# Library Best for 1 PyTorch The dominant deep learning framework — research & production, dynamic graphs, huge ecosystem 2 TensorFlow / Keras Deep learning with strong production tooling (TF Serving, TF Lite); Keras gives a clean high-level API 3 Hugging Face Transformers Pre-trained LLMs & transformer models (text, vision, audio) — download and fine-tune SOTA models 4 scikit-learn Classical ML — regression, classification, clustering, preprocessing pipelines 5 NumPy Foundational array/tensor math that nearly every other library is built on 6 pandas Data loading, cleaning, and manipulation — the backbone of any ML data pipeline 7 LangChain Building LLM-powered apps — RAG, agents, chains, tool integration...
Introduction Data is everywhere — but raw numbers alone tell us very little. To make sense of data, statisticians use probability distributions : mathematical patterns that describe how values are likely to appear. Whether you're flipping a coin, measuring heights, counting website visitors, or predicting waiting times, there is a distribution that fits. Understanding these patterns helps data scientists, analysts, and curious learners spot trends, test ideas, and build smarter models. In this post, we'll explore nine essential distributions every data enthusiast should know — from the famous bell curve to the lesser-known Beta and Log Normal — explained simply, with real-world examples. Some of these are: Normal Distribution, Bernoulli Distribution, Binomial Distribution, Poisson Distribution, Exponential Distribution, Gamma Distribution, Beta Distribution, Uniform Distribution, Log Normal Distribution. See below for explanation. 1. Normal Distribution The Normal Distrib...