The 30 Days of Python programming challenge is a step-by-step guide to learn the Python programming language in 30 days. This challenge may take more than 100 days. Follow your own pace. These videos may help too: https://www.youtube.com/channel/UC7PNRuno1rzYPb1xLa4yktw
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The 500 AI Agents Projects is a curated collection of AI agent use cases across various industries. It showcases practical applications and provides links to open-source projects for implementation, illustrating how AI agents are transforming sectors such as healthcare, finance, education, retail, and more.
Deep Learning papers reading roadmap for anyone who are eager to learn this amazing tech!
Python sample codes and textbook for robotics algorithms.
A curated list of awesome libraries, packages, strategies, books, blogs, tutorials for systematic trading.
AWS zero to hero repo for devops engineers to learn AWS in 30 Days. This repo includes projects, presentations, interview questions and real time examples.
This repository is a curated collection of links to various courses and resources about Artificial Intelligence (AI)
This repo collects awesome AI tools. Welcome everyone to recommend more awesome AI tools together!
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.
- deep-learning
- neural-network
- coursera
- andrew-ng
The microblogging application developed in my Flask Mega-Tutorial series. This version maps to the 2024 Edition of the tutorial.
- python
- flask
- webapp
Welcome to the SDE Interview Preparation Roadmap! This repository is not just about my personal journey; it's a collaborative space for collective learning. As I prepare for Software Development Engineer (SDE) interviews, I've created a comprehensive checklist to guide my preparation.
This repository consists of all the material required for cracking the coding rounds and technical interviews during placements.
- placements
- placement
- placement-preparation
- placement-preparation-portal
Follow along with my AI Agents Masterclass videos! All of the code I create and use in this series on YouTube will be here for you to use and even build on top of!
This is the Curriculum for "How to Learn Mathematics Fast" By Siraj Raval on Youtube
Common solutions and tools developed by Google Cloud's Professional Services team. This repository and its contents are not an officially supported Google product.
- google-cloud-platform
- google-cloud-dataflow
- google-cloud-ml
- google-cloud-compute
A production-ready FastAPI template for building AI agent applications with LangGraph integration. This template provides a robust foundation for building scalable, secure, and maintainable AI agent services.
This is a list of substantial, commercial-or-social-good mainstream websites which provide onion services.
- tor
- onion-sites
- onion-routing
Using python and scikit-learn to make stock predictions
- stock-prediction
- machine-learning
- scikit-learn
- python
Learn autonomous driving by building it: Python simulations and an open textbook where the system grows one stage at a time. Swap and compare localization, planning and control algorithms in the same system.
- algorithm
- autonomous-driving
- autonomous-navigation
- autonomous-vehicles
This is a continuously updated handbook for readers to easily track the latest Text-to-SQL techniques in the literature and provide practical guidance for researchers and practitioners.
Evidence archive for Jesus of Nazareth: research dossier (PDF), New Testament, gnostic and extracanonical gospels, church fathers, ancient witnesses, Roman legal documents, archaeology, and all 238 cited references — searchable reader at https://sybil-solutions.github.io/all-historical-evidence-of-jesus-christ/
This project shows how to use CNN to perform Chinese character recognition, a much more complicated task compared to MNIST digit recognition.
A Kata exercise. This one involves writing code to reserve seats on a train.
In this project, the Pacman agent will find paths through his maze world, both to reach a particular location and to collect food efficiently. I implemented general search algorithms such as depth-first, breadth-first, uniform cost, and A* search algorithms which are used to solve navigation problems in the Pacman world.