machine-learning-list

Introduction to the Machine Learning Reading List

The Machine Learning Reading List project, hosted by Elicit, aims to facilitate the learning process for new employees diving into the world of machine learning, with a strong emphasis on language models. This curriculum is thoughtfully curated to balance the understanding of core ML concepts necessary for immediate deployment in practical applications while also considering advanced techniques that can support long-term scalability. Importantly, this resource isn't just for employees—Elicit is also open to hiring new talent in ML and software engineering.

Purpose and Structure

The reading list is intended to provide a structured learning journey through a series of topics that are pivotal in the field of machine learning. The materials are organized into "tiers" across various subjects, indicating the suggested order of reading to build up foundational knowledge before moving on to more complex concepts.

For ease of navigation, the contents are divided into several major sections:

Fundamentals: This includes resources that cover an introduction to machine learning, transformers, key model architectures, and the training and finetuning processes.
Reasoning and Runtime Strategies: Here, learners can explore the intricacies of in-context reasoning, task decomposition, debates, and how to effectively use tools and scaffolding.
Applications: This covers practical applications of ML in science, forecasting, and search and ranking systems.
ML in Practice: Pertains to the real-world deployment of ML models, including production, benchmarks, and datasets.
Advanced Topics: Delves into more complex areas like world models, planning, uncertainty management, interpretability, and reinforcement learning.
The Big Picture: Looks at overarching themes like AI scaling, safety, economic/social impacts, and philosophical considerations.

Learning Path

The materials are categorized into multiple tiers for each topic, allowing learners to gradually increase their understanding:

Fundamentals include introductory materials on machine learning concepts and neural networks, and more advanced insights into backpropagation and reinforcement learning.
Transformers offer an in-depth visual and theoretical explanation of this critical ML architecture, from the basics to advanced implementations.
Key Foundation Model Architectures focus on key papers about influential models like GPT-2, GPT-3, and others.
Training and Finetuning cover techniques for improving model performance through various training strategies.

Each tier provides progressively complex and detailed explanations of the content, ensuring a comprehensive grasp of topics.

Enhancing Practical Skills

The Reading List places special emphasis on "ML in Practice," bridging the gap between theoretical knowledge and real-world application. This includes resources on deploying models in production environments, understanding benchmarks to evaluate model performance, and selecting the right datasets for diverse applications.

Looking at the Broader Impact

"The Big Picture" section encourages learners to reflect on the societal and philosophical implications of AI and machine learning technologies—ensuring that as they master the technical aspects, they're also aware of the responsibilities and ethical considerations that come with wielding such powerful tools.

Encouragement to Engage

By engaging with this reading list, learners are invited not only to expand their technical expertise but also to contemplate the broader implications of their work in AI, preparing them to contribute thoughtfully and effectively in the field.