Deep Learning with TensorFlow and Keras in Python: A Hands-On Guide to Building Neural Networks with Python: Build AI Systems with CNNs, RNNs, and Transformers in Python

What You Will Learn in This Book Build a Solid Foundation in Deep Learning: Gain a clear understanding of the core concepts of AI, machine learning, and deep learning, including the power of neural networks and the factors driving their current popularity. Master the TensorFlow and Keras Ecosystem: Learn to use TensorFlow 2.x and Keras to build, train, and evaluate your own deep learning models. You'll understand key components like Tensors, Layers, Optimizers, and Loss Functions, and learn to set up your own complete deep learning environment. Create Your Own Neural Networks: Get hands-on experience building foundational neural networks for classification and regression problems, learning the complete Keras workflow from defining models to making predictions and visualizing training history. Prevent Common Modeling Pitfalls: Understand the critical concepts of overfitting and underfitting and learn practical regularization techniques like Dropout, L1/L2 regularization, and Early Stopping to build more robust and generalizable models. Develop Advanced Computer Vision Solutions: Master Convolutional Neural Networks (CNNs) and their core components to build powerful image classification models. You'll learn how to work with image data, apply data augmentation, and use state-of-the-art pre-trained models with transfer learning. Work with Sequential Data like Text and Time Series: Dive into Recurrent Neural Networks (RNNs), including LSTMs and GRUs, to handle sequential data. You'll learn to prepare data for time series forecasting and perform sentiment analysis on text using word embeddings. Explore Cutting-Edge Architectures: Get a conceptual introduction to advanced models like Autoencoders for dimensionality reduction, Generative Adversarial Networks (GANs) for creating new data, and the groundbreaking Transformer architecture that powers modern NLP. Deploy Your Models to Production: Learn how to save your trained models in the recommended SavedModel format and explore different deployment strategies using TensorFlow Serving for web applications, TensorFlow Lite for mobile devices, and more. Enhance Model Performance and Interpretability: Discover techniques for hyperparameter tuning to optimize your models, and use tools like TensorBoard for visualization and debugging. You will also learn the basics of Explainable AI (XAI) to understand and interpret your model's predictions. Navigate the Ethical Landscape of AI: Understand the challenges of bias, fairness, and accountability in deep learning models and learn about responsible AI development practices.