Deep Learning A-Z 2024: Neural Networks, AI & ChatGPT

Deep Learning A-Z (currently listed on Udemy as 'Deep Learning A-Z 2026: Neural Networks, AI & ChatGPT Prize') is a breadth-first, intuition-led video course by Kirill Eremenko and Hadelin de Ponteves of SuperDataScience that is best understood as a guided tour of six neural-network families rather than a rigorous deep-dive. Across roughly 22.5 hours of video plus 38 articles and downloadable resources, it walks you through two volumes: Supervised Deep Learning (Artificial Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks) and Unsupervised Deep Learning (Self-Organizing Maps, Boltzmann Machines, AutoEncoders), each section pairing a no-math 'intuition' lecture with hands-on coding in Python using Keras/TensorFlow and PyTorch. The consistent verdict across Class Central (4.7 from 388 ratings), the Udemy on-platform rating (~4.5 across tens of thousands of reviews, with Udemy reporting 54% five-star), and OpenCourser/Reddit commentary is the same: students praise the breadth and the gentle on-ramp to exotic models like SOMs and Boltzmann Machines, while the recurring criticism is shallow mathematical depth and case studies that feel more illustrative than production-grade. It is a strong confidence-builder for someone who already knows basic Python and wants a panoramic, low-friction introduction, but it is not the course to make you a job-ready deep-learning engineer or to teach you the underlying math.

Take it if you want a broad, beginner-friendly survey of many neural-network architectures and value intuition over rigor; skip it if you need mathematical depth, production-grade engineering, or coverage of modern transformer/LLM architectures, since the hands-on work centers on classic ANN/CNN/RNN/SOM/Boltzmann/AutoEncoder examples rather than today's state-of-the-art.

Best for: Learners with basic Python and high-school-level math who want a panoramic, low-pressure introduction to the deep-learning landscape and a quick, confidence-building way to see many architectures (ANN, CNN, RNN, SOM, Boltzmann Machines, AutoEncoders) implemented end-to-end. It suits career-switchers, analysts, and students who learn best from 'intuition first, then code-along' teaching and who appreciate the SuperDataScience presentation style.

Skip if: People who want mathematical rigor (backpropagation derivations, optimization theory), aspiring research-track engineers, or anyone seeking up-to-date coverage of transformers, attention, and large language models. The 'ChatGPT/LLM Prize' in the title is a competition incentive, not a curriculum on building LLMs. Complete beginners with no Python and those who dislike heavy hand-holding or want to write architectures from scratch without guided templates should also look elsewhere.