Tools and Libraries
Tools and Libraries: 11 artificial-intelligence concepts explained in plain language, each with a short definition and a fuller explanation. Part of the Guide to AI knowledge base.
Guide to AI (ai-know.org) is a free, bilingual (English and Hebrew) knowledge base about artificial intelligence: explained concepts, curated tools, a daily news digest and a browser-side token calculator. It is a static site — no account, no tracking, no paywall.
Concepts in this section
TensorFlow
An open-source library for numerical computation and machine learning, developed by Google.
TensorFlow is a comprehensive platform for machine learning and artificial intelligence. Key features: Support for deep learning and complex neural networks. Ability to work on various hardware platforms, including CPU, GPU, and TPU. Tools for visualizing learning processes (TensorBoard). Support for deploying models on edge devices, servers, and cloud. Rich ecosystem of tools and add-ons.
PyTorch
An open deep learning library, especially popular in research.
PyTorch is a deep learning library developed by Facebook. Its advantages include: Flexibility and ease of use, especially for developing complex models. Support for dynamic computation, allowing network structure changes at runtime. Good integration with the Python ecosystem. Large user community and extensive support. Excellent performance, especially in research and development of innovative models.
Keras
A high-level interface for building neural networks with JAX, TensorFlow, or PyTorch backends.
Keras is a Python library for building neural networks, offering: A simple and intuitive interface for building complex models. Support for a wide range of built-in layers and algorithms. Support for JAX, TensorFlow, and PyTorch backends. Options for extension and writing custom layers and algorithms. Keras is designed to accelerate the experimentation and development process of deep learning models.
Scikit-learn
An open-source Python library for classical machine learning, data preprocessing, and model evaluation.
Scikit-learn provides consistent Python interfaces for many common machine learning workflows. Includes classification, regression, clustering, dimensionality reduction, and model selection algorithms. Provides preprocessing tools, pipelines, and evaluation metrics. Integrates with NumPy and SciPy for numerical and scientific computing.
Hugging Face Transformers
An open-source library for using and training pretrained text, vision, audio, and multimodal models.
Hugging Face Transformers provides APIs and pretrained model implementations for inference and training. Supports natural-language processing, computer vision, audio, and multimodal tasks. Includes utilities for loading pretrained models and fine-tuning them on task-specific data. Connects to the Hugging Face Hub for model discovery and sharing.
JAX
A Python library for high-performance numerical computing with automatic differentiation and compilation.
JAX provides a NumPy-like interface together with program transformations for machine learning and scientific computing. Supports automatic differentiation, just-in-time compilation, and automatic vectorization. Uses OpenXLA to target CPU, GPU, and TPU hardware. Is commonly used as a foundation for research-oriented numerical and ML systems.
LangChain
A framework for building applications and agents that use language models and external tools.
LangChain provides building blocks for composing language-model applications. Supports tool calling, retrieval workflows, agent patterns, and integrations. Helps connect models with data sources, APIs, and application state. Can be used alongside LangGraph for durable agent execution and workflow control.
MLflow
An open-source platform for tracking experiments and managing the machine learning lifecycle.
MLflow helps teams record, compare, package, and manage machine learning work. Tracks runs, parameters, metrics, datasets, and artifacts. Supports model packaging, registry workflows, and deployment-oriented management. Includes tools for traditional ML as well as LLM and agent evaluation workflows.
NumPy
The core Python library for fast array computing that most of the scientific and ML stack builds on.
NumPy provides the n-dimensional array and the numerical routines that much of the Python data ecosystem relies on. Supports vectorized math, broadcasting, linear algebra, random sampling, and Fourier transforms. Runs array operations in compiled code, which is far faster than equivalent pure-Python loops. Its array interface is shared or mirrored by libraries such as pandas, scikit-learn, SciPy, and JAX.
XGBoost
An open-source gradient-boosted decision tree library widely used for tabular data.
XGBoost trains ensembles of decision trees in which each new tree corrects the errors of the previous ones. Supports classification, regression, and ranking tasks. Includes regularization, native handling of missing values, and parallel and distributed training. Gradient-boosted trees remain a strong baseline for structured, tabular datasets.
vLLM
An open-source engine for high-throughput, memory-efficient serving of large language models.
vLLM is used to run language-model inference efficiently on GPUs and other accelerators. Uses PagedAttention to manage the attention key-value cache with less wasted memory. Batches incoming requests continuously to raise throughput. Offers an OpenAI-compatible API server, so applications can switch to self-hosted models with few code changes.