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Learning Deep Learning

Unit 2

Pearson
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Reading list

We've selected 50 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Learning Deep Learning: Unit 2.
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Deep Learning
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Provides a comprehensive overview of deep learning, including convolutional neural networks. It is written by three leading researchers in the field and is suitable for both beginners and experienced practitioners.
Deep Learning (Adaptive Computation and Machine...
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Deep Learning (Adaptive Computation and Machine...
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Python Deep Learning Projects
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Provides a comprehensive overview of word embeddings, covering their creation, evaluation, and applications in natural language processing. It is written by a leading researcher in the field and is suitable for both beginners and experienced practitioners.
Python Deep Learning Projects
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Python Deep Learning Projects: 9 projects...
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Supervised Sequence Labelling with Recurrent Neural...
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Alex Graves's landmark monograph introduces bidirectional LSTMs and Connectionist Temporal Classification (CTC), laying the mathematical and algorithmic foundations for end-to-end speech recognition, handwriting transcription, and modern sequence labeling.
Supervised Sequence Labelling with Recurrent Neural...
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Speech and Language Processing. An Introduction to...
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This widely recognized and comprehensive textbook that provides a deep dive into the subject of language processing. It is suitable for both undergraduate and graduate-level courses and is considered a must-read for gaining a strong theoretical and applied understanding of NLP. The authors are leading researchers in the field, and the book is frequently updated to reflect advancements. The latest draft is available online.
Speech and Language Processing An Introduction to...
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Natural Language Processing with Transformers,...
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Co-authored by creators of the Hugging Face ecosystem, the revised edition serves as the definitive practitioner manual for training, fine-tuning, and deploying encoder-decoder and decoder-only transformers. Readers dissect multi-head self-attention, tokenization pipelines, and model quantization through production PyTorch code.
Natural Language Processing with Transformers,...
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Natural Language Processing with Transformers,...
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Hands-On Machine Learning with Scikit-Learn, Keras,...
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Serves as the definitive modern hands-on standard for machine learning, guiding practitioners through concrete scikit-learn workflows before deeply exploring TensorFlow 2 and Keras architectures.
Hands-On Machine Learning with Scikit-Learn, Keras,...
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Hands-On Machine Learning with Scikit-Learn, Keras,...
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Hands-On Machine Learning with Scikit-Learn, Keras,...
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Build a Large Language Model (from Scratch)
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Demystifies transformer internals by guiding engineers step by step through coding a GPT-style architecture in pure PyTorch without relying on high-level library abstractions. Covers causal self-attention, positional embeddings, layer normalization, pretraining loops, and instruction fine-tuning on consumer hardware.
Build a Large Language Model (from Scratch)
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Build a Large Language Model (from Scratch)
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Recurrent Neural Networks
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Dedicated exclusively to recurrent architectures, Fathi Salem systematically analyzes Elman networks, Jordan networks, LSTM variants, Peephole connections, and Gated Recurrent Units (GRUs). Dynamical systems stability analysis clarifies vanishing and exploding gradient behavior.
Recurrent Neural Networks: From Simple to Gated...
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Recurrent Neural Networks: From Simple to Gated...
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Deep Learning with Python
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Authored by the creator of Keras, this canonical text delivers crystal-clear conceptual intuition and elegant Python implementations for computer vision, NLP, and generative deep learning pipelines.
Deep Learning with Python, Second Edition
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Deep Learning with Python
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(Español) Deep Learning con Python
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(Polski) Deep Learning Praca z jezykiem Python i biblioteka...
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(Deutsch) Deep Learning mit Python und Keras: Das Praxis...
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(Italiano) Deep Learning con Python: Imparare a implementare...
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Handbook of Face Recognition
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Provides a comprehensive overview of face detection and recognition, covering topics such as face detection algorithms, feature extraction methods, and recognition algorithms.
Handbook of Face Recognition
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Handbook of Face Recognition
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Handbook of Face Recognition: The Deep Neural...
Hardcover
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Handbook of Face Recognition
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Handbook of Face Recognition
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Neural Network Methods in Natural Language...
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Yoav Goldberg provides the clearest pedagogical exposition of sequence modeling with recurrent neural networks—covering stacked RNNs, bidirectional encoders, sequence-to-sequence transducers, and tree-structured recursive networks for linguistic tasks.
Neural Network Methods in Natural Language...
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Neural Network Methods in Natural Language...
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Hands-On Large Language Models
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Renowned for intuitive visual pedagogy, Jay Alammar and Maarten Grootendorst illustrate how representation and generation transformers process tokens, contextual embeddings, and dense retrieval vectors. Ideal for developers seeking rigorous geometric intuition alongside practical semantic search and fine-tuning workflows.
Hands-On Large Language Models: Language...
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Hands-On Large Language Models: Language...
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TensorFlow in Action
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Walks developers through realistic deep learning challenges using TensorFlow 2, emphasizing clean Keras syntax, custom model subclassing, data loading pipelines, and deployment-ready training loops.
TensorFlow in Action
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TensorFlow in Action
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Recurrent Neural Networks for Short-Term Load...
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Benchmarking Elman RNNs, LSTMs, GRUs, NARX networks, and Echo State Networks on real-world multivariate time-series datasets, Filippo Maria Bianchi and colleagues provide a practical engineering guide to recurrent sequence forecasting and regularization.
Recurrent Neural Networks for Short-Term Load...
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Recurrent Neural Networks for Short-Term Load...
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Building Machine Learning Pipelines
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Covers the mechanics of deploying production pipelines with TensorFlow Extended (TFX), focusing on artifact lineage, data validation at scale, model analysis, and resilient orchestrations.
Building Machine Learning Pipelines: Automating...
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Building Machine Learning Pipelines: Automating...
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LLM Engineer's Handbook
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Examines production-grade engineering patterns for training and serving large language models, addressing distributed GPU training, synthetic data pipelines, and quantitative model evaluations.
LLM Engineer's Handbook: Master the art of...
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LLM Engineer's Handbook: Master the art of...
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Machine Learning in Production
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Bridges neural model experimentation and production delivery, demonstrating how to containerize TensorFlow pipelines, maintain automated model registries, and monitor data drift in live cloud deployments.
Machine Learning in Production
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Machine Learning in Production
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Deep Learning with PyTorch
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An updated edition of the popular 'Deep Learning with PyTorch', this book includes new content on transformers and generative AI models, reflecting contemporary advancements in the field. It builds upon the foundational knowledge of the first edition, making it valuable for those seeking to stay current with PyTorch and deep learning.
Deep Learning with PyTorch: Build, train, and tune...
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Deep Learning with PyTorch: Build, train, and tune...
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AI Engineering
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Bridges foundational transformer architecture with production systems engineering, examining how attention bottlenecks, KV-cache memory footprints, and context length constraints govern real-world deployment. Equips practitioners to evaluate foundation models, architect retrieval-augmented pipelines, and optimize inference latency.
AI Engineering
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AI Engineering
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Dive into Deep Learning
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Delivers an interactive, comprehensive curriculum teaching multi-layer perceptrons, convolutional neural networks, attention mechanisms, and optimization math with runnable multi-framework code.
Dive into Deep Learning
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Natural Language Processing with TensorFlow
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Investigates deep neural text architectures in TensorFlow, detailing embedding representations, recurrent layers, attention modules, and token classification for modern language tasks.
Natural Language Processing with TensorFlow: The...
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Linguistically Motivated Statistical Machine...
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Provides a systematic introduction to Neural Machine Translation, covering core methods and cutting-edge research topics. It is structured into foundations, principles, practices, and frontiers, offering a comprehensive view of the field. It valuable resource for Chinese-speaking students and researchers interested in NMT.
Linguistically Motivated Statistical Machine...
Hardcover
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Foundations of Statistical Natural Language...
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This foundational text offers a comprehensive introduction to statistical methods in NLP. It covers the necessary theory and algorithms for building NLP tools, with a rigorous treatment of mathematical and linguistic foundations. While published in 1999, it remains highly relevant for understanding the statistical underpinnings of many NLP techniques and valuable reference for researchers and students alike.
Foundations of Statistical Natural Language...
Hardcover
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Foundations of Statistical Natural Language...
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Learning OpenCV 3
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Provides a comprehensive overview of object recognition, covering topics such as feature detection, object tracking, and scene understanding.
Learning OpenCV 3: Computer Vision in C++ with the...
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Learning OpenCV 3: Computer Vision in C++ with the...
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Advanced Deep Learning with Keras
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Implementing production deep learning architectures in Keras, Rowel Atienza demonstrates how recurrent layers, stacked LSTMs, and recurrent autoencoders model sequential dependencies across language generation and temporal representation learning.
Advanced Deep Learning with Keras
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Advanced Deep Learning with Keras
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Computer Vision: A Modern Approach
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Provides a comprehensive overview of computer vision, covering topics such as image formation, feature detection, object recognition, and video analysis.
Computer Vision: A Modern Approach
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Computer Vision: A Modern Approach
Hardcover
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Transformers for Natural Language Processing and...
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Expands beyond text-only models to demonstrate how Vision Transformers (ViT), CLIP, and multimodal architectures unify image patch embeddings with sequence attention. Includes hands-on notebooks covering BERT, GPT-4, Hugging Face pipelines, and cross-modal representation learning.
Transformers for Natural Language Processing and...
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Transformers for Natural Language Processing and...
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Autonomous Vehicle Technology
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Provides a comprehensive overview of autonomous vehicle technology, covering topics such as sensor systems, perception algorithms, and control systems.
Autonomous Vehicle Technology: A Guide for...
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Computer Vision
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Provides a comprehensive overview of computer vision algorithms and applications, covering topics such as image formation, feature detection, object recognition, and video analysis.
Computer Vision
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Computer Vision
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Deep Learning for Coders with fastai and PyTorch
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Takes a top-down approach, focusing on practical applications of deep learning using the fastai library, which is built on PyTorch. It quickly gets readers building models for computer vision, natural language processing, and tabular data, while also covering underlying concepts. It's highly recommended for those who want to get hands-on with PyTorch quickly and see it applied to real-world problems.
Deep Learning for Coders with Fastai and PyTorch:...
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Deep Learning for Coders with fastai and PyTorch:...
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Generative Deep Learning
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Instructs engineers on building generative neural models, from variational autoencoders and generative adversarial networks to diffusion models and multimodal text-to-image synthesis pipelines.
Generative Deep Learning: Teaching Machines To...
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Generative Deep Learning: Teaching Machines to...
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Generative Deep Learning: Teaching Machines To...
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(Français) Deep learning génératif
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(Deutsch) Generatives Deep Learning: Maschinen das Malen,...
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(中文) Generative deep learning(Chinese Edition)
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LLM Systems in Production
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Tackles end-to-end serving bottlenecks for enterprise deep learning systems, offering rigorous recipes for hardware acceleration, tensor distributed execution, and real-time model inference.
LLM Systems in Production
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LLM Systems in Production
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Cover image
Deep Learning
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Establishes the mathematical bedrock of sequence modeling, optimization, and representation learning that motivated the shift from recurrent networks and sequence-to-sequence attention to modern transformers. Indispensable for mastering backpropagation, regularization, and probabilistic deep learning.
Deep Learning
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Deep Learning (Adaptive Computation and Machine...
Hardcover
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Deep Learning (Adaptive Computation and Machine...
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Understanding Deep Learning
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Offers a mathematically lucid, modern graduate treatment of dot-product self-attention, multi-head projections, positional encodings, and residual connections. Connects transformer mechanics directly to graph neural networks, diffusion models, and overparameterized optimization dynamics.
Understanding Deep Learning
Hardcover
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Understanding Deep Learning
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Python Machine Learning
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Sebastian Raschka and Vahid Mirjalili clearly illustrate how hidden-to-hidden recurrent recurrences work under the hood, guiding readers through character-level language generation and sentiment analysis using multi-layer RNNs and LSTMs.
Machine Learning with PyTorch and Scikit-Learn:...
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Python Machine Learning: Machine Learning and Deep...
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Python Machine Learning - Second Edition: Machine...
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(Español) Machine Learning con PyTorch y Scikit-Learn:...
Paperback
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(Español) Python Machine Learning (Spanish Edition)
Kindle Edition
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(Italiano) Machine Learning con Python - Nuova edizione:...
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(Deutsch) Machine Learning mit Python und Keras, TensorFlow 2...
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Natural Language Processing with Python
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Serves as a practical introduction to NLP with a strong focus on using the Natural Language Toolkit (NLTK) library in Python. It's excellent for beginners and those who want a hands-on approach to learning NLP concepts through programming examples. It's widely used as a textbook and provides a solid foundation for implementing basic NLP tasks.
Natural Language Processing with Python: Analyzing...
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(中文) Python Natural Language Processing(Chinese Edition)
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Natural Language Processing with Python: Analyzing...
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The Art of Chunking in AI Systems
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Explores high-performance neural vector processing and token chunking strategies, bridging deep embedding generation in TensorFlow with modern retrieval-augmented pipelines.
The Art of Chunking in AI Systems
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The Art of Chunking in AI Systems
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Hands-On Computer Vision with TensorFlow 2
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Demonstrates practical visual recognition techniques, guiding readers through convolutional networks, transfer learning, object detection, and image segmentation with practical Keras code.
Hands-On Computer Vision with TensorFlow 2:...
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Hands-On Computer Vision with TensorFlow 2:...
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Introduction to Natural Language Processing
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This textbook offers a technical perspective on NLP, synthesizing classical methods with contemporary machine learning techniques. , It covers topics from basic textual analysis to structured representations and semantic analysis, making it suitable for advanced undergraduate and graduate courses. It requires a background in programming and college-level mathematics.
Introduction to Natural Language Processing...
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Introduction to Natural Language Processing...
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Time Series Forecasting in Python
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Applying deep learning to multi-step temporal forecasting, Marco Peixeiro shows how to structure sliding-window datasets and train LSTM and GRU architectures alongside autoregressive baselines for high-dimensional sequential signals.
Time Series Forecasting in Python
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Time Series Forecasting in Python
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Advanced Deep Learning with TensorFlow 2 and Keras
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Unpacks advanced generative models and reinforcement learning techniques, detailing variational autoencoders, generative adversarial networks, and energy-based models with clean TensorFlow 2 implementations.
Advanced Deep Learning with TensorFlow 2 and Keras:...
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Advanced Deep Learning with TensorFlow 2 and Keras:...
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Designing Large Language Model Applications
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Synthesizes transformer internals with enterprise software architecture, breaking down attention variants, tokenization trade-offs, domain fine-tuning, and KV-cache inference optimization. Equips engineers to build dependable LLM pipelines using retrieval-augmented generation, structured outputs, routing cascades, and systematic failure mitigation.
Designing Large Language Model Applications: A...
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Designing Large Language Model Applications: A...
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Local Models That Don't Feel Dumb
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Analyzes edge-inference architectures for open-weights neural models, tackling parameter quantization, GGUF execution, KV cache memory bottlenecks, and efficient desktop serving.
Local Models That Don't Feel Dumb
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Local Models That Don't Feel Dumb
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Modern Time Series Forecasting with Python
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Jeffrey Tackes and Manu Joseph update industrial time-series pipelines for modern Python, showing how LSTMs, GRUs, and attention-augmented recurrent architectures handle multi-horizon forecasting, probabilistic intervals, and feature engineering.
Modern Time Series Forecasting with Python:...
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Modern Time Series Forecasting with Python:...
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Statistical Machine Translation
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Considered a classic in the field of Machine Translation, this book provides a thorough grounding in statistical methods, which were the dominant paradigm before the rise of neural networks. While not covering the latest neural approaches in depth, it is essential for understanding the historical context and foundational concepts of MT. It valuable reference for anyone studying the evolution of MT.
Statistical Machine Translation
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Statistical Machine Translation
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Mastering PyTorch
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Delves into more advanced PyTorch techniques for building and deploying complex deep learning models, including CNNs, RNNs, transformers, and generative models. It covers topics like optimizing training with multiple GPUs and deploying models to production, making it suitable for those looking to deepen their understanding and apply PyTorch in a professional setting.
Mastering PyTorch: Create and deploy deep learning...
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Building Transformer Models with PyTorch 2.0
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Demonstrates how to leverage PyTorch 2.0 compiler optimizations and torch.compile to accelerate transformer training, attention layer fusion, and distributed model parallelism.
Building Transformer Models with PyTorch 2.0: NLP,...
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Building Transformer Models with PyTorch 2.0: NLP,...
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Digital Image Processing
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Provides a comprehensive overview of digital image processing, covering topics such as image acquisition, image enhancement, image compression, and image segmentation.
Digital Image Processing (3rd Edition)
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Hands-On Generative AI with Transformers and...
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Connects language transformers with multimodal generative architectures through practical code written by core Hugging Face contributors. Shows practitioners how cross-attention conditioning, encoder embeddings, and parameter-efficient fine-tuning bridge text, vision, and audio synthesis workflows.
Hands-On Generative AI with Transformers and...
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Hands-On Generative AI with Transformers and...
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Deep Learning with TensorFlow and Keras
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Presents a well-structured tour of foundational neural patterns, covering feedforward networks, autoencoders, and modern optimization strategies across the TensorFlow 2 ecosystem.
Deep Learning with TensorFlow and Keras - Third...
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Deep Learning with TensorFlow 2 and Keras - Second...
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Deep Learning with TensorFlow and Keras: Build and...
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Deep Learning with TensorFlow 2 and Keras:...
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