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# Introduction
When I first started learning AI, I spent a lot of time copying code from tutorials, but I realized I was not really understanding how it worked. The real skill is not just running models. It is knowing why they work and how to apply them to real problems. AI books helped me learn the concepts, the reasoning, and the practical side of AI in a way that no quick tutorial could. With this in mind, we are starting this series to recommend FREE but truly valuable books. This article is for all those who want to learn AI, and here are the first set of recommendations.
# 1. Neural Networks and Deep Learning
The book Neural Networks and Deep Learning takes you from the basics of neural networks to actually building and training deep models on your own. It begins with simple ideas like perceptrons and sigmoid neurons, then walks you through creating a network that can recognize handwritten digits. You also get to see how backpropagation really works to train these models, and how to improve them with things like cost functions, regularization, weight initialization, and tuning hyperparameters. There are a lot of Python code examples so you can test things yourself and see how everything connects. It mixes both intuition and math nicely, so you start to understand not just how neural networks work, but why. If you already know a bit of math (like linear algebra or calculus), this one’s a good pick to go beyond just using a library and actually know what’s happening under the hood.
// Overview of Outline:
- Foundations of Neural Networks (Perceptrons, sigmoid neurons, network architecture, classifying handwritten digits, gradient descent, implementing networks)
- Backpropagation and Learning (Matrix-based computation, cost function assumptions, Hadamard product, four fundamental backpropagation equations, algorithm implementation, improving learning)
- Advanced Training Techniques (Cross-entropy cost, overfitting & regularization, weight initialization, hyperparameter selection, universality of neural nets, extensions beyond sigmoid neurons)
- Deep Learning & Convolutional Networks (Vanishing gradient problem, unstable gradients, convolutional neural networks, practical implementations, recent progress in image recognition, future directions)
# 2. Deep Learning
Deep Learning gives a really good overview of deep learning and how machines actually learn from experience, building up complex ideas from the simpler ones. It starts with the math part you’ll need, like linear algebra, probability, information theory, and a bit of numerical computation, then goes through the basics of machine learning. After that, it goes deeper into modern deep learning methods like feedforward, convolutional and recurrent networks, regularization, and optimization, showing how they’re used in real projects. It also talks about some advanced topics like autoencoders, generative and representation learning, and structured probabilistic models. It’s mostly made for people with a solid math background, so it’s more like a proper reference for research or advanced work than a beginner’s guide.
// Overview of Outline:
- Factor Models & Autoencoders (PCA, ICA, sparse coding, undercomplete & regularized autoencoders, denoising, manifold learning)
- Representation Learning & Probabilistic Models (Layer-wise pretraining, transfer learning, distributed representations, structured probabilistic models, approximate inference, Monte Carlo methods)
- Deep Generative Models & Advanced Techniques (Boltzmann machines, deep belief networks, convolutional models, generative stochastic networks, autoencoder sampling, evaluating generative models)
# 3. Practical Deep Learning
Link:
The free course Practical Deep Learning is made for people who already know some coding and want to get hands-on with machine learning and deep learning. Instead of just reading theory, you’ll start building models for real tasks right away. The course covers modern tools like Python, PyTorch, and the fastai library, and shows you how to handle everything from data cleaning to model training, testing, and deployment. You’ll work with actual notebooks, datasets, and problems so you learn by doing. The focus is on practical, up-to-date methods for choosing the right algorithm, validating it properly, scaling it, and deploying it.
// Overview of Outline:
- Foundations & Model Training (Neural network basics, stochastic gradient descent, affine functions & nonlinearities, backpropagation, MLPs, autoencoders)
- Applications Across Domains (Computer vision with CNNs, natural language processing (NLP) including embeddings & phrase similarity, tabular data modeling, collaborative filtering & recommendations)
- Advanced Techniques & Optimization (Transfer learning, weight decay, data augmentation, accelerated stochastic gradient descent (SGD), ResNets, mixed precision, DDPM/DDIM, attention & transformers, latent diffusion, super-resolution)
- Deployment & Practical Skills (Turning models into web apps, improving accuracy/speed/reliability, ethical considerations, frameworks like The Learner, matrix operations, model initialization/normalization)
# 4. Artificial Intelligence: Foundations of Computational Agents
The book Artificial Intelligence: Foundations of Computational Agents explains AI through the idea of “computational agents,” systems that can sense, learn, reason, and act. The latest edition adds newer topics like neural networks, deep learning, causality, and the social and ethical sides of AI. It shows how agents are built, how they plan and act, and how they handle complex or uncertain situations. Each chapter includes algorithms in Python, case studies, and real-world discussions, so you learn both the how and the why. It’s a balanced mix of theory and practice, great for students or anyone who wants a modern and deep intro to AI.
// Overview of Outline:
- Foundations of AI and Agents (natural vs. artificial intelligence, historical context, agent design space, and examples like delivery robots, diagnostic assistants, tutoring systems, trading agents, and smart homes.)
- Agent Architectures & Control (hierarchical control, agent functions, offline vs. online computation, and how agents perceive and act within environments.)
- Reasoning, Planning & Search (problem-solving through search, graph traversal, constraint satisfaction, probabilistic reasoning, and planning methods including forward, regression, and partial-order planning)
- Learning & Neural Networks (supervised learning, decision trees, regression, overfitting, composite models like boosting, deep learning architectures (convolutional neural networks (CNNs), recurrent neural networks (RNNs), transformers), and large language models.)
- Uncertainty, Causality & Reinforcement Learning (probabilistic reasoning, Bayesian learning, unsupervised methods, causal inference, decision-making under uncertainty, sequential decisions, and reinforcement learning strategies like Q-learning and evolutionary algorithms.)
# 5. Ethical Artificial Intelligence
The paper Ethical Artificial Intelligence looks at how future AI systems might behave in ways we don’t expect or that could be harmful, and it suggests ways to design them safely. It starts by pointing out that AI may learn models of the world far more complex than humans can fully understand, which makes safeguards tricky. The authors recommend using utility functions (mathematical descriptions of what the AI should care about) rather than vague rules, because they make goals clearer. It also covers problems like self-delusion, where AI could corrupt its own observations or rewards, unintended “shortcut” actions that hurt us, and reward generator corruption, where AI manipulates its own reward system. The authors propose models that learn human values, use finite definitions, and include self-modeling so AI can reason about its own actions. It also considers the bigger picture, like how AI might impact society, politics, and humanity’s future.
// Overview of Outline:
- Foundations & AI Design (future AI vs. current AI, instructing AI, utility-maximizing agents, learning environment models, intelligence measures, ethical frameworks)
- AI Behavior & Challenges (self-delusion, unintended instrumental actions, model-based utility functions, learning human values, evolving and embedded agents)
- Testing, Governance & Society (AI testing, real-world behavior, political dimensions, transparency, allocation of benefits, ethical considerations)
- Philosophical & Societal Impact (quest for meaning, societal and cultural implications, bridging computation and human values)
# Wrapping Up
These books (and a paper, and a course) cover a wide range of what an AI engineer needs, from neural networks and deep learning to hands-on coding, agent-based AI, and ethical issues. They give a clear path from learning the ideas to applying AI in real-world situations. What topics would you like me to cover next? Drop your suggestions in the comments!
Kanwal Mehreen is a machine learning engineer and a technical writer with a profound passion for data science and the intersection of AI with medicine. She co-authored the ebook “Maximizing Productivity with ChatGPT”. As a Google Generation Scholar 2022 for APAC, she champions diversity and academic excellence. She’s also recognized as a Teradata Diversity in Tech Scholar, Mitacs Globalink Research Scholar, and Harvard WeCode Scholar. Kanwal is an ardent advocate for change, having founded FEMCodes to empower women in STEM fields.
