world examples

Book Description

Machine learning algorithms are an integral part of almost all modern applications. To make the learning process faster and more accurate, you need a tool flexible and powerful enough to help you build machine learning algorithms quickly and easily. With The Machine Learning Workshop, you'll master the scikit-learn library and become proficient in developing clever machine learning algorithms.

The Machine Learning Workshop begins by demonstrating how unsupervised and supervised learning algorithms work by analyzing a real-world dataset of wholesale customers. Once you've got to grips with the basics, you'll develop an artificial neural network using scikit-learn and then improve its performance by fine-tuning hyperparameters. Towards the end of the workshop, you'll study the dataset of a bank's marketing activities and build machine learning models that can list clients who are likely to subscribe to a term deposit. You'll also learn how to compare these models and select the optimal one.

By the end of The Machine Learning Workshop, you'll not only have learned the difference between supervised and unsupervised models and their applications in the real world, but you'll also have developed the skills required to get started with programming your very own machine learning algorithms.

What you will learn

• Understand how to select an algorithm that best fits your dataset and desired outcome
• Explore popular real-world algorithms such as K-means, Mean-Shift, and DBSCAN
• Discover different approaches to solve machine learning classification problems
• Develop neural network structures using the scikit-learn package
• Use the NN algorithm to create models for predicting future outcomes
• Perform error analysis to improve your model's performance

Who this book is for

The Machine Learning Workshop is perfect for machine learning beginners. You will need Python programming experience, though no prior knowledge of scikit-learn and machine learning is necessary.