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The obvious Getting started
When I first started learning ai, I spent a lot of time copying code from tutorials, but I realized that I didn't really understand how it worked. Real talent isn't just about racing models. Knowing why they work and how to apply them to real problems. AI books helped me learn the concepts, rationale, and practical side of AI in a way that no quick tutorial could. With this in mind, we begin this series of recommendations Free but really valuable books. This article is for all those who want to learn AI, and here is the first set of recommendations.
The obvious 1. Neural networks and deep learning
A book Neural networks and deep learning It takes you from the basics of neural networks to building and training deep models on your own. It starts with simple ideas like Perceptrons and Sigmoid Neurons, then moves on to making a network that can recognize handwritten digits. And you get to see how backpropagation works to train these types, and how you can improve them with things like cost functions, passive functions, weight activation, and tune hyperparameters. There are plenty of Python code examples so you can check things out for yourself and see how it all connects. It mixes both intuition and math well, so you start to get confused How Neural networks work, though by whom. If you already know a bit of mathematics (like linear algebra or Calculator), this is a good candidate to go through the use of the library and know what's going on under the hood.
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- Fundamentals of neural networks (First, sigmoid neurons, network structures, segmenting handwritten digits, gradient descent, using networks)
- Backpropagation and learning .
- Advanced training techniques (Cross-entropy input cost, extremes, weight initialization, weight establishment, hyperparameter selection, approximate neural nets, extensions of sigmoid Neneurons)
- Deep learning and transparent networks (Printing the gradient problem, unstable gradients, neural networks, functional implementation, recent progress in image recognition, future directions)
The obvious 2. Deep learning
Deep learning It gives a really good overview of deep learning and how machines learn through experience, building complex ideas from simple ones. It starts with the math part you will need, such as linear algebra, probability, information theory, and numerical integration, then goes through the basics of machine learning. After that, it goes deeper into modern methods of deep learning such as feed, transparent networks and general networks, normalization, and optimization, showing how they are used in real projects. It also covers some advanced topics such as autoencoders, learning generation and representation, and structured models. It is intended for most people with a strong background in mathematics, so it is more like a suitable reference for research or advanced work than a beginner's guide.
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- Factor models and autoencoders (PCA, ICA, Sparse Coders, Offecsete & Standard AutoECoders, Decousing, Manifold Learning)
- Learning Learning with probabilistic models (As if the intelligent layer, learning to learn, distributed representations, probabilistic models, approximate methods, approximate methods, Monte Carlo methods)
- Deep generative models and advanced techniques (boltzmann machines, deep belief networks, convolutional models, generalized stochastic networks, sampling autocoder, evaluating generative models)
The obvious 3. Deep learning
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Free course Deep learning it's designed for people who just know how to code and want to get hands on with machine learning and deep learning. Instead of just learning an idea, you'll start building models into real jobs right away. The course covers modern tools such as Python, Pytorchno Fatai Library, and shows you how to manage everything from data cleaning to Model Training, testing, and deployment. You will work with real notebooks, datasets and problems to learn by doing. The focus is on practical, state-of-the-art scenarios for selecting an appropriate algorithm, validating it, deriving it, and implementing it.
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- Exemplary foundations and training (Neaural Network Basics, Stochastic Gradient Fornt, embedding and randomization functions, backpropagation, mlps, autoencoders)
- Applications around the Domain (Computer vision with CNNs, natural language processing (NLP) including embedding and expression matching, tabular data modeling, active filtering)
- Advanced techniques and optimization (Transfer learning, weight decay, data augmentation, stochastic gradient fear (SGD), revpms, mixed precision, DDPM / DDIM, attention, dynamic flexibility, high flexibility)
- Submission and practical skills .
The obvious 4. Artificial Intelligence: Fundamentals of computational agents
A book Artificial Intelligence: Fundamentals of computational agents It defines AI in the sense of “computational agents,” systems that can understand, learn, and act. The latest edition adds new topics such as neural networks, deep learning, perturbation, and the social and behavioral aspects of AI. It shows how agents are created, how they plan and act, and how they handle complex or uncertain situations. Each chapter includes algorithms PythonCase studies, and real world interviews, so you learn both why. It's a balanced mix of theory and practice, great for students or anyone looking for a modern and in-depth intro to Ayi.
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- Basics of AI and agents .
- Agent & control structures (Hierarchical control, agent tasks, offline, online, and how agents see and act in environments.)
- Consulting, Planning and Searching (problem solving with search, graph satisfaction, stress satisfaction, predictive logic, and programming methods including forward, backward, and partial order programming)
- Learning & Newaural Networks (Supervised learning, decision trees, regression, hierarchical models, ensemble models such as augmentation, deep structural networks (CNNS), transformers), and large language models.)
- Uncertainty, lowering and strengthening reinforcement (Spurious Reasoning, Bayesian Methods, Random Methods, Decision Recognition, Decision Making Under Uncertainty, Exploratory Decisions, and Reinforcement Evolutionary Techniques LIKE
The obvious 5
Paper Moral intelligence He looks at how future systems may behave in unexpected or potentially dangerous ways, and suggests ways to design them safely. It starts by pointing out that AI can learn models of the world that are far more complex than humans can fully understand, making the defense illusory. The authors recommend using implementation functions (mathematical descriptions of what the AI should take care of) rather than vague rules, because they make clear goals. It also includes problems such as self-deception, where the AI could spoil what it saw or the rewards, not intended for “actions that harm us, when the Ai shows its reward system, when the Ai shows its reward system, when the Ai shows its reward system, when the AI shows its reward system, when the Ai shows its reward system. The authors propose models that learn human values, use comprehensive explanations, and include preparation to ai can discuss its actions. It also looks at the big picture, how AI can influence the future of politics, and the future of humanity.
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- Fundamentals & AI Design (Future AI vs current AI, teaching AI, State-enhancing agents, natural learning models, intelligence methods, behavioral frameworks)
- AI Outial & Challenges .
- Evaluation, management and society (AI testing, Real-world behavior, political dimensions, transparency, benefit sharing, ethical considerations)
- Philosophical & Societal Impact (The search for meaning, social and cultural meanings, binding complings and human values)
The obvious Wrapping up
These books (and paper, and course) cover a wide range of what an AI engineer needs, from neural networks and deep learning to AI-AI AI, and ethical issues. They provide a clear way to learn the concepts of using 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 technical writer with a strong interest in data science and the intersection of AI and medicine. Authored the eBook “Increasing Productivity with Chatgpt”. As a Google Event 2022 APAC host, she is a symbol of diversity and excellence in education. He has also been recognized as a teradata distinction in tech scholar, a mitacs Globalk research scholar, and a Harvard WeCode Scholar. Kanwal is a passionate advocate for change, who has created femcodes to empower women.
