355 skills found · Page 4 of 12
thinakaranmanokaran / Commant GeneratorCode Comment Generator AI 🧠 automatically adds smart, single-line comments to your code in Visual Studio Code 💻. Save time ⏱️, boost readability 📖, and keep your projects clean ✨. Supports JavaScript, Python, Java, C++, HTML, CSS 🌐 and more — just select your code and let AI do the magic 🎉!
Osamah-Sufyan / AI RoadmapThis roadmap takes you from math and Python foundations to classical ML, deep learning, transformers, reinforcement learning, and advanced AI projects in a clear step-by-step progression.
M4RKUS28 / NexoraThis project is a full-stack application designed to deliver cutting-edge AI solutions for personalized learning assistance. It leverages a powerful Python backend and a modern React frontend to offer an innovative educational platform.
szmyty / OpenAI Retro SuperMarioWorld SNESThis project contains my implementation of the NEAT-Python algorithm to use a recurrent neural network that enables an AI-controlled Mario to train itself to complete levels in "Super Mario World" on SNES. I used the gym-retro-integration program to create my own variables from the game's RAM values and then used those variables to reward/penalize the AI. If the AI complete's a level, the neural network is saved as the winner.
aliezzahn / AI Research AgentA beginner-friendly Python project to build an AI research assistant using LangChain, with tools like Wikipedia, web search, and file saving. Includes structured output and detailed documentation.
akapkotel / Python Real Time Strategy GameThis is an RTS-like game written in Python 3 with arcade library. This is my most ambitious project up to date, since I try to overcome Python's shortcomings and make as efficiently-running game as possible. It will cover pathfinding, UI, AI, and graphics, using relatively slow and inefficient language. Main goal is to make it running sufficiently fast for p1layers to be able to actually play the game without pain. I do not have any good idea for the name of the game, so, for now it is just an RTS.
Zenable-io / AI Native PythonThe AI-Native paved road for Python projects
mdzaheerjk / Mdzaheerjk💻 AI/ML Enthusiast | Sharing code & learning in public 📚 Exploring Python, Data Science & Deep Learning 🛠️ Building real-world projects with modern AI tools 🤝 Open to contributions & innovative collaborations
AI-ANK / C3 Python NostreamPython Server for C3 AI app. A project that brings the power of Large Language Models (LLM) and Retrieval-Augmented Generation (RAG) within reach of everyone, particularly emphasizing accessibility. My goal is to democratize access to advanced AI technologies.
deepeshmittal / AI PacMan ProjectsImplemented various AI algorithms in Pac-Man projects developed by UC Berkeley. Implemented informed/blind state-space search using search algorithms like BFS, DFS, UCS and A* algorithm with heuristic calculation. Designed an algorithm for reflex agent, minimax and alpha-beta pruning. Reinforcement Learning using MDP (Value Iteration and Policy Evaluation) - Implemented Markov Decision process using Value iteration in the Pacman World. Designed perceptron classifier for data classification. Developed applications using python. http://ai.berkeley.edu/project_overview.html
ginking / Archimedes 1Archimedes 1 is a bot based sentient based trader, heavily influenced on forked existing bots, with a few enhancements here or there, this was completed to understand how the bots worked to roll the forward in our own manner to our own complete ai based trading system (Archimedes 2:0) This bot watches [followed accounts] tweets and waits for them to mention any publicly traded companies. When they do, sentiment analysis is used determine whether the opinions are positive or negative toward those companies. The bot then automatically executes trades on the relevant stocks according to the expected market reaction. The code is written in Python and is meant to run on a Google Compute Engine instance. It uses the Twitter Streaming APIs (however new version) to get notified whenever tweets within remit are of interest. The entity detection and sentiment analysis is done using Google's Cloud Natural Language API and the Wikidata Query Service provides the company data. The TradeKing (ALLY) API does the stock trading (changed to ALLY). The main module defines a callback where incoming tweets are handled and starts streaming user's feed: def twitter_callback(tweet): companies = analysis.find_companies(tweet) if companies: trading.make_trades(companies) twitter.tweet(companies, tweet) if __name__ == "__main__": twitter.start_streaming(twitter_callback) The core algorithms are implemented in the analysis and trading modules. The former finds mentions of companies in the text of the tweet, figures out what their ticker symbol is, and assigns a sentiment score to them. The latter chooses a trading strategy, which is either buy now and sell at close or sell short now and buy to cover at close. The twitter module deals with streaming and tweeting out the summary. Follow these steps to run the code yourself: 1. Create VM instance Check out the quickstart to create a Cloud Platform project and a Linux VM instance with Compute Engine, then SSH into it for the steps below. The predefined machine type g1-small (1 vCPU, 1.7 GB memory) seems to work well. 2. Set up auth The authentication keys for the different APIs are read from shell environment variables. Each service has different steps to obtain them. Twitter Log in to your Twitter account and create a new application. Under the Keys and Access Tokens tab for your app you'll find the Consumer Key and Consumer Secret. Export both to environment variables: export TWITTER_CONSUMER_KEY="<YOUR_CONSUMER_KEY>" export TWITTER_CONSUMER_SECRET="<YOUR_CONSUMER_SECRET>" If you want the tweets to come from the same account that owns the application, simply use the Access Token and Access Token Secret on the same page. If you want to tweet from a different account, follow the steps to obtain an access token. Then export both to environment variables: export TWITTER_ACCESS_TOKEN="<YOUR_ACCESS_TOKEN>" export TWITTER_ACCESS_TOKEN_SECRET="<YOUR_ACCESS_TOKEN_SECRET>" Google Follow the Google Application Default Credentials instructions to create, download, and export a service account key. export GOOGLE_APPLICATION_CREDENTIALS="/path/to/credentials-file.json" You also need to enable the Cloud Natural Language API for your Google Cloud Platform project. TradeKing (ALLY) Log in to your TradeKing (ALLY account and create a new application. Behind the Details button for your application you'll find the Consumer Key, Consumer Secret, OAuth (Access) Token, and Oauth (Access) Token Secret. Export them all to environment variables: export TRADEKING_CONSUMER_KEY="<YOUR_CONSUMER_KEY>" export TRADEKING_CONSUMER_SECRET="<YOUR_CONSUMER_SECRET>" export TRADEKING_ACCESS_TOKEN="<YOUR_ACCESS_TOKEN>" export TRADEKING_ACCESS_TOKEN_SECRET="<YOUR_ACCESS_TOKEN_SECRET>" Also export your TradeKing (ALLY) account number, which you'll find under My Accounts: export TRADEKING_ACCOUNT_NUMBER="<YOUR_ACCOUNT_NUMBER>" 3. Install dependencies There are a few library dependencies, which you can install using pip: $ pip install -r requirements.txt 4. Run the tests Verify that everything is working as intended by running the tests with pytest using this command: $ export USE_REAL_MONEY=NO && pytest *.py --verbose 5. Run the benchmark The benchmark report shows how the current implementation of the analysis and trading algorithms would have performed against historical data. You can run it again to benchmark any changes you may have made: $ ./benchmark.py > benchmark.md 6. Start the bot Enable real orders that use your money: $ export USE_REAL_MONEY=YES Have the code start running in the background with this command: $ nohup ./main.py & License Archimedes (edits under Invacio) Max Braun Frame under Max Braun, licence under Apache V2 License. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.
miguelusque / AI Transfer Learning For Image ClassificationThis repository contains the 'transfer learning for image classification' project of the Udacity's AI Programming with Python Nanodegree Program.
kaust-generative-ai / Pytorch Gpu Genai ProjectTemplate repository for a Python 3-based Generative AI project with GPU acceleration using the PyTorch ecosystem.
AsharibAli / Agentic AI ProjectsAgentic AI Projects build with Python, Streamlit, UV, Litellm, CrewAI, LangGraph, and OpenAI Agents SDK 🔥
Bidur-Khanal / Raspberry Pi GSM GPS WIFIThis project includes a python module with functions to send-receive SMS, get GPS coordinates, interfacing Raspberry Pi with GSM/GPS/GPRS module " A7 AI Thinker" using serial UART. It also has additional modules that enable the user to automatically connect to a wifi when SMS received in a format with UserName and Password (when interfaced with GSM module- A7 AI Thinker).
techinAI / DeepLearning.AIIn five courses, you will learn the foundations of Deep Learning, understand how to build neural networks, and learn how to lead successful machine learning projects. You will learn about Convolutional networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, and more. You will work on case studies from healthcare, autonomous driving, sign language reading, music generation, and natural language processing. You will master not only the theory, but also see how it is applied in industry. You will practice all these ideas in Python and in TensorFlow, which we will teach. You will also hear from many top leaders in Deep Learning, who will share with you their personal stories and give you career advice. AI is transforming multiple industries. After finishing this specialization, you will likely find creative ways to apply it to your work. We will help you master Deep Learning, understand how to apply it, and build a career in AI. Created by: Deeplearning.AI
FardeenMohammed / AI Virtual Painter 2.0AI Virtual Painter with geometric shapes | drawing in air | computer vision| opencv | AI TechStack AI Virtual Painter, an amazing project in python using opencv. This project has the following new features : 1 : Variable Eraser thickness 2 : Drawing circle 3 : Drawing rectangle 4 : Drawing ellipse 5 : Free Hand Writing 6 : Two fingers up, clears the complete screen 7 : Showing brush thickness on screen
JenolinJoy / Student Performance PredictionAI project predicting student performance using Python & Logistic Regression
whatlulumomo / MCTS AI ReversiThe project provides two version of AI of Reversi chess of python and QT(C++), based on MCTS algorithm. Actually, the version of C++ performs better than python by virtue of high efficiency.
princepal9120 / AI LearningAI Learning: A comprehensive repository for Artificial Intelligence and Machine Learning resources, primarily using Jupyter Notebooks and Python. Explore tutorials, projects, and guides covering foundational to advanced concepts in AI, ML, DL and Gen/Agentic Ai.
