Chapter 28: External AI Resources for Research

Tip

This chapter focuses on carefully selected, high-value resources that are widely used, well maintained, and directly relevant to academic research. It is not meant to be exhaustive. New tools and platforms appear constantly, and the right question is always whether a resource is reliable and appropriate for your work, not simply whether it is popular.

Overview

Beyond what UM provides, the broader AI community offers a wide range of open tools, datasets, and learning resources that can meaningfully accelerate research. The sections below organize these by purpose: learning and training, open datasets, modeling frameworks and tools, compute options, and software ecosystems.

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Learning and Training Resources

DeepLearning.AI

Founded by Andrew Ng, DeepLearning.AI offers a well-regarded library of courses covering machine learning foundations, deep learning, large language models, prompt engineering, RAG, and applied topics in computer vision, NLP, and reinforcement learning. The courses are structured for learners who want both conceptual grounding and hands-on practice.

Link: https://www.deeplearning.ai/

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Kaggle Learn

Kaggle Learn provides short, practical micro-courses covering Python, pandas, machine learning, deep learning, computer vision, time series, and LLM applications. These are a good starting point for researchers who want to build hands-on fluency without a large time commitment.

Link: https://www.kaggle.com/learn

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The Turing Way

The Turing Way is an open-source, community-driven handbook covering reproducible research, project management and documentation, and ethics and open science. It is one of the direct inspirations for this handbook and remains an excellent reference for researchers building responsible, transparent workflows.

Link: https://the-turing-way.netlify.app/

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Fast.ai

Fast.ai offers free practical deep learning courses that start with working models and build conceptual understanding from there, rather than the other way around. Topics include NLP, vision, tabular learning, and model interpretation.

Link: https://www.fast.ai/

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Dive into Deep Learning (D2L)

D2L is a fully open-source deep learning textbook with runnable code in Jupyter notebooks. It is widely used in academic courses for combining mathematical foundations with hands-on, experiment-ready implementations in PyTorch. It is well suited for researchers who want both the theory and the code in one place.

Link: https://d2l.ai/

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Hugging Face NLP Course

Hugging Face offers a free, interactive NLP course that walks through using transformers for text classification, named entity recognition, question answering, and summarization. It is built around the same transformers library used throughout Part 3 of this handbook, so the examples map directly to what you would do in practice. A good next step after reading the BERT and RAG chapters if you want to go deeper on implementation.

Link: https://huggingface.co/learn/nlp-course/

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MIT Introduction to Generative AI

A freely available series of video lectures from MIT covering foundation models, generative AI architectures, and practical applications across research domains. The videos are pitched at a conceptual level rather than a heavy math level, which makes them accessible to researchers coming from outside computer science.

Link: https://mit-genai.pubpub.org/

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Google Machine Learning Crash Course

A structured, freely available introduction to machine learning fundamentals from Google. It covers core concepts like gradient descent, overfitting, and classification, and is a reasonable starting point for researchers with no prior ML background.

Link: https://developers.google.com/machine-learning/crash-course

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Anthropic Prompt Library

A curated collection of ready-to-use prompts for research and professional tasks, covering literature review, data analysis, writing, coding assistance, and more. Useful for getting a feel for how to structure prompts for different kinds of work, and for benchmarking your own prompts against examples that have been tested across domains. Pairs well with Chapter 3 of this handbook on prompt engineering.

Link: https://docs.anthropic.com/en/prompt-library/library

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Recommended Reading

If you want a grounded view of where generative AI actually stands in research contexts, this paper is worth the time: Reddy and Shojaee offer a broad and honest assessment of where generative AI is making a real difference in scientific discovery and where significant limitations remain ([Reddy and Shojaee, 2025]).

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Open Datasets and Searchable Repositories

Kaggle Datasets

Kaggle hosts one of the largest collections of queryable datasets across domains, including tabular, image, text, and time series data. Most datasets include metadata and community notebooks that let you start exploring immediately.

Link: https://www.kaggle.com/datasets

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HuggingFace Datasets

HuggingFace offers a massive ecosystem of machine learning-ready datasets with a standardized API, covering NLP, vision, audio, multimodal, and scientific domains.

Link: https://huggingface.co/datasets

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UCI Machine Learning Repository

The UCI repository hosts a large collection of classic and benchmark datasets commonly used for prototyping and evaluation across a wide range of tasks.

Link: https://archive.ics.uci.edu/

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PhysioNet

PhysioNet provides open access to biomedical and physiological datasets, including several synthetic and demo sets well suited for methods development without requiring IRB approval.

Link: https://physionet.org/

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OpenNeuro

OpenNeuro is a free platform for sharing and accessing neuroimaging datasets, including fMRI, EEG, MEG, and fNIRS data.

Link: https://openneuro.org/

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Modeling, Frameworks, and Tools

HuggingFace Transformers

HuggingFace is the central open-source hub for pre-trained LLMs, vision models, audio models, multimodal architectures, and fine-tuning pipelines. If a published model has an open checkpoint available, it is almost certainly accessible through HuggingFace.

Link: https://huggingface.co/models

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Papers with Code

Papers with Code is a searchable collection of machine learning papers that link directly to their code implementations, datasets, and benchmark results. It is one of the most practical resources for seeing exactly how a method was implemented, not just described. MIDAS includes it as a recommended tool in their generative AI resource hub.

Link: https://paperswithcode.com/

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Stanford HELM

HELM (Holistic Evaluation of Language Models) is a living benchmark developed at Stanford that evaluates large language models across a wide range of scenarios in a transparent and systematic way. If you need to compare models for a specific task based on accuracy, fairness, calibration, or robustness, HELM provides a more principled basis for that comparison than relying on marketing claims ([Liang et al., 2022]).

Link: https://crfm.stanford.edu/helm/

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spaCy

spaCy is a fast, production-ready NLP library for applied tasks like named entity recognition, dependency parsing, part-of-speech tagging, and text classification. It is a lighter-weight alternative to the full Hugging Face transformers stack when you need something that runs quickly on a laptop and does not require a GPU. Particularly well suited for researchers processing large volumes of text where raw extraction and annotation matter more than generative capability.

Link: https://spacy.io/

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Microsoft AutoGen

AutoGen is an open framework from Microsoft Research for building multi-agent AI systems, where multiple LLM-powered agents collaborate to complete tasks. It is the framework most directly relevant to the concepts introduced in the AI Agents chapter of this handbook. The documentation includes working examples for research-relevant scenarios like code generation, literature search, and data analysis pipelines.

Link: https://microsoft.github.io/autogen/

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AutoGluon

AutoGluon is the AutoML toolkit used throughout Part 2 of this handbook. It covers tabular prediction, NLP, vision, and multimodal learning, and is designed for rapid hypothesis testing without requiring you to write complex modeling code from scratch.

Link: https://auto.gluon.ai/

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Ollama

Ollama makes it straightforward to run open-source LLMs locally on your own machine, with no data sent to external servers. This is particularly useful for researchers working with sensitive or unpublished data who need LLM capabilities but cannot use a cloud-based service. It supports a growing range of models including Llama, Mistral, and Gemma.

Link: https://ollama.com/

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PyTorch

PyTorch is the dominant deep learning framework in academic research, widely used for building, training, and deploying custom neural network models.

Link: https://pytorch.org/

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TensorFlow and Keras

TensorFlow and its high-level interface Keras are widely used in both research and production settings, with a large community and extensive documentation.

Link: https://www.tensorflow.org/

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LangChain and LlamaIndex

LangChain and LlamaIndex are the two most commonly used frameworks for building retrieval-augmented generation pipelines, LLM agents, and data-connected chatbots.

LangChain: https://www.langchain.com/ LlamaIndex: https://www.llamaindex.ai/

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Cloud Compute and Low-Cost GPU Resources

Google Colab

Colab provides free or low-cost Jupyter notebooks with GPU and TPU access. It is one of the most accessible ways to run ML experiments without any local hardware setup, and the notebooks used throughout this handbook are designed to run on Colab.

Link: https://colab.research.google.com/

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Kaggle Notebooks

Kaggle offers free GPU-enabled notebooks with zero configuration required. A useful fallback when Colab GPU availability is limited.

Link: https://www.kaggle.com/code

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Vast.ai

Vast.ai is a marketplace for renting GPU compute at relatively low cost, with a wide range of hardware configurations available. A practical option for larger training jobs that exceed what Colab or Kaggle provide.

Link: https://vast.ai/

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Lambda Labs

Lambda Labs provides cloud GPU infrastructure oriented toward research use, with options ranging from on-demand instances to reserved capacity.

Link: https://lambdalabs.com/

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RunPod

RunPod offers easy-to-configure GPU workspaces with ready-made templates for Jupyter notebooks, inference servers, and custom environments.

Link: https://runpod.io/

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Software and Notebook Ecosystems

GitHub

GitHub is the standard platform for version control, open-source collaboration, and reproducibility in research software. This handbook’s source is hosted there.

Link: https://github.com/

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VS Code

VS Code is a widely used editor for AI and data science workflows, with strong support for Python, Jupyter notebooks, and remote development.

Link: https://code.visualstudio.com/

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JupyterLab

JupyterLab is the standard interactive notebook environment for data exploration, ML prototyping, and visualization.

Link: https://jupyter.org/

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Quick Reference Table

Category	Resource	Description	Link
Learning	DeepLearning.AI	High-quality AI courses	https://www.deeplearning.ai/
Learning	Kaggle Learn	Practical micro-courses	https://www.kaggle.com/learn
Learning	Turing Way	Reproducible research handbook	https://the-turing-way.netlify.app/
Learning	Fast.ai	Practical deep learning	https://www.fast.ai/
Learning	D2L	Open-source deep learning textbook	https://d2l.ai/
Learning	HuggingFace NLP Course	Free interactive NLP and transformers course	https://huggingface.co/learn/nlp-course/
Learning	MIT Intro to Generative AI	Conceptual video lectures on foundation models	https://mit-genai.pubpub.org/
Learning	Anthropic Prompt Library	Curated prompts for research tasks	https://docs.anthropic.com/en/prompt-library/library
Datasets	Kaggle Datasets	Largest open dataset library	https://www.kaggle.com/datasets
Datasets	HuggingFace Datasets	ML-ready dataset hub	https://huggingface.co/datasets
Datasets	UCI Repository	Classic benchmark datasets	https://archive.ics.uci.edu/
Datasets	PhysioNet	Biomedical and physiological datasets	https://physionet.org/
Datasets	OpenNeuro	Open neuroimaging datasets	https://openneuro.org/
Tools	HuggingFace Transformers	LLM and model hub	https://huggingface.co/models
Tools	Papers with Code	AI papers with code and benchmarks	https://paperswithcode.com/
Tools	Stanford HELM	LLM evaluation benchmark	https://crfm.stanford.edu/helm/
Tools	AutoGluon	AutoML for rapid experiments	https://auto.gluon.ai/
Tools	Ollama	Run LLMs locally for sensitive data work	https://ollama.com/
Tools	spaCy	Fast NLP for extraction and annotation	https://spacy.io/
Tools	Microsoft AutoGen	Multi-agent AI framework	https://microsoft.github.io/autogen/
Compute	Colab	Free GPU notebooks	https://colab.research.google.com/
Compute	Kaggle Notebooks	Free GPU compute	https://www.kaggle.com/code
Compute	Vast.ai	Low-cost GPU rental	https://vast.ai/
Compute	Lambda Labs	Research-oriented GPU cloud	https://lambdalabs.com/
Compute	RunPod	Easy GPU workspace setup	https://runpod.io/

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[1]

Chandan K. Reddy and Parshin Shojaee. Towards scientific discovery with generative AI: progress, opportunities, and challenges. 2025. Accessed 2026-03-06. URL: https://arxiv.org/abs/2501.05510.

[2]

Percy Liang, Rishi Bommasani, Tony Lee, Dimitris Tsipras, and others. Holistic evaluation of language models. In Transactions on Machine Learning Research. 2022. URL: https://crfm.stanford.edu/helm/.