Deep learning has become crucial for machine learning practitioners and even many software engineers with the comeback of neural networks in the 2010s. For data scientists and software developers with machine learning knowledge, this book offers a thorough introduction. Starting with the fundamentals of deep learning, you'll soon move on to the specifics of significant advanced architectures, building everything from scratch along the way.
The supervised learning methods simple linear regression, the traditional multilayer perceptron, and more complex deep convolutional networks are introduced in the first chapter of this book. Additionally, you'll study image processing including handwritten digit identification, image categorization, and sophisticated object detection with associated image annotations. Also presented is an example of how to identify key facial features.
Everything is changing because to deep learning. Traditional computer vision approaches are already being eclipsed by this machine-learning approach, and NLP is following suit. Using Facebook's PyTorch framework, this practical book will quickly get you up to speed on the essential ideas if you're trying to incorporate deep learning into your domain. After author Ian Pointer assists you in configuring PyTorch in a cloud-based setting, you will discover how to use the framework to build neural architectures that process text, audio, images, and other kinds of data. You will be able to build neural networks and train them on various kinds of data at the end of the book.
Artificial intelligence (AI) problems can be solved in countless ways thanks to natural language processing (NLP), which enables services like Google Translate and Amazon Alexa. This practical article demonstrates how to use pytorch, a Python-based deep learning library, to use NLP and deep learning techniques if you're a developer or data scientist who's new to these fields. The authors, Delip Rao and Brian McMahon, provide you a strong foundation in natural language processing (NLP) and deep learning methods. They also show you how to utilize Pytorch to create applications that leverage rich text representations that are tailored to your own issues. There are multiple code examples and graphics in every chapter.
