Introduction to Deep Learning

MIT 6.S191 Introduction to Deep Learning is, for the price (free), one of the best starting points for deep learning on the internet, and the recommendation here is to take it as a fast, modern overview rather than a rigorous from-scratch course. It is MIT's official intensive built around weekly video lectures from Alexander Amini and Ava Amini plus three open-source Colab software labs (music generation, facial detection, and fine-tuning an LLM), all released yearly under an MIT license. The 2026 edition runs roughly nine lectures from late March to late May and covers neural network fundamentals, sequence models, computer vision, generative modeling (VAEs, GANs, diffusion), reinforcement learning, and current frontiers including LLMs and AI for science. The biggest honest caveats, echoed by a first-hand MIT student review, are the breakneck pacing (multiple major architectures compressed into a single lecture), thin mathematical depth, and lab friction such as Google Colab free-GPU limits and occasionally unreliable sponsor tooling. It is an excellent conceptual primer and lab sampler, but it is not a substitute for a deep, math-heavy course like Stanford CS229 or the multi-month Deep Learning Specialization.

Free, high-production MIT lectures and hands-on Colab labs make it a top-tier conceptual primer, but the compressed pacing, shallow math, and lack of a real certificate mean it suits curious learners and refreshers far better than anyone needing rigorous foundations or a credential.

Best for: Programmers, students, and working engineers who want a fast, current, MIT-quality overview of how deep learning works (CNNs, sequence models, VAEs/GANs/diffusion, RL, LLMs) and want to run real Jupyter/Colab labs without paying anything. Ideal as a survey before a deeper course, or as a refresher to see the latest applications like LLM fine-tuning and AI for science.

Skip if: Complete coding beginners (the labs assume Python), anyone who needs deep mathematical derivations or a slow, build-it-from-scratch pace, and learners who require a verifiable certificate or accredited credential, since the open online version awards none. Those wanting structured graded assignments with grading and support should choose Coursera-style offerings instead.