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Machine Learning For Trading: Code for Machine Learning for Trading, 3rd edition — from data sourcing to live execution. It is listed under Development & Code on AI Registry as a free tool.
The tools below are approved listings in the same category, ordered by how closely their tags match Machine Learning For Trading. This is an automatic list. We have not written a researched verdict for Machine Learning For Trading yet, so treat it as a shortlist and check each vendor's pricing page before switching.
Built from each tool's listing. Vendors can update their own listing after claiming it.
CAMEL: The first and the best multi-agent framework.
CAMEL is an open source, modular framework for building multi agent AI systems in Python. It supplies the primitives for creating agents that reason, plan and call tools, composing them into societies with defined roles, and running them against code interpreters, memory stores and retrieval pipelines. The project desc...
dashAI: an interactive platform for training, evaluating and deploying AI models
DashAI is an open source desktop application for data analysis and machine learning model training without writing code. It runs entirely on the user's own machine and needs no cloud services, external authentication or API keys for its core functions. Within one visual interface users import datasets, explore data, tr...
An open source library for deep learning end-to-end dialog systems and chatbots.
DeepPavlov is an open source conversational AI framework for building chatbots, virtual assistants and complex dialogue systems, and for research in natural language processing. The core DeepPavlov Library provides pretrained deep learning models, including BERT based models, for tasks such as text classification, name...
Flower: A Friendly Federated AI Framework
Flower is an open source framework for federated AI, in which models are trained across data that stays where it already lives rather than being pooled in one place. The project supplies the libraries and infrastructure to define a training strategy, connect participating nodes and run federated training and fine tunin...
GPU Profiler for AI agents
Graphsignal is a GPU profiler for inference workloads, built so that an AI agent can tune performance as well as a human engineer. It measures where time actually goes during generation and serves the results in a form a coding agent can read, act on by changing a flag or a line of code, and then measure again. It is ...
Happy Transformer makes it easy to fine-tune and perform inference with NLP Transformer models.
Happy Transformer is a Python package that makes it easier to fine tune and run inference with NLP transformer models. It wraps common model families behind a small, consistent interface so developers can try a model or train it on their own data without writing the surrounding boilerplate. The documentation lists the...