One day, DSPy will be on top of that chart. DSPy developed the Intent-Oriented Programming paradigm, and this is fundamentally the effective way to add intelligence pieces into your code. It will thus be as ubiquitous as numpy.

This repo is a goldmine for anyone learning RAG 🔥. No LangChain. No FAISS. Just clean, readable Python using openai, numpy, matplotlib & friends. Every technique is broken down with real code. 100% open-source. Must-bookmark!

If you are a Beginner in Python:. - Django / Flask / FastAPI. - NumPy / Pandas. - Data Science / Data Analysis. - Machine Learning. Don't miss out these Top YouTube Courses:

📊Just implemented Multiple Linear Regression From Scratch (Numpy and Math) . - Check YouTube Video going in-depth : - GitHub Repo(Star it hehe) : (Cool manim animation )

Using PyTorch after solving 30 NumPy Deep-ML questions

Learn Numpy Fundamentals

千葉大が無料公開しているPython解説サイト。.具体例が豊富でわかりやすい。.章末に演習問題と解答例も付いている。 . ・プログラムの作成と実行.・変数と型.・代入と演算.・リストと辞書.・条件分岐.・繰り返し.・ファイルの読み書き.・関数.・クラス.・numpy.・scipy

Mastering Python for Data Science?.Here are 9 essential libraries you must know:. - NumPy.- Pandas.- SciPy.- Scikit-Learn.- TensorFlow.- Keras.- PyTorch.- Theano.- Matplotlib . Each one unlocks a powerful capability — from data wrangling to deep learning!. #Python #DataScience

Nx and Hugin are my new favorite things in OCaml, so good to have something like NumPy in this language. It just feels right. Even tho I only plot stuff from some load testing, it's so awesome. Great job @tmattio_ ! Looking forward to the workshop at @FunOCaml!

oh husbant. you cannot write autograd in numpy from scratch without using chatgpt despite describing yourself as an ML professional and this is your punishment

The math behind the Quant Scientist Stack:. • SciPy: $0.• Zipline: $0.• Python: $0.• NumPy: $0.• PyFolio: $0.• pandas : $0.• OpenBB: $0.• Empyrical: $0.• AlphaLens: $0.• Statsmodels: $0.• RiskFolio-Lib: $0. You can start algorithmic trading for free.

#180DaysofWhatever .> learning numpy library .> did debugging

SciMaster: Towards General-Purpose Scientific AI Agents, Part I. X-Master as Foundation: Can We Lead on Humanity's Last Exam?. X-Master introduces a general-purpose, open tool-augmented reasoning agent: .- Uses Python as an interaction language (NumPy, web search, code execution)

25 Python libraries you MUST know:. -NumPy, Pandas, Matplotlib.-TensorFlow, PyTorch, Scikit-learn.-Django, Flask, FastAPI.-NLTK, spaCy, SymPy.-Plotly, Dash, Bokeh.-Selenium, BeautifulSoup, OpenCV & more. #Python #DataScience #MachineLearning #100DaysOfCode #PythonLibraries

finally completed it, ANN for MNIST classification from scratch only used numpy-scikit, implemented kfold cv, grid search, backprop, adam from scratch just pure numpy. will share the code and resources tomorrow, pretty tired rn

Day 12 of data science .Going through numpy.Arrays✅.Multidimensional indexing and axis✅.Data type✅

The book “#AlgorithmicTrading with Python” discusses modern quant trading methods in #Python with a heavy focus on PANDAS, Numpy, and scikit-learn. Available at: by @__chrisconlan__. ➡️ Get free code from the book here: ⬅️

"Release early, release often is the motto of open source.". The creator of NumPy shares the story of how he fell in love with open source 💙 . Watch the full story ➡️

random recursive functions. art made in python. built with numpy/lightgbm. since computing the functions is slow, a surrogate model is trained on synthetic data for exploration. 100mil. possible functions can then be filtered through to find some interesting ones quickly.