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Fast.ai
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fast.ai is a non-profit research group focused on deep learning and artificial intelligence. It was founded in 2016 by Jeremy Howard and Rachel Thomas with the goal of democratizing deep learning.[1] They do this by providing a massive open online course (MOOC) named "Practical Deep Learning for Coders," which has no other prerequisites except for knowledge of the programming language Python.[2]
Key Information
Massive Open Online Course
[edit]The free MOOC "Practical Deep Learning for Coders" is available as recorded videos, initially taught by Howard and Thomas at the University of San Francisco. In contrast to other online learning platforms such as Coursera or Udemy, a certificate is not granted to those successfully finishing the course online. Only the students following the in-person classes can obtain a certificate from the University of San Francisco.[3]
The MOOC consists of two parts, each containing seven lessons. Topics include image classification, stochastic gradient descent, natural language processing (NLP), and various deep learning architectures such as convolutional neural networks (CNNs), recursive neural networks (RNNs) and generative adversarial networks (GANs).
Applications and alumni
[edit]- In 2018, students of fast.ai participated in the Stanford’s DAWNBench challenge alongside big tech companies such as Google and Intel. While Google could obtain an edge in some challenges due to its highly specialized TPU chips, the CIFAR-10 challenge was won by the fast.ai students, programming the fastest and cheapest algorithms.[4]
- As a fast.ai student, alumna Sara Hooker created software to detect illegal deforestation. She later became a founding member of Google AI in Accra, Ghana—the first AI research office in Africa.[5]
Software
[edit]In the fall of 2018, fast.ai released v1.0 of their free open-source library for deep learning called fastai (without a period), sitting atop PyTorch. Google Cloud was the first to announce its support.[6] This open-source framework is hosted on GitHub and is licensed under the Apache License, Version 2.0.[7][8]
References
[edit]- ^ "Launching fast.ai". 7 Oct 2016.
- ^ "Practical Deep Learning for Coders". KDnuggets. Dec 2016.
- ^ "Deep Learning Certificate". University of San Francisco. 8 June 2020.
- ^ "An AI speed test shows clever coders can still beat tech giants like Google and Intel". The Verge. 7 May 2018.
- ^ "New schemes teach the masses to build AIO". The Economist. 27 Oct 2018.
- ^ "Fast.ai's software could radically democratize AI". ZDnet. 2 Oct 2018.
- ^ "The fastai deep learning library". GitHub. Retrieved 8 June 2018.
- ^ "Open Machine Learning Frameworks". Archived from the original on 8 June 2020. Retrieved 8 June 2018.
External links
[edit]Fast.ai
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Overview
Founding and mission
fast.ai was founded in October 2016 by Jeremy Howard and Rachel Thomas as an initiative to broaden access to deep learning technologies. The organization emerged from Howard's experiences at Enlitic, where he recognized the potential of deep learning to revolutionize fields like medical diagnostics but was frustrated by the manual and inefficient processes required to build models, which limited its use to a small cadre of specialists. Thomas, a mathematician and data scientist, joined Howard to address this gap, aiming to empower domain experts—such as biologists, artists, and policymakers—without requiring advanced mathematical backgrounds.[11] The core mission of fast.ai centers on democratizing deep learning by countering the perceived elitism in AI education and development, making cutting-edge techniques accessible to non-experts through practical, hands-on approaches. As a non-profit research lab, fast.ai focuses on three interconnected pillars: education via free online resources, research into user-friendly AI methods, and the creation of open-source tools that simplify model training and deployment. This structure emphasizes inclusivity, supporting diverse programming languages, operating systems, and backgrounds to foster widespread adoption of AI.[4][12] In 2024, fast.ai announced its integration with Answer.AI, a move designed to amplify its educational reach while maintaining its commitment to accessibility. This partnership has introduced initiatives like "How To Solve It With Code," starting with a beta course launched in late 2024 that leverages AI-assisted coding to teach problem-solving skills, and continuing with subsequent courses, including one launched on November 3, 2025. In 2025, the initiative expanded with refinements to the Solveit platform and its applications to areas like software development and research, building directly on fast.ai's foundational goal of practical AI empowerment.[13][14][15]Key personnel
Jeremy Howard is a co-founder of fast.ai and plays a central role in leading its course development and research initiatives. With a background in machine learning, he co-founded Kaggle, serving as its president, and previously worked as a distinguished research scientist, applying deep learning to fields like medicine through his founding of Enlitic.[16][17] Rachel Thomas, the other co-founder of fast.ai, brings expertise in mathematics—holding a PhD from Duke University—and AI ethics, where she previously directed the Center for Applied Data Ethics at the University of San Francisco. She contributes significantly to curriculum design and outreach efforts, including recent 2025 writings on AI applications in medicine that explore both opportunities and ethical challenges.[18][3] Sylvain Gugger served as a key maintainer and lead developer of the fastai library, with substantial contributions to its software implementation, including co-authoring the foundational layered API design that enables accessible deep learning. His work on the library's core components, detailed in the 2020 arXiv paper, has been instrumental in fast.ai's practical approach to AI education and application.[19] Following fast.ai's integration with Answer.AI in late 2024, notable collaborators such as researchers from the Answer.AI team have supported ongoing projects, aligning with fast.ai's mission to advance open-source AI tools and education.[13]Educational Programs
Practical Deep Learning for Coders
The Practical Deep Learning for Coders is a flagship free massive open online course (MOOC) offered by fast.ai, designed to teach practical applications of deep learning to individuals with coding experience but no prior machine learning knowledge.[5] Originally launched in late 2016 as a seven-week program, it has evolved through multiple iterations, with a complete rewrite and update released in 2022 that maintains its core structure while incorporating contemporary tools and techniques.[20][21][22] The course consists of a two-part series, with Part 1 comprising nine lessons, each approximately 90 minutes long, focusing on building and deploying models for computer vision, natural language processing (NLP), tabular data analysis, and collaborative filtering.[5][21] Prerequisites for the course are minimal, requiring only basic Python programming skills—equivalent to about one year of experience—and high school-level mathematics, such as algebra and basic statistics, with no advanced calculus or prior exposure to machine learning concepts necessary.[5][21] This accessibility aims to democratize deep learning education, enabling participants from diverse backgrounds to achieve practical results quickly. The course format emphasizes hands-on, interactive learning through captioned video lectures, accompanying Jupyter notebooks for code execution, and integrated quizzes to reinforce concepts.[5] It adopts a top-down pedagogical approach, where learners first construct working models to solve real-world problems—such as classifying images—and then explore the underlying theory, including optimization techniques like stochastic gradient descent (SGD), to build intuition without overwhelming mathematical prerequisites.[5][21] Key topics in Part 1 include image classification using transfer learning from pre-trained models, processing tabular and text data for predictive tasks, and model deployment via user-friendly interfaces like Gradio and Hugging Face spaces, allowing students to share interactive applications by the second lesson.[5][21] Examples in the notebooks leverage the fastai library to streamline implementation.[5] Community integration is a core element, with dedicated forums at forums.fast.ai where learners share projects, such as custom image classifiers or NLP tools, seek support from peers and instructors, and collaborate on extensions of course exercises.[5][23] This fosters a collaborative environment that extends beyond the structured lessons, encouraging real-world application and ongoing engagement.[21]Advanced and specialized courses
Following the foundational "Practical Deep Learning for Coders" course, fast.ai offers Part 2: "Deep Learning Foundations to Stable Diffusion," a comprehensive advanced program exceeding 30 hours of video content that delves into the theoretical underpinnings and implementation of sophisticated deep learning architectures.[5][24] This course assumes completion of Part 1 and covers convolutional neural networks (CNNs), transformers, and diffusion models, culminating in the from-scratch implementation of the Stable Diffusion algorithm for image generation.[25] Participants engage with Jupyter notebooks to build and experiment with these models, emphasizing conceptual mastery over rote application.[24] In addition to Part 2, fast.ai has introduced "How To Solve It With Code" through a partnership with Answer.AI, announced in late 2024 and made fully available in October 2025.[2][26] This resource shifts focus to iterative problem-solving in AI development, teaching learners to collaborate with AI tools for tasks like writing code, building web applications, and debugging, using small, incremental steps rather than generating large codebases at once.[2] It promotes practical skills in integrating AI into coding workflows without requiring prior deep learning expertise beyond basic programming.[26] Fast.ai's advanced offerings also incorporate specialized topics such as ethics in AI and from-scratch model construction, with 2025 updates expanding on generative AI techniques and model interpretability.[5][27] Ethics discussions, led by co-founder Rachel Thomas, explore biases, privacy, and societal impacts of AI systems, drawing from the Center for Applied Data Ethics.[27] From-scratch building reinforces understanding by implementing core components without high-level libraries, while recent additions emphasize generative models' creative applications and techniques for explaining AI decisions, such as attention visualization.[25] All advanced courses maintain fast.ai's signature format of video lectures paired with interactive notebooks, fostering project-based learning where students apply concepts to real-world problems like custom generative tools or ethical AI audits.[5] No formal certifications are provided, prioritizing hands-on expertise and community-driven projects over credentials.[24] These resources build directly on the practical basics from the introductory course, enabling deeper exploration for those with coding experience.[5]Software and Tools
fastai library
The fastai library is an open-source deep learning framework designed to enable practitioners to achieve state-of-the-art results with minimal code, built atop PyTorch to leverage its dynamic computation graph for flexibility in model development.[28][7] Version 1.0 was released on October 2, 2018, marking a significant update that introduced a unified interface for common deep learning tasks while incorporating modern best practices such as progressive resizing and discriminative learning rates.[29] The library is licensed under the Apache 2.0 open-source license, allowing broad commercial and non-commercial use, and is hosted on GitHub, where the main repository has garnered over 25,000 stars as of 2023 with ongoing updates and community contributions.[6][7] At its core, fastai provides a high-level API that abstracts complex PyTorch operations into intuitive components tailored for specific data modalities, including computer vision, natural language processing, tabular data analysis, and collaborative filtering.[28][7] Key features include built-in support for advanced data augmentation techniques, such as randomized transformations to improve model generalization in vision tasks, and seamless transfer learning, which allows fine-tuning pre-trained models like ResNet on custom datasets with just a few lines of code.[30][29] For collaborative filtering, the library offers tools to build recommender systems using matrix factorization and embedding layers, enabling rapid prototyping of personalized prediction models.[31] Additionally, fastai is optimized for GPU acceleration through PyTorch, making it compatible with cloud environments like Google Cloud Platform for scalable training on high-performance hardware.[29] In practice, the library excels at simplifying workflows to produce competitive results efficiently; for instance, training a vision classifier to state-of-the-art accuracy can often be accomplished in a single line using theLearner class, which handles optimization, callbacks, and evaluation automatically.[28][7] This approach remains highly relevant in 2025 as an accessible entry point in machine learning toolboxes, particularly for beginners seeking to experiment with production-ready models without deep expertise in low-level tensor operations.[32] The current version, 2.8.5 (as of October 2025), includes enhancements such as improved support for transformers and large language models.[33][34] The library's development was led by Sylvain Gugger, a key contributor who co-authored the foundational implementations and the accompanying O'Reilly book Deep Learning for Coders with Fastai and PyTorch.[9][35]
fasttransform and extensions
fasttransform is a Python library released on February 20, 2025, designed to create reversible data transformation pipelines that leverage multiple dispatch for enhanced extensibility.[36] It allows developers to define transformations using pairedencodes and decodes methods, enabling automatic reversal of data processing steps without manual inverse function implementation.[36] This approach utilizes the plum library for multiple dispatch, permitting transforms to adapt dynamically based on input data types within a single pipeline.[36] As an open-source project under the fast.ai ecosystem, it is hosted on GitHub and aims to streamline complex data workflows in machine learning applications.[37]
Key features of fasttransform include tools for model interpretability, such as show_batch for visualizing transformed data and plot_top_losses for analyzing prediction errors, which facilitate deeper insights into model behavior.[36] The library supports one-way-to-reversible transformations, exemplified by classes like Normalize that apply standardization during encoding and restore original scales during decoding.[36] These capabilities simplify debugging and experimentation in deep learning by allowing seamless inspection of intermediate data states via methods like .decode(), reducing the overhead of tracking pipeline effects.[36] By complementing the high-level API of the fastai library, fasttransform enables more modular and maintainable code for data preprocessing.[36]
Extensions to fasttransform include integrations that support deployment in broader AI ecosystems, such as compatibility with Hugging Face Spaces for hosting fastai-based models, where fasttransform handles transformation dependencies during model loading.[38] Additional ecosystem tools, like fastxtend, build on fasttransform's foundations by providing fused optimizers and progressive resizing callbacks to accelerate training pipelines.[39] In 2025, updates within the fast.ai suite have incorporated fasttransform into workflows for generative AI, enhancing pipeline transparency for tasks involving diffusion models and text generation.[36] Overall, these extensions target developers seeking transparent and reversible data flows, promoting efficient iteration in production-grade AI systems.[36]