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GPT4All is an open-source ecosystem of on-edge large language models that run locally on consumer-grade CPUs. It offers a powerful and customizable AI assistant for a variety of tasks, including answering questions, writing content, understanding documents, and generating code.

GPT4All is supported and maintained by Nomic AI, which aims to make it easier for individuals and enterprises to train and deploy their own large language models on the edge.

Release date2023
AuthorNomic AI
TypeNatural Language Processing

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To start building with GPT4All, visit the GPT4All website and follow the installation instructions for your operating system.

GPT4All Libraries

A curated list of libraries to help you build great projects with GPT4All.

GPT4All Examples

For more information on GPT4All, including installation instructions, technical reports, and contribution guidelines, visit the GPT4All GitHub repository.

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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):