LEARN KERAS: Master Neural Networks and Deep Learning with Python - Brossura

Smart Tech Content, StudioD21; Rodrigues, Diego

Brossura

ISBN 13: 9798315178934

Casa editrice: Independently published, 2025

Riassunto

Learn how to build Deep Learning models with Keras, the high-level library based onTensorFlow and the most widely used for neural networks in Python. This book offers a technical, straight-to-the-point guide that takes you from the fundamentals of neural networks to advanced applications with CNNs, RNNs, LSTM, Transfer Learning, NLP, and MLOps.

You will learn how to set up the ideal development environment, build architectures using the sequential model and the functional API, tune hyperparameters, apply regularization, interpret networks with Grad-CAM, and monitor results with TensorBoard. Additionally, this manual details integration with libraries such as Pandas, Matplotlib, and Scikit-Learn, enabling complete workflows for modeling, evaluation, and deployment in production using APIs or serverless environments.

The book covers essential topics such as training with GPUs and TPUs, tuning with Keras Tuner, using callbacks, best practices with TensorFlow, and modern strategies for model engineering. Each chapter follows the TECHWRITE 2.1 Protocol, featuring clear explanations, practical examples, commented common errors, applicable best practices, and a full focus on immediate applicability.

Suitable for beginners in neural networks as well as professionals in data science, machine learning, and predictive automation, this book is designed to transform theory into real-world solutions, with a focus on technical clarity, progressive structure, and practical mastery of neural networks.

APRENDA Keras is your definitive step to begin your journey toward mastering Deep Learning with efficiency, robustness, and professional purpose. Ideal for those who want to master artificial intelligence with depth and pragmatism.

Dettagli dell'edizione

• Editore: Independently published
• Data di pubblicazione: 2025
• Lingua: Inglese
• ISBN 13: 9798315178934
• Rilegatura: Copertina flessibile
• Numero di pagine: 198
• Contatto del produttore: non disponibile
• Persona responsabile: non disponibile