Grayson-Jockey Club Research Foundation, Inc.
Funding equine research to let horses of all breeds live healthier lives
Napoleon Solo Continues Preparations, Next Target Still a Question Mark
Trainer Chad Summers still hasn't made a decision on which Kentucky Derby prep race his Grade I winner Napoleon Solo will return in.
Thoroughbred OwnerView – Thoroughbred Owners, Thoroughbred Trainers, Thoroughbred Partners…
Grayson-Jockey Club Research Foundation, Inc.
Funding equine research to let horses of all breeds live healthier lives
Equibase | Horse Racing | Horse Racing Entries | Horse Racing Results | Past Performances…
Welcome to Equibase.com, your official source for horse racing results, mobile racing data, statistics as well as all other horse racing and thoroughbred racing information. Find everything you need to know about horse racing at Equibase.com.
gorilla/goex at main · ShishirPatil/gorilla · GitHub
Gorilla: Training and Evaluating LLMs for Function Calls (Tool Calls) - ShishirPatil/gorilla
gorilla/goex/exec_engine/pipeline.py at 34ae76099a21aaf01df434ebca33f83489b55096 · Shishir…
Gorilla: Training and Evaluating LLMs for Function Calls (Tool Calls) - ShishirPatil/gorilla
gorilla/goex/exec_engine/pipeline.py at 34ae76099a21aaf01df434ebca33f83489b55096 · Shishir…
Gorilla: Training and Evaluating LLMs for Function Calls (Tool Calls) - ShishirPatil/gorilla
gorilla/goex/cli.py at 34ae76099a21aaf01df434ebca33f83489b55096 · ShishirPatil/gorilla · G…
Gorilla: Training and Evaluating LLMs for Function Calls (Tool Calls) - ShishirPatil/gorilla
Introduction to large language models - Training | Microsoft Learn
Learn about large language models, their core concepts, the models that are available to use, and when to use them.
gorilla/goex/exec_engine/api_executor.py at 34ae76099a21aaf01df434ebca33f83489b55096 · Shi…
Gorilla: Training and Evaluating LLMs for Function Calls (Tool Calls) - ShishirPatil/gorilla
gorilla/raft at main · ShishirPatil/gorilla · GitHub
Gorilla: Training and Evaluating LLMs for Function Calls (Tool Calls) - ShishirPatil/gorilla
gorilla/raft at main · ShishirPatil/gorilla · GitHub
Gorilla: Training and Evaluating LLMs for Function Calls (Tool Calls) - ShishirPatil/gorilla
[2403.10131] RAFT: Adapting Language Model to Domain Specific RAG
Pretraining Large Language Models (LLMs) on large corpora of textual data is now a standard paradigm. When using these LLMs for many downstream applications, it is common to additionally bake in new knowledge (e.g., time-critical news, or private domain knowledge) into the pretra…
Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four…
Most current speech recognition systems use hidden Markov models (HMMs) to deal with the temporal variability of speech and Gaussian mixture models (GMMs) to determine how well each state of each HMM fits a frame or a short window of frames of coefficients that represents the aco…
Multi-column deep neural networks for image classification | IEEE Conference Publication |…
Traditional methods of computer vision and machine learning cannot match human performance on tasks such as the recognition of handwritten digits or traffic signs. Our biologically plausible, wide and deep artificial neural network architectures can. Small (often minimal) recepti…
Llemma: An Open Language Model For Mathematics | EleutherAI Blog
ArXiv | Models | Data | Code | Blog | Sample Explorer Today we release Llemma: 7 billion and 34 billion parameter language models for mathematics. The Llemma models were initialized with Code Llama weights, then trained on the Proof-Pile II, a 55 billion token dataset of mathemat…
Generative AI and large language models in health care: pathways to implementation
Generative AI is designed to create new content from trained parameters. Learning from large amounts of data, many of these models aim to simulate human conversation. Generative AI is being applied to many different sectors. Within healthcare there has been innovation specificall…
Optimization Algorithms in Neural Networks - KDnuggets
This article presents an overview of some of the most used optimizers while training a neural network.
Outracing champion Gran Turismo drivers with deep reinforcement learning | Nature
Using the game Gran Turismo, an agent was trained with a combination of deep reinforcement learning algorithms and specialized training scenarios, demonstrating success against championship-level human racers.