--- title: AI Fundamentals description: This topic introduces the core concepts, history, and branches of artificial intelligence, including search, logic, and probabilistic reasoning. Learners will understand the foundational theories and architectures that drive modern intelligent systems and automated decision-making. category: programming-tech subcategory: artificial-intelligence difficulty: beginner, intermediate, advanced url: /subject/ai-fundamentals --- # AI Fundamentals This topic introduces the core concepts, history, and branches of artificial intelligence, including search, logic, and probabilistic reasoning. Learners will understand the foundational theories and architectures that drive modern intelligent systems and automated decision-making. ## Available Resources 3 Videos • 2 Books • 6 Courses • 2 Websites ## Books ### 1. AI Superpowers **Author:** Kai-Fu Lee A rigorous geopolitical and economic analysis of the race for AI dominance and its structural impact on global labor markets. **Difficulty:** Intermediate | **Price:** Paid **Link:** https://www.amazon.com/s?k=132854639X&tag=edmonddante07-20 **Tags:** artificial-intelligence, china, us-china-relations, technology-policy, economics ### 2. Artificial Intelligence: A Modern Approach **Author:** Stuart Russell, Peter Norvig Standard university textbook presenting AI through the intelligent-agent framework: search, logic and knowledge representation, probabilistic reasoning, machine learning, perception, and robotics. Working through it gives you the vocabulary, algorithms, and formal foundations assumed by most graduate AI coursework and research papers. **Difficulty:** Intermediate | **Language:** English | **Price:** Paid **Link:** https://www.amazon.com/dp/0131038052?tag=edmonddante07-20 **Tags:** books, technology-computer-science, ai-ml ## Courses ### 1. Artificial Intelligence (MIT 6.034) **Author:** Patrick Henry Winston Covers knowledge representation, search, constraint satisfaction, rule-based systems, neural nets, support vector machines and boosting. Includes 30 lecture videos, problem-solving videos, programming assignments and exams. Afterwards you can build simple intelligent systems and reason about how machines solve problems and learn. **Difficulty:** Intermediate | **Price:** Free **Link:** https://ocw.mit.edu/courses/6-034-artificial-intelligence-fall-2010/ **Tags:** symbolic-ai, search-algorithms, knowledge-representation, constraint-satisfaction, machine-learning ### 2. Neural Networks and Deep Learning In the first course of the Deep Learning Specialization, you will study the foundational concept of neural networks and deep learning. By the end, you will be familiar with the significant technological trends driving the rise of deep learning; build, train, and apply fully connected deep neural networks; implement efficient (vectorized) neural networks; identify key parameters in a neural network’s architecture; and apply deep learning to your own applications. The Deep Learning Specialization is our foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology. It provides a pathway for you to gain the knowledge and skills to apply machine learning to your work, level up your technical career, and take the definitive step in the world of AI. **Difficulty:** Beginner | **Price:** Free **Link:** https://www.coursera.org/learn/neural-networks-deep-learning **Tags:** deep-learning, neural-networks, backpropagation, gradient-descent ### 3. Machine Learning Course - CS 156 Master theoretical machine learning with Yaser Abu-Mostafa's renowned "Learning From Data" course. Explore fundamental concepts and algorithms. **Difficulty:** Advanced | **Price:** Free **Link:** https://www.youtube.com/playlist?list=PLD63A284B7615313A **Tags:** machine-learning, statistical-learning-theory, generalization, caltech ### 4. Practical Deep Learning for Coders fast.ai's free course teaching deep learning top-down: you train working image, text, and tabular models in the first lessons, then work back to the underlying mechanics. Assumes about a year of coding experience, uses PyTorch and the fastai library. **Difficulty:** Beginner | **Price:** Free **Link:** https://course.fast.ai/ **Tags:** deep-learning, pytorch, fastai, computer-vision, top-down-teaching ### 5. Machine Learning In the first course of the Machine Learning Specialization, you will: • Build machine learning models in Python using popular machine learning libraries NumPy and scikit-learn. • Build and train supervised machine learning models for prediction and binary classification tasks, including linear regression and logistic regression The Machine Learning Specialization is a foundational online program created in collaboration between DeepLearning.AI and Stanford Online. In this beginner-friendly program, you will learn the fundamentals of machine learning and how to use these techniques to build real-world AI applications. This Specialization is taught by Andrew Ng, an AI visionary who has led critical research at Stanford University and groundbreaking work at Google Brain, Baidu, and Landing.AI to advance the AI field. This 3-course Specialization is an updated and expanded version of Andrew’s pioneering Machine Learning course, rated 4.9 out of 5 and taken by over 4.8 million learners since it launched in 2012. It provides a broad introduction to modern machine learning, including supervised learning (multiple linear regression, logistic regression, neural networks, and decision trees), unsupervised learning (clustering, dimensionality reduction, recommender systems), and some of the best practices used in Silicon Valley for artificial intelligence and machine learning innovation (evaluating and tuning models, taking a data-centric approach to improving performance, and more.) By the end of this Specialization, you will have mastered key concepts and gained the practical know-how to quickly and powerfully apply machine learning to challenging real-world problems. If you’re looking to break into AI or build a career in machine learning, the new Machine Learning Specialization is the best place to start. **Difficulty:** Beginner | **Price:** Free **Link:** https://www.coursera.org/learn/machine-learning **Tags:** supervised-learning, linear-regression, logistic-regression, python, scikit-learn ### 6. AI for Everyone AI is not only for engineers. If you want your organization to become better at using AI, this is the course to tell everyone--especially your non-technical colleagues--to take. In this course, you will learn: - The meaning behind common AI terminology, including neural networks, machine learning, deep learning, and data science - What AI realistically can--and cannot--do - How to spot opportunities to apply AI to problems in your own organization - What it feels like to build machine learning and data science projects - How to work with an AI team and build an AI strategy in your company - How to navigate ethical and societal discussions surrounding AI Though this course is largely non-technical, engineers can also take this course to learn the business aspects of AI. **Difficulty:** Beginner | **Language:** English | **Duration:** 4 weeks of study, 2-3 hours/week | **Price:** Free **Link:** https://www.coursera.org/learn/ai-for-everyone **Tags:** courses, technology-computer-science, ai-ml ## Websites ### 1. Neural Network Zoo **Author:** Fjodor van Veen A single chart maps roughly thirty neural network architectures, from perceptrons and CNNs to LSTMs, autoencoders, GANs, Hopfield nets and Boltzmann machines, each with a short paragraph and the original paper citation. **Difficulty:** Beginner | **Price:** Free **Link:** https://www.asimovinstitute.org/neural-network-zoo/ **Tags:** neural-networks, deep-learning, architectures, cnn, rnn ### 2. Distill.pub Peer-reviewed web journal of machine learning explanations, publishing interactive articles on topics like feature visualisation, attention and neural network interpretability. Archive remains readable, though the journal went on indefinite hiatus in 2021 and no longer publishes. **Difficulty:** Intermediate | **Price:** Free **Link:** https://distill.pub/ **Tags:** deep-learning, neural-networks, interpretability, interactive-visualization, machine-learning ## Videos ### 1. Vectors | Chapter 1, Essence of Linear Algebra **Author:** Grant Sanderson Opening chapter of Grant Sanderson's animated linear algebra series, contrasting the physics, computer science and mathematics views of vectors. Shows vectors as arrows and coordinate lists, and how addition and scalar multiplication work geometrically, laying intuition for linear combinations and transformations. **Difficulty:** Beginner | **Price:** Free **Link:** https://www.youtube.com/watch?v=fNk_zzaMoSs **Tags:** linear-algebra, vectors, vector-addition, visual-intuition ### 2. StatQuest with Josh Starmer - Neural Networks videos Josh Starmer's StatQuest provides clear, concise, and often humorous explanations of statistical and machine learning concepts. His videos on neural networks break down complex ideas into easily digestible 'quests.' **Difficulty:** Beginner | **Language:** English | **Price:** Free **Link:** https://www.youtube.com/playlist?list=PLXe8IwAUB2B-Ews9ADFJBHE7nVAZAfV05 **Tags:** neural-networks, backpropagation, machine-learning, statistics, video-series ### 3. 3Blue1Brown - Neural Networks series Grant Sanderson's 3Blue1Brown channel offers visually intuitive and mathematically rigorous explanations of complex topics. His series on neural networks is particularly praised for making the core concepts, like backpropagation, understandable through animated visuals. **Difficulty:** Beginner | **Language:** English | **Price:** Free **Link:** https://www.youtube.com/playlist?list=PLW_xnxm7jEso **Tags:** neural-networks, backpropagation, gradient-descent, deep-learning, visual-explanation --- *This content is part of Dantes.io - Your Treasure Map to Knowledge* *Curated by humans at Dantes.io. Personal study use welcome; republishing this curation requires permission (team@dantes.io).* View this page online: https://dantes.io/subject/ai-fundamentals