Deep Learning Classes

Stanford, CMU and MIT Deep Learning Classes, which are all free!

Feb 26, 2025

Stanford CS224n: Natural Language Processing with Deep Learning

Stanford’s CS224n stands as the gold standard for NLP education, offering a rigorous exploration of neural architectures, sequence modeling, and transformer-based systems. The course begins with word embeddings and progresses to advanced topics like self-attention mechanisms and large language model (LLM) fine-tuning. Unlike many introductory NLP courses, CS224n integrates theoretical derivations with PyTorch implementations, requiring students to implement core algorithms like bidirectional LSTMs and transformer blocks from scratch. This hands-on approach ensures learners grasp both the mathematical underpinnings and engineering challenges of modern NLP systems.

Stanford CS25: Transformers United

As transformer architectures dominate AI research, Stanford’s CS25 addresses the critical gap in specialized transformer education. The course dissects seminal papers like "Attention Is All You Need" and explores variants such as sparse, linear, and memory-augmented transformers. Unique to CS25 is its focus on interpretability tools like attention rollout and prototype networks, enabling students to debug and optimize transformer-based systems. This course is particularly very good for those seeking roles in LLM development, as it covers scaling laws and distributed training techniques.

Stanford CS229: Machine Learning

Andrew Ng’s legendary CS229 remains a cornerstone of machine learning education, blending theoretical rigor with geometric intuition. The course distinguishes itself through detailed derivations of kernel methods, expectation-maximization algorithms, and reinforcement learning foundations. Unlike fast-paced tutorials, CS229 methodically builds from linear regression to variational inference, emphasizing the statistical assumptions behind each model. While some learners may find the lack of immediate coding projects challenging, the course’s problem sets—featuring derivations of backpropagation through time and Gaussian process regression.

CMU 11-747: Neural Networks for NLP

CMU’s 11-747 bridges the gap between traditional NLP and modern neural approaches, offering a unique curriculum that contrasts symbolic and distributional semantics. The course’s highlight is its systematic comparison of CNN, RNN, and transformer architectures for tasks like semantic role labeling and coreference resolution. 11-747 dedicates substantial time to low-resource NLP scenarios, teaching techniques like unsupervised parsing and cross-lingual transfer learning. This focus makes it invaluable for practitioners targeting multilingual or domain-specific applications. The course further excels in its coverage of dynamic evaluation methods, where students learn to adapt language models to evolving text streams—a topic rarely addressed in comparable programs.

CMU 11-777: Multimodal Machine Learning

CMU’s 11-777 pioneers education in cross-modal alignment, featuring cutting-edge content on contrastive learning (CLIP), neural rendering (NeRF), and audiovisual fusion. The course’s lab components involve aligning fMRI data with language embeddings and generating 3D scenes from text prompts, challenges absent in single-modality courses. 11-777 appeals to researchers building embodied AI or multimedia systems.

MIT 6.S191: Introduction to Deep Learning

MIT’s 6.S191 masterfully balances theoretical foundations with immediate application, using TensorFlow to implement generative adversarial networks (GANs) and music-generation RNNs in its first lab session23. The course stands out for its updated 2024 curriculum, which introduces diffusion models and LLM fine-tuning alongside traditional CNNs and RNNs. Unlike CMU’s specialized tracks, 6.S191 serves as a holistic introduction, comparing activation functions, regularization techniques, and optimization algorithms through real-world case studies like COVID-19 prediction models2. The inclusion of MIT-specific research—such as neural architecture search for robotics—provides industry-relevant insights unmatched by broader MOOCs.

MIT 6.S094: Deep Learning for Autonomous Vehicles

This specialized MIT course tackles the intersection of deep learning and robotics, with lectures on LiDAR point cloud processing, trajectory prediction networks, and safety-critical model verification. The curriculum’s uniqueness lies in its integration of simulation tools like CARLA, allowing students to test perception models in photorealistic environments. Compared to Stanford’s CS230 (general deep learning), 6.S094 offers domain-specific depth, teaching techniques like temporal fusion transformers for motion forecasting—skills directly applicable to autonomous systems roles.

DeepMind COMP M050: Introduction to Reinforcement Learning

Developed by DeepMind researchers, COMP M050 reimagines reinforcement learning (RL) education with an emphasis on scalability and safety. The course progresses from multi-armed bandits to hierarchical RL, featuring case studies on AlphaFold and robotic manipulation. Unlike theoretical RL courses, COMP M050 includes PyTorch labs on distributed training using Ray, preparing students for large-scale RL deployment. Its comparison of model-based vs. model-free approaches in partially observable environments offers practical insights absent in CMU’s graphical models course.

DeepMind Deep Learning Series: Attention and Memory Mechanisms

This UCL-DeepMind collaboration delves into advanced architectures like differentiable neural computers and perceivers. The course’s standout feature is its mathematical treatment of attention as kernel regression, providing a unified framework to analyze transformers, perceivers, and graph attention networks. For students who’ve completed MIT’s introductory material, this series offers the necessary depth to innovate in architecture design.

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