Applied Deep Learning
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Deep Learning for Coders with fastai and PyTorch
Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications. Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. Train models in computer vision, natural language processing, tabular data, and collaborative filtering Learn the latest deep learning techniques that matter most in practice Improve accuracy, speed, and reliability by understanding how deep learning models work Discover how to turn your models into web applications Implement deep learning algorithms from scratch Consider the ethical implications of your work Gain insight from the foreword by PyTorch cofounder, Soumith Chintala
Advanced Applied Deep Learning
Develop and optimize deep learning models with advanced architectures. This book teaches you the intricate details and subtleties of the algorithms that are at the core of convolutional neural networks. In Advanced Applied Deep Learning, you will study advanced topics on CNN and object detection using Keras and TensorFlow. Along the way, you will look at the fundamental operations in CNN, such as convolution and pooling, and then look at more advanced architectures such as inception networks, resnets, and many more. While the book discusses theoretical topics, you will discover how to work efficiently with Keras with many tricks and tips, including how to customize logging in Keras with custom callback classes, what is eager execution, and how to use it in your models. Finally, you will study how object detection works, and build a complete implementation of the YOLO (you only look once) algorithm in Keras and TensorFlow. By the end of the book you will have implemented various models in Keras and learned many advanced tricks that will bring your skills to the next level. What You Will Learn See how convolutional neural networks and object detection work Save weights and models on disk Pause training and restart it at a later stage Use hardware acceleration (GPUs) in your code Work with the Dataset TensorFlow abstraction and use pre-trained models and transfer learning Remove and add layers to pre-trained networks to adapt them to your specific project Apply pre-trained models such as Alexnet and VGG16 to new datasets Who This Book Is For Scientists and researchers with intermediate-to-advanced Python and machine learning know-how. Additionally, intermediate knowledge of Keras and TensorFlow is expected.
Applied Deep Learning
Author: Dr. Rajkumar Tekchandani
language: en
Publisher: BPB Publications
Release Date: 2023-04-29
A comprehensive guide to Deep Learning for Beginners KEY FEATURES ● Learn how to design your own neural network efficiently. ● Learn how to build and train Recurrent Neural Networks (RNNs). ● Understand how encoding and decoding work in Deep Neural Networks. DESCRIPTION Deep Learning has become increasingly important due to the growing need to process and make sense of vast amounts of data in various fields. If you want to gain a deeper understanding of the techniques and implementations of deep learning, then this book is for you. The book presents you with a thorough introduction to AI and Machine learning, starting from the basics and progressing to a comprehensive coverage of Deep Learning with Python. You will be introduced to the intuition of Neural Networks and how to design and train them effectively. Moving on, you will learn how to use Convolutional Neural Networks for image recognition and other visual tasks. The book then focuses on localization and object detection, which are crucial tasks in many applications, including self-driving cars and robotics. You will also learn how to use Deep Learning algorithms to identify and locate objects in images and videos. In addition, you will gain knowledge on how to create and train Recurrent Neural Networks (RNNs), as well as explore more advanced variations of RNNs. Lastly, you will learn about Generative Adversarial Networks (GAN), which are used for tasks like image generation and style transfer. WHAT YOU WILL LEARN ● Learn how to work efficiently with various Convolutional models. ● Learn how to utilize the You Only Look Once (YOLO) framework for object detection and localization. ● Understand how to use Recurrent Neural Networks for Sequence Learning. ● Learn how to solve the vanishing gradient problem with LSTM. ● Distinguish between fake and real images using various Generative Adversarial Networks. WHO THIS BOOK IS FOR This book is intended for both current and aspiring Data Science and AI professionals, as well as students of engineering, computer applications, and masters programs interested in Deep learning. TABLE OF CONTENTS 1. Basics of Artificial Intelligence and Machine Learning 2. Introduction to Deep Learning with Python 3. Intuition of Neural Networks 4. Convolutional Neural Networks 5. Localization and Object Detection 6. Sequence Modeling in Neural Networks and Recurrent Neural Networks (RNN) 7. Gated Recurrent Unit, Long Short-Term Memory, and Siamese Networks 8. Generative Adversarial Networks