# Machine Learning in Python: Main developments and technology trends in data science, machine learning, and artificial intelligence

arXiv: https://arxiv.org/abs/2002.04803 · HTML: https://mlsanity.com/paper/2002.04803/

## Key facts

- Cited by 82 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Feb 2020.

## Built on by (most cited first, 30 of 82)

- [IGB: Addressing The Gaps In Labeling, Features, Heterogeneity, and Size of Public Graph Datasets for Deep Learning Research](https://mlsanity.com/paper/2302.13522/) (2023)
- [Benchopt: Reproducible, efficient and collaborative optimization benchmarks](https://mlsanity.com/paper/2206.13424/) (2022)
- [OpenSplat3D: Open-Vocabulary 3D Instance Segmentation using Gaussian Splatting](https://mlsanity.com/paper/2506.07697/) (2025)
- [Cluster and Predict Latent Patches for Improved Masked Image Modeling](https://mlsanity.com/paper/2502.08769/) (2025)
- [Large Language Models Encode Semantics and Alignment in Linearly Separable Representations](https://mlsanity.com/paper/2507.09709/) (2025)
- [DeepCAVE: An Interactive Analysis Tool for Automated Machine Learning](https://mlsanity.com/paper/2206.03493/) (2022)
- [Let Go of Your Labels with Unsupervised Transfer](https://mlsanity.com/paper/2406.07236/) (2024)
- [Fine-grained Category Discovery under Coarse-grained supervision with Hierarchical Weighted Self-contrastive Learning](https://mlsanity.com/paper/2210.07733/) (2022)
- [SepicNet: Sharp Edges Recovery by Parametric Inference of Curves in 3D Shapes](https://mlsanity.com/paper/2304.06531/) (2023)
- [Agglomerative Token Clustering](https://mlsanity.com/paper/2409.11923/) (2024)
- [Beyond Scalars: Concept-Based Alignment Analysis in Vision Transformers](https://mlsanity.com/paper/2412.06639/) (2024)
- [The Wasserstein Believer: Learning Belief Updates for Partially Observable Environments through Reliable Latent Space Models](https://mlsanity.com/paper/2303.03284/) (2023)
- [Random Fourier Signature Features](https://mlsanity.com/paper/2311.12214/) (2023)
- [PeaTMOSS: A Dataset and Initial Analysis of Pre-Trained Models in Open-Source Software](https://mlsanity.com/paper/2402.00699/) (2024)
- [Look Into the LITE in Deep Learning for Time Series Classification](https://mlsanity.com/paper/2409.02869/) (2024)
- [SCMIL: Sparse Context-aware Multiple Instance Learning for Predicting Cancer Survival Probability Distribution in Whole Slide Images](https://mlsanity.com/paper/2407.00664/) (2024)
- [Weighted Point Set Embedding for Multimodal Contrastive Learning Toward Optimal Similarity Metric](https://mlsanity.com/paper/2404.19228/) (2024)
- [Wasserstein Distances, Neuronal Entanglement, and Sparsity](https://mlsanity.com/paper/2405.15756/) (2024)
- [A Large-Scale Study of Model Integration in ML-Enabled Software Systems](https://mlsanity.com/paper/2408.06226/) (2024)
- [AI-powered virtual tissues from spatial proteomics for clinical diagnostics and biomedical discovery](https://mlsanity.com/paper/2501.06039/) (2025)
- [MCI-GRU: Stock Prediction Model Based on Multi-Head Cross-Attention and Improved GRU](https://mlsanity.com/paper/2410.20679/) (2024)
- [Bootstraping Clustering of Gaussians for View-consistent 3D Scene Understanding](https://mlsanity.com/paper/2411.19551/) (2024)
- [Visualizing LLM Latent Space Geometry Through Dimensionality Reduction](https://mlsanity.com/paper/2511.21594/) (2025)
- [GPU Semiring Primitives for Sparse Neighborhood Methods](https://mlsanity.com/paper/2104.06357/) (2021)
- [Automated Detection of Abnormalities from an EEG Recording of Epilepsy Patients With a Compact Convolutional Neural Network](https://mlsanity.com/paper/2105.10358/) (2021)
- [Accelerating Genetic Programming using GPUs](https://mlsanity.com/paper/2110.11226/) (2021)
- [Self-Supervised Dynamical System Representations for Physiological Time-Series](https://mlsanity.com/paper/2512.00239/) (2025)
- [RT-kNNS Unbound: Using RT Cores to Accelerate Unrestricted Neighbor Search](https://mlsanity.com/paper/2305.18356/) (2023)
- [Inshrinkerator: Compressing Deep Learning Training Checkpoints via Dynamic Quantization](https://mlsanity.com/paper/2306.11800/) (2023)
- [Using causal inference to avoid fallouts in data-driven parametric analysis: a case study in the architecture, engineering, and construction industry](https://mlsanity.com/paper/2309.11509/) (2023)

## Abstract (arXiv)

Smarter applications are making better use of the insights gleaned from data, having an impact on every industry and research discipline. At the core of this revolution lies the tools and the methods that are driving it, from processing the massive piles of data generated each day to learning from and taking useful action. Deep neural networks, along with advancements in classical ML and scalable general-purpose GPU computing, have become critical components of artificial intelligence, enabling many of these astounding breakthroughs and lowering the barrier to adoption. Python continues to be the most preferred language for scientific computing, data science, and machine learning, boosting both performance and productivity by enabling the use of low-level libraries and clean high-level APIs. This survey offers insight into the field of machine learning with Python, taking a tour through important topics to identify some of the core hardware and software paradigms that have enabled it. We cover widely-used libraries and concepts, collected together for holistic comparison, with the goal of educating the reader and driving the field of Python machine learning forward.