abdullah-eisa Goto Github PK

abdockgen

abdullah-eisa-supervised-and-unsupervised-machine-learning-models-for-financial-abu-dhabi-commercial

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algorithms-collection-python

Collection of Algorithms implemented in Python

application-guide

Everything you need to know about creating a strong application for the Qiskit Advocates program, all in one place!

arabiccompetitiveprogramming

The repository contains the ENGLISH description files attached to the video series in my ARABIC algorithms channel.

awesome-quantum-machine-learning

Here you can get all the Quantum Machine learning Basics, Algorithms ,Study Materials ,Projects and the descriptions of the projects around the web

camel

🐫 CAMEL: Communicative Agents for “Mind” Exploration of Large Scale Language Model Society

camera-calibration-and-stereo-vision

Camera Calibration and Stereo Vision project

cie555-assignment2-transfer-learning

classical-and-quantum-regression-analysis-for-the-optoelectronic-performance-of-ntcda

This repository contains the notebooks that can reproduce the results presented in the paper "Classical and quantum regression analysis for the optoelectronic performance of NTCDA/p-Si UV photodiode"

computer-network-project--gpn-with-udp

coursera-deep-learning-specialization

Notes, programming assignments and quizzes from all courses within the Coursera Deep Learning specialization offered by deeplearning.ai: (i) Neural Networks and Deep Learning; (ii) Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization; (iii) Structuring Machine Learning Projects; (iv) Convolutional Neural Networks;

coursera-deeplearning-specialization

Homework from the deeplearning.ai Deep Learning Specialization on Coursera (Completed)

deeplearning.ai-summary

This repository contains my personal notes and summaries on DeepLearning.ai specialization courses. I've enjoyed every little bit of the course hope you enjoy my notes too.

dfdc_deepfake_challenge

A prize winning solution for DFDC challenge

dsb2018_topcoders

DSB2018 [ods.ai] topcoders

edxcourse-introduction-to-linux

Summary of edX course - Introduction to Linux

eligible-claims-classifier-using-deep-learning-app

fend

General Front End Nanodegree Content Resources

free-programming-books

Forked :books: Freely available programming books

fwd-advanced-data-analysis-track0

hospital-dbms

intro-to-linux

Linux is actually everywhere: phone, cars, tv, supercomputers, world’s stock exchanges, desktops, servers, and embedded systems across the globe, Linux is one of the most reliable, secure and worry-free operating systems available. Let's learn basic linux. This note has taken from edx "Introduction to linux" offered by Linux Foundation.

learning-quantum-computing-course

⚛️ 💥 ⚙️ A project based in Quantum Physics/Mechanics and Quantum Computing. This projects contains Tutorials, Examples, Exercises, Papers, Posters and Illustrations covering several concepts of Quantum Physics/Mechanics and Quantum Computing, such as Quantum Information Science, Quantum Algorithms, Quantum Error Correction, Quantum Internet/Networks, Quantum Cryptography, Quantum Artificial Intelligence, Quantum Machine Learning and Quantum Game Theory. It works as a support for the Online Web Course of Learning Quantum Computing available in the following GitHub Page: https://rubenandrebarreiro.github.io/learning-quantum-computing. This project it's built using OpenQASM Framework (IBM's Open Quantum Assembly Language), Qiskit Framework (IBM Q Experience), Cirq Framework (Google AI Quantum), Q# Framework (Microsoft Quantum Development Kit - QDK), PyQuil Framework (Rigetti's Forest SDK), PennyLane Framework (Xanadu Quantum Technologies), Project Q Framework (ETHZ's Open-Source Software Framework for Quantum Computing) and Quantum Inspire (TU Delft's/QuTech's Quantum Computing Platform). The Tutorials, Examples and Exercises are mostly built in Python and Jupyter Notebooks. The Papers are built in TeX (LaTeX). The Posters and Illustrations are built in Adobe Photoshop, Adobe Illustrator and Adobe Photoshop Lightroom. Some Tutorials, Examples and Exercises use other important Packages, Libraries and Frameworks for Data Science, Cybersecurity, Cryptography, Quantum Physics/Mechanics and Quantum Computing, such as Bokeh, Cython, Dask, Jython, Matplotlib, Numba, NumPy, PyTorch, SciKit Learn, SciPy, SymPy, TensorFlow, Theano, QuTip, or even, OpenSSL and Python's Cryptography Library. Some Examples and Tutorials also use the NetSquid and SimulaQron Simulators Frameworks (TU Delft's/QuTech's Quantum Internet's/Network Simulators).

linuxfoundationx-lfs101x-introduction-to-linux

LinuxFoundationX: LFS101x Introduction to Linux :fire::fire::fire:

machine-learning

Content for Udacity's Machine Learning curriculum

machine-learning-cheat-sheet

Classical equations and diagrams in machine learning

machine-learning-collection

A resource for learning about Machine learning & Deep Learning

machine-learning-specialization-coursera

Contains Solutions and Notes for the Machine Learning Specialization By Stanford University and Deeplearning.ai - Coursera (2022) by Prof. Andrew NG

mingpt

A minimal PyTorch re-implementation of the OpenAI GPT (Generative Pretrained Transformer) training