Practical Deep Learning

Is this course completely free?

Yes, absolutely everything is free including the complete video course, the entire textbook available online, all Jupyter notebooks with code, and access to the supportive online community forums. You can use free cloud computing resources like Kaggle Notebooks and Paperspace Gradient for running the code, so you don’t need to purchase any hardware or software. The course materials, book, and all code are open source and freely available. This commitment to accessibility reflects Fast.ai’s mission to make deep learning education available to everyone regardless of their financial situation or geographic location.

What coding and math background do I need?

You need about one year of coding experience, preferably in Python, though experienced programmers in other languages can adapt. The course assumes you know basic programming concepts like loops, functions, and conditional statements. For mathematics, you only need high school-level math—the course teaches you the necessary calculus and linear algebra as you go along. You absolutely don’t need university-level mathematics, lots of data, or expensive computers. The course is specifically designed to make deep learning accessible to people without technical or mathematical backgrounds, proving that common barriers to entry are myths rather than requirements.

What makes this course different from other deep learning courses?

Fast.ai uses a unique top-down teaching approach: you start by building and deploying a complete working model in the very first lesson, then gradually learn how it works underneath. This contrasts with traditional bottom-up approaches that start with theory and mathematics before building anything practical. The course emphasizes getting real results quickly, with students often deploying their own deep learning applications by lesson two. Jeremy Howard has spent over 1000 hours testing and selecting the best tools, and the course reflects 30 years of machine learning experience including being the top-ranked Kaggle competitor globally. The teaching philosophy prioritizes intuitive understanding through examples and practical context over abstract symbol manipulation.

What will I actually build during this course?

By lesson two, you’ll have built and deployed your own deep learning model using data you collect yourself. Throughout the course, you’ll create models for image classification (like pet breed classifiers or dinosaur identifiers), natural language processing applications (sentiment analysis, document classification, patent similarity detection), tabular data analysis, movie recommendation systems, and more. You’ll learn to deploy these models as web applications that others can use. The course also covers building models from scratch, implementing stochastic gradient descent, creating random forests, and understanding cutting-edge techniques like transformers. Many students share impressive projects on the forums, showcasing real applications they’ve created.

Do I need powerful hardware or GPUs to take this course?

No, you don’t need any special hardware. The course strongly recommends using free cloud platforms like Kaggle Notebooks or Paperspace Gradient rather than your own computer, even if you have a GPU. These platforms provide free access to powerful GPUs and come pre-configured with all necessary software, avoiding the complexity of managing GPU drivers, CUDA, and Linux system administration. This democratizes access to deep learning—you can achieve state-of-the-art results using completely free resources accessible from any computer with internet access. The course shows you exactly how to set up and use these free platforms effectively.

What career outcomes have past students achieved?

Alumni have achieved remarkable success across diverse paths. Many have landed positions at top tech companies including Google Brain, OpenAI, Adobe, Amazon, and Tesla. Others have become multiple gold medal winners in prestigious international machine learning competitions on Kaggle, with some achieving top rankings from zero programming background in just a few years. Students have published research papers at leading conferences like NeurIPS and created successful startups using skills learned in the course. For example, Isaac Dimitrovsky won first place in the RA2-DREAM Challenge after completing the course, and Petro Cuenca added deep learning features to Camera+, which Apple then featured for its “machine learning magic.” The course has transformed careers across industries from healthcare to finance.

How is the course structured and how long does it take?

Part 1 consists of 9 lessons, each approximately 90 minutes long, totaling about 13-14 hours of video content. However, to truly master the material, you should spend additional time working through the accompanying Jupyter notebooks, experimenting with code, building your own projects, and participating in the forums. Most students spend 2-4 months on Part 1 at a pace of 5-10 hours per week, though the self-paced format allows flexibility. Part 2 is an advanced 30+ hour continuation covering deep learning foundations through Stable Diffusion, diving deeper into topics like transformers, diffusion models, and building frameworks from scratch. Each lesson corresponds to chapters from the free online book, creating an integrated learning experience combining video, interactive code, and written explanations.