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Reinforcement learning (RL) is an area of machine learning concerned with how intelligent agents ought to take actions in an environment in order to maximize the notion of cumulative reward. Reinforcement learning is one of three basic machine learning paradigms, alongside supervised learning and unsupervised learning.
About Reinforcement Learning
- What is reinforcement learning? Comprehensive article on Wikipedia about reinforcement learning
Reinforcement Learning AI technology page Hackathon projects
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Many Personal Trainers use Google Sheets to manage their clients, and it gets tedious to upkeep communication and sustains the same amount of extrinsic motivation. But what if we can use AI to maintain a constant stream of communication and allow your clients to autonomously begin to internalize motivation and begin to regulate their behavior? I am introducing Motivational Lambda, which contextualizes client goals and lifestyle information within a motivational message! This framework is to be used specifically for clientele management within Google Sheets, which serve as the data loader source for our RAG LLM Chain which summarizes client context to serve as a knowledge base for a Zero Shot Prompted motivation speaker LLM. Sending messages is done by Twilio, from a Docker image hosted on AWS ECR.
Elevate the realm of machine learning with "WebML Assist." This innovative project integrates the power of WebGPU and the capabilities of the "BabyAGI" framework to offer a seamless, high-speed experience in machine learning tasks. "WebML Assist" empowers users to build, train, and deploy AI models effortlessly, leveraging the parallel processing of GPUs for accelerated training. The platform intuitively guides users through data preprocessing, model architecture selection, and hyperparameter tuning, all while harnessing the performance boost of WebGPU. Experience the future of efficient and rapid machine learning with "WebML Assist." Technologies Used: WebGPU OpenAI APIs (GPT-3.5, GPT-4) BabyAGI Pinecone API (for task management) FineTuner.ai (for no-code AI components) Python (for backend) Redis (for data caching) Qdrant (for efficient vector similarity search) Generative Agents (for simulating human behavior). AWS SageMaker (for developing machine learning models quickly and easily build, train, and deploy). Reinforcement learning (is an area of machine learning concerned with how intelligent agents). Categories: Machine Learning AI-Assisted Task Management Benefits: "WebML Assist" brings together the capabilities of WebGPU and AI frameworks like "BabyAGI" to provide an all-encompassing solution for ML enthusiasts. Users can seamlessly transition from data preprocessing to model deployment while harnessing the GPU's power for faster training. The incorporation of AI agents ensures intelligent suggestions and efficient task management. By integrating AI, GPU acceleration, and user-friendly interfaces, "WebML Assist" empowers both novice and experienced ML practitioners to unlock the true potential of their projects, transforming the way AI models are built, trained, and deployed.
HyperTarget - Content Personalization Revolution
HyperTarget, a groundbreaking AI plugin for WordPress, embeds personalized affiliate content directly into blog posts in real time. Tailored to individual reader's interests, it amplifies engagement and revenue like never before. Enter the new era of direct content personalization with HyperTarget! We've pioneered an AI-powered plugin that tailors affiliate content - essentially customized advertisements - based on each unique reader's personal interests. How? By seamlessly integrating these personalized ads into the blog posts they're reading (and in real time!). This innovative approach heightens engagement, boosts click-through rates, and significantly enhances revenue for blog creators. No more generic, intrusive ads; with HyperTarget, every reader gets a unique, interest-based ad experience. Revolutionize your blog's earning potential with the power of AI personalization! But we don't rest on today's achievements; we're building tomorrow's capabilities. HyperTarget continually collects data from every user interaction, fueling our reinforcement learning algorithms. With each click, our system learns, evolves, and fine-tunes its text generation capabilities, ensuring that every new integration is more effective and more personalized than the last.
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A trading agent AI is an artificial intelligence system that uses computational intelligence methods such as machine learning and deep reinforcement learning to automatically discover, implement, and fine-tune strategies for autonomous adaptive automated trading in financial markets This project implements a Stock Trading Bot, trained using Deep Reinforcement Learning, specifically Deep Q-learning. Implementation is kept simple and as close as possible to the algorithm discussed in the paper, for learning purposes. Generally, Reinforcement Learning is a family of machine learning techniques that allow us to create intelligent agents that learn from the environment by interacting with it, as they learn an optimal policy by trial and error. This is especially useful in many real world tasks where supervised learning might not be the best approach due to various reasons like nature of task itself, lack of appropriate labelled data, etc. The important idea here is that this technique can be applied to any real world task that can be described loosely as a Markovian process. This work uses a Model-free Reinforcement Learning technique called Deep Q-Learning (neural variant of Q-Learning). At any given time (episode), an agent abserves it's current state (n-day window stock price representation), selects and performs an action (buy/sell/hold), observes a subsequent state, receives some reward signal (difference in portfolio position) and lastly adjusts it's parameters based on the gradient of the loss computed. There have been several improvements to the Q-learning algorithm over the years, and a few have been implemented in this project: Vanilla DQN DQN with fixed target distribution Double DQN Prioritized Experience Replay Dueling Network Architectures Trained on GOOG 2010-17 stock data, tested on 2019 with a profit of $1141.45 (validated on 2018 with profit of $863.41):