deeplizard is a deep learning focused education project that has quietly built a reputation as one of the more approachable ways to get started with the frameworks, and its defining quality is structure. Where a lot of free deep learning content lives as scattered playlists, deeplizard organises its material into actual courses with a sensible progression, grouped by level from beginner foundations through intermediate framework work in TensorFlow and PyTorch, into more advanced topics like reinforcement learning, generative adversarial networks and, more recently, stable diffusion and generative AI. Just as important, the videos are not the whole product, because each lesson is backed on the site by written notes, code and short quizzes, which turns passive watching into something closer to studying and helps the material actually stick. The teaching is deliberately beginner friendly, with clear pacing and a genuine effort to explain how the frameworks work rather than just pasting code, so for someone taking their first real steps into PyTorch or Keras it is a comfortable and confidence building place to start, and the fact that the core content is free on YouTube makes it easy to try.
The trade offs are the honest ones for a resource pitched at getting people started. The depth is moderate, so these courses will make you comfortable and productive with the tools and give you a working grasp of the ideas, but they will not take you to research level or to the frontier of any single topic, and the treatment of the underlying mathematics is lighter than what you would get from a heavyweight academic course. Because deep learning libraries change quickly, some of the older framework videos have drifted from the current versions and you occasionally have to make small adjustments to get code running, which is manageable but worth expecting. And the same gentle pacing that serves beginners so well can feel slow if you already know the basics and just want the advanced parts.
My take is that deeplizard is an underrated first stop for the practical side of deep learning, particularly if you like your learning organised and reinforced rather than freeform. Use it to get genuinely comfortable with a framework and to take a friendly first pass at areas like reinforcement learning and generative models that a lot of intro courses avoid, treat the site notes and quizzes as part of the course rather than optional extras, and once you have that solid working base, graduate to the deeper and more mathematically demanding courses when you want to push past comfortable competence into real depth.