Deep Learning from the Foundations – fast.ai

Summary

fast.ai has launched a free 15-hour course that takes students from manually implementing matrix multiplication and back-propagation steps to training ImageNet-class models. The course re-codes significant portions of the fastai and PyTorch libraries from scratch. The final two lessons, covering Swift for TensorFlow, are co-led by Chris Lattner.

Why it matters

In a field where practitioners often view frameworks as black boxes, this code-centric curriculum reconstructs internal structures, addressing topics such as initialization schemes, training loops, callbacks, and mixed-precision training. This approach provides learners with a conceptual advantage in adapting to rapidly evolving architectures. The collaboration with Google Brain's Swift for TensorFlow team (which released version 0.4 concurrently) highlights a rare open, cross-ecosystem educational initiative at a time when deep learning education is largely confined to a single framework.

Highlights

• Code-focused pedagogy: Every major component, including matrix multiplication, back-propagation, softmax, optimizers, and callbacks, is implemented from scratch in Python before touching library code.
• Completion of the fast.ai Part 1 (Practical Deep Learning for Coders) course is required as a prerequisite. This effectively doubles the total commitment to approximately twice the advertised 15 hours.
• The final two lessons transition to Swift for TensorFlow. This section, co-led by Chris Lattner, is released concurrently with the framework's new 0.4 version.

Critical take

The 'state-of-the-art' framework is inherently time-dependent. As of 2019, the architectures covered (ResNet, ULMFiT) were already a generation behind the rising transformer-based models. The Swift for TensorFlow track was also locked to version 0.4, which had uncertain long-term sustainability. Furthermore, requiring Part 1 as a mandatory prerequisite means the 'fundamentals' course is effectively a two-course commitment. This may deter the entry-level students that the free access mission aims to attract.