Embarking on the journey of AI development is an exciting endeavor, whether you're a beginner or looking to enhance your existing skills.
This diagram visually represents how deep learning is a subset of machine learning, which in turn, is a subset of artificial intelligence. Our guide includes resources that cover each of these areas.
Books for Comprehensive Understanding
- Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville: This book is an in-depth exploration intended to help students and practitioners enter the field of machine learning in general and deep learning in particular.
- Python Machine Learning by Sebastian Raschka and Vahid Mirjalili: A practical guide that offers clear explanations and Python examples for machine learning.
- Neural Networks from Scratch by Harrison Kinsley (Sentdex): Guide for understanding neural networks. Each chapter accompanied with animated examples
Online Courses for Practical Skills
- Machine Learning by Andrew Ng (Coursera): A popular course by Andrew Ng, providing a comprehensive introduction to machine learning and data mining.
- Fast.ai: A hands-on approach to learning deep learning and machine learning, known for its practical sessions and easy-to-understand methodology.
- Neural Networks: Zero to Hero: A course that starts from the basics and progresses to advanced neural network concepts, taught by a renowned expert in the field Andrej Karpathy .
- The DTU MLops course repository: An extensive course focusing on Machine Learning Operations (MLOps), offering insights into the practical aspects of deploying and managing AI systems.
Essential Websites and Portals
- arXiv.org: Source for the latest research papers in AI, offering a wealth of academic insights and developments.
- Pytorch's Official Website: The official site for PyTorch, providing resources, documentation, and a community forum.
- Tensorflow's Neural Network Playground: An interactive visualization of neural networks, great for understanding the basics of how these models learn and function.
Youtube Channels
- Statquest with Josh Starmer: Simplifying statistics and data science concepts through nursery "children book"-style visuals and nursery rhymes.
- Hugo Larochelle: Featuring a range of tutorials and lectures on machine learning and deep learning, presented by a leading AI researcher at Google's DeepMind.
- 3Blue1Brown: Offers visually engaging videos for understanding key mathematical concepts. Contains a particularly useful playlist for understanding deep neural networks
- Sentdex: Provides practical programming tutorials with a focus on Python and its applications in machine learning and data analysis.
- Two Minute Papers: Breaks down complex AI research papers into bite-sized two-minute videos, making cutting-edge research accessible to all.
- Arxiv Insights: Anotother channel that delves into research papers, but explaining them in a more thorough manner whilst still being an approachable light watch.
Insightful Podcasts
For those who prefer audio learning or want to delve into discussions on AI while on the go, these podcasts are invaluable resources:
- MLG: Machine Learning Guide: Machine learning audio course, teaching the fundamentals of machine learning and artificial intelligence. Covers models, math, languages and frameworks
- Latent Space: The AI Engineer Podcast: A must-listen for AI Engineers, this podcast discusses cutting-edge topics in Software 3.0, with news and interviews from industry leaders and innovators.
- Data Science Central: Hosted by industry experts, this podcast covers a wide range of topics in data science and AI, making complex subjects accessible to a broader audience.
- TWIML AI Podcast: Focused on bringing machine learning and AI to life, featuring interviews with top researchers and practitioners in the field.
Additional resources for Math, Statistics, and Programming
If you're new to these foundational subjects, the following resources will help you build the necessary background to dive into AI:
- Khan Academy: Probability and Statistics: An interactive platform offering beginner-friendly lessons on probability and statistics, essential for understanding data in AI.
- The Matrix Cookbook: A comprehensive resource for linear algebra, providing useful formulas and concepts used in machine learning and AI.
- Math Is Fun: Understanding Eigenvalues and Eigenvectors: A simplified explanation of eigenvalues and eigenvectors, key concepts in understanding algorithms in machine learning.
The field of AI is constantly evolving, so staying updated and continually learning is key.