This repository is designed to help you master NumPy, the fundamental package for scientific computing with Python. Whether you are just getting started or looking to refine your skills with advanced techniques, this repo covers a comprehensive range of topics and functionalities. It covers the range of topics from beginner to advance.
This Python Project code implements a simple neural network built from scratch using the NumPy library to classify whether a given number is odd or even.
All about python NumPy library
Repository to store sample python programs for python learning
Comprehensive tutorials on popular Python libraries: NumPy, Pandas, Matplotlib, Seaborn, Keras, TensorFlow, and PyTorch. Easy-to-follow guides with code examples for data manipulation, visualization, and machine learning. Perfect for beginners and experienced developers. Contribute and learn with us!
Repository for participants of the "Scientific Python" training
Introduction to the Python programming language for data analysis in earth sciences
A collection of Jupyter notebooks📒 and study notes on NumPy, documenting essential concepts and operations for numerical computing in Python.
This repo is regarding simple implementation of small concepts hidden in big Deep learning architectures
Theory of digital signal processing (DSP): signals, filtration (IIR, FIR, CIC, MAF), transforms (FFT, DFT, Hilbert, Z-transform) etc.
Notes & Code to go over "Grokking Deep Learning" Book by Andrew Trask
Just simple tasks of practice of NumPy library of python
Files in Python
Numpy Basics and some methods for Machine learning
NumPy is the fundamental package for scientific computing with Python. It contains among other things: a powerful N-dimensional array object sophisticated (broadcasting) functions tools for integrating C/C++ and Fortran code useful linear algebra, Fourier transform, and random number capabilities
Numerical Python, working with numerical data in Python, going from Python lists to Numpy arrays, CSV data files using Numpy, and etc..
📓 Python scripts and Jupyter Notebooks for the Physics Laboratory 3 course of the University of Florence
Annotated transformer architecture in numpy and jax
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