Photo by editor
# Introduction
When I first started learning AI, I spent a lot of time copying code from tutorials, but I realized I didn’t really understand how it worked. Real skills aren’t just running models. It knows why they work and how to apply them to real problems. AIBooks helped me learn the concepts, reasoning, and practical side of AI in a way that no quick tutorial could. With that in mind, we’re starting to recommend this series Free but really valuable books. This article is for everyone who wants to learn AI, and here is 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 starts with simple ideas like perceptrons and sigmoid neurons, then walks you through building a network that can recognize handwritten digits. You also get to see how backpropagation actually works to train these models, and how to optimize them with things like cost functions, regularization, weight initialization, and tuning hyperparameters. There are plenty of Python code examples so you can test things out yourself and see how everything connects. It mixes both intuition and mathematics nicely, so you just start to understand no how Neural networks work, but Why?. If you already know a little math (like linear algebra or calculus), it’s a good choice to just jump ahead using the library and actually know what’s going on under the hood.
// Outline Overview:
- Fundamentals of Neural Networks .
- Backpropagation and learning .
- Advanced training techniques .
- Deep Learning and Convolutional Networks .
# 2. Deep learning
Deep learning Gives a really great overview of deep learning and how machines learn from experience, developing ideas from simple to complex. It starts with the math you’ll need, such as linear algebra, probability, information theory, and a bit of numerical calculus, then goes through the basics of machine learning. Then, it goes deeper into modern deep learning methods such as feedforward, convolutional and recurrent networks, regularization and optimization, showing how they are used in real projects. It also discusses some advanced topics such as autoencoders, generative and representation learning, and structured probabilistic models. It’s mostly intended for people with a solid math background, so it’s more like a reference for research or advanced work than a beginner’s guide.
// Outline Overview:
- Factor models and autonomers .
- Representation learning and probabilistic models .
- Deep generative models and advanced techniques .
# 3. Applied deep learning
link:
Free course Applied deep learning Designed 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. The course includes advanced tools such as Python, Pytorchand Fastai library, and shows you how to handle everything from data cleaning to model training, testing, and deployment. You’ll work with real notebooks, datasets, and problems so you learn by doing. The focus is on practical, state-of-the-art methods for choosing the right algorithm, properly validating it, scaling it, and deploying it.
// Outline Overview:
- Foundations and model training .
- Requests across domains .
- Modern techniques and optimization .
- Deployment and practical skills .
# 4. Artificial Intelligence: Foundations of Computational Agents
The book Artificial Intelligence: Foundations of Computational Agents Defines AI through the theory of “computational agents,” systems that can perceive, learn, reason, and act. The latest edition includes new topics such as neural networks, deep learning, causality, and social and ethical aspects of AI. It shows how agents are constructed, how they plan and execute, and how they handle complex or uncertain situations. Algorithms are included in each chapter The pythoncase studies, and real-world discussions, so you learn how and why. It is a balanced mix of theory and practice, for students or anyone who wants an advanced and in-depth introduction to AI.
// Outline Overview:
- Foundations of AI and Agents .
- Agent architecture and control .
- Reasoning, planning and searching .
- Learning and Neural Networks .
- Uncertainty, causality and reinforcement learning .
# 5. Ethical Artificial Intelligence
Paper Ethical artificial intelligence It looks at how future AI systems might behave in ways we don’t expect or might be harmful, and it suggests ways to design them safely. It begins by pointing out that AI can learn models of the world far more complex than humans can fully understand, making security measures difficult. The authors recommend using utility functions (what AI should care about) rather than vague rules, as they clarify goals. It also covers issues such as self-degradation, where an AI can corrupt its own observations or rewards, unintentional “shortcut” actions that hurt us, and reward generator corruption, where an AI manipulates its own reward system. The authors propose models that learn human values, use limited definitions, and incorporate self-modeling so that AI can reason about its actions. It also considers the bigger picture, such as how AI can affect society, politics and the future of humanity.
// Outline Overview:
- Foundations and AI Design .
- AI behavior and challenges .
- Evaluation, Governance and Society .
- Philosophical and social implications (Meaning, Social and Cultural Implications, Bridging Computation and Struggle for Human Values)
# wrap 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 coding, agent-based AI, and ethical issues. They provide a clear path from learning ideas to applying AI in real-world situations. What topics would you like to cover next? Leave your suggestions in the comments!
Kanwal Mehreen is a machine learning engineer and technical writer with a deep passion for data science and the intersection of AI with medicine. He co-authored the eBook “Maximizing Productivity with ChatGPT.” As a 2022 Google Generation Scholar for APAC, she champions diversity and academic excellence. He has also been recognized as a Teradata Diversity in Tech Scholar, a MITACS GlobalLink Research Scholar, and a Harvard Wicked Scholar. Kanwal is a passionate advocate for change, having founded the Fame Code to empower women in stem fields.