Overview

Applied Deep Learning is a practical textbook for readers who want to build working deep learning systems without treating them as magic. Written for learners, instructors, and practitioners who know basic Python, the book connects neural-network ideas to code, experiments, figures, and failure modes. It starts with intuition-building examples such as TensorFlow Playground and MNIST, then moves through convolutional neural networks, training practice, transfer learning, embeddings, recurrent networks, attention, Transformers, large language models, retrieval-augmented generation, LoRA adaptation, reinforcement learning, vision Transformers, multimodal models, object detection, segmentation, image generation, speech recognition, text-to-speech, and advanced sequence and LLM systems. The focus is not on memorizing model names. The focus is on the habits that make deep learning useful in practice: representing data as tensors, building baselines, training carefully, reading learning curves, comparing accuracy with runtime, inspecting errors, debugging overfitting and underfitting, and knowing when a simpler method is enough. Most chapters pair concepts with runnable companion code, structured exercises, or larger homework tasks. The examples use tools from the modern Python deep learning ecosystem, including Keras, PyTorch, fast.ai, Hugging Face tooling, and LoRA-style adaptation workflows where they serve the lesson. If you want a grounded path from first neural-network experiments to modern applied AI systems, Applied Deep Learning gives you the vocabulary, workflows, and experimental discipline needed to understand what your models are allowed to learn, what evidence shows they learned it, and what to check when they fail.

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