Amita Kapoor Reihenfolge der Bücher (Chronologisch)

Build cutting edge machine and deep learning systems for the lab, production, and mobile devices Deep Learning with TensorFlow and Keras teaches you neural networks and deep learning techniques using TensorFlow (TF) and Keras. You'll learn how to write deep learning applications in the most powerful, popular, and scalable machine learning stack available. TensorFlow 2.x focuses on simplicity and ease of use, with updates like eager execution, intuitive higher-level APIs based on Keras, and flexible model building on any platform. This book uses the latest TF 2.0 features and libraries to present an overview of supervised and unsupervised machine learning models and provides a comprehensive analysis of deep learning and reinforcement learning models using practical examples for the cloud, mobile, and large production environments. This book also shows you how to create neural networks with TensorFlow, runs through popular algorithms (regression, convolutional neural networks (CNNs), transformers, generative adversarial networks (GANs), recurrent neural networks (RNNs), natural language processing (NLP), and graph neural networks (GNNs)), covers working example apps, and then dives into TF in production, TF mobile, and TensorFlow with AutoML. This hands-on machine learning book is for Python developers and data scientists who want to build machine learning and deep learning systems with TensorFlow. This book gives you the theory and practice required to use Keras, TensorFlow, and AutoML to build machine learning systems. Some machine learning knowledge would be useful. We don't assume TF knowledge.

Build machine and deep learning systems with TensorFlow 2 and Keras for lab, production, and mobile devices. This resource introduces TensorFlow 2 and Keras from the outset, teaching essential machine and deep learning techniques through clear explanations and extensive code samples. The second edition focuses on neural networks and deep learning alongside TensorFlow and Keras, enabling the creation of deep learning applications using a powerful and scalable machine learning stack. TensorFlow is the preferred library for professional applications, while Keras provides a user-friendly Python API for TensorFlow access. The book covers various applications, including regression, convolutional networks (CNNs), generative adversarial networks (GANs), recurrent neural networks (RNNs), and natural language processing (NLP). It includes two practical example apps and discusses deploying TensorFlow in production and mobile environments, as well as utilizing AutoML. Readers will learn to build machine learning systems, apply regression analysis, understand CNNs for image classification, generate new data with GANs, process sequences with RNNs, and automate ML workflows using Google tools. This book is ideal for Python developers and data scientists looking to enhance their machine learning and deep learning expertise with TensorFlow and Keras, assuming some prior knowledge of the field.