---
title: "AI research tools"
description: "The working researcher's stack — experiment tracking, data curation, eval frameworks, and the tools labs actually use."
canonical_url: "https://fanout.sh/ai/tools"
md_url: "https://fanout.sh/ai/tools.md"
access: "public"
---

# AI research tools

The working researcher's stack — experiment tracking, data curation, eval frameworks, and the tools labs actually use.

## Research tools

### [Google Colab](https://colab.research.google.com)

Free GPU notebooks in the browser. The fastest way to start training models.

- Category: Notebooks & IDEs

### [PyTorch](https://pytorch.org)

The dominant deep learning framework. Dynamic computation graphs, Pythonic API.

- Category: Frameworks

### [Weights & Biases](https://wandb.ai)

Experiment tracking, model registry, hyperparameter sweeps. Essential MLOps.

- Category: Platforms

### [TensorBoard](https://www.tensorflow.org/tensorboard)

Visualize training metrics, model graphs, embeddings. Built into TF and PyTorch.

- Category: Visualization

### [Arxiv](https://arxiv.org/list/cs.LG/recent)

Open-access research papers. Where all ML papers are published first.

- Category: Papers & Data

### [ONNX Runtime](https://onnxruntime.ai)

Cross-platform ML inference. Convert and run models anywhere.

- Category: Deployment & Inference

## Practice sites

### [Kaggle](https://kaggle.com)

ML competitions, datasets, and notebooks. The competitive ML platform.

- Category: Competitions

### [LeetCode ML](https://leetcode.com/problemset/machine-learning/)

ML and data science interview questions. Practice for FAANG interviews.

- Category: Exercises & Projects

### [TensorFlow Playground](https://playground.tensorflow.org)

Visualize neural networks in your browser. Tweak architectures, see results live.

- Category: Interactive

### [DeepLearning.AI](https://deeplearning.ai)

Andrew Ng's specializations. Deep Learning, MLOps, NLP, GenAI courses.

- Category: Courses

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