wtf is deep-learning-101?

This repository is a structured set of educational materials for learning deep learning, originally built for a six-week course. It serves as a roadmap for beginners who want to understand what deep learning is and how it is used in fields like computer vision, robotics, and natural language processing. The content is organized into a weekly curriculum that gradually introduces core concepts and well-known AI models. It starts with the basics of deep learning and introductory tools, then moves into the algorithms behind famous systems like AlphaGo. Over the weeks, the topics shift to practical applications such as teaching computers to recognize objects in images, generate text descriptions for pictures, and answer visual questions. Someone who would use this is a student or beginner looking for a guided path through the deep learning landscape rather than a single textbook. For example, if you want to learn how self-driving cars identify objects or how image generation works, this curriculum points you toward the specific models that make those things possible, like YOLO for object detection and Generative Adversarial Networks for image creation. The curriculum includes external slides and links to specific contributors' work, serving as a curated collection rather than a from-scratch course. It covers a wide range of topics, from basic optimization methods to advanced concepts like adversarial attacks and neural style transfer, giving learners a broad survey of the field's major milestones.