Popular GitHub Repositories for Eager

Deep Learning papers reading roadmap for anyone who are eager to learn this amazing tech!

Simple tutorials for building neural networks with TensorFlow Eager mode.

???♂? Library designed for developers eager to explore the potential of Large Language Models (LLMs) and other generative AI through a clean, effective, and Go-idiomatic approach.

TensorFlow Eager implementation of NEAT and Adaptive HyperNEAT

Faster R-CNN R-101-FPN model was implemented with TensorFlow2.0 eager execution.

Simple gradient checkpointing for eager mode execution

Deploy Swin Transformer using TorchServe

Eagerly Experimentable!!!

In this project I implement Neural Machine Translation using Attention mechanism. The code is written using the TensorFlow library in Python. I have used TensorFlow functionalities like tf.data.Dataset to manage the input pipeline, Eager Execution and Model sub classing to create the model architecture.

A minimal implementation of a VAE with BinConcrete (relaxed Bernoulli) latent distribution in TensorFlow.

The "lcel-tutorial" repo is designed for mastering LangChain Expression Language (LCEL), offering exercises to build stateful, multi-actor LLM applications. It's a hands-on guide to leveraging LCEL for complex workflows and agent-like behaviors. Perfect for enthusiasts eager to explore LLM's potential.

Mastery AI Apps Dev Workshop: Jumpstart your journey into AI-powered app development! This hands-on workshop guides you through building real-world applications using cutting-edge AI tools and cloud technologies. Perfect for beginners and enthusiasts eager to turn ideas into powerful solutions. Join us and master the future of app development!

Keras, Tensorflow eager execution implementation of Categorical Variational Autoencoder

Implementation of the paper Learning to reinforcement learn(https://arxiv.org/abs/1611.05763)

Classical deep learning paper implementation using tensorflow's eager execution

A proposal for distribution-based loss function in neural network

This repository contains a notebook for object detection with the help of fine tuning of a (TF2 friendly) RetinaNet architecture on very few examples of a novel class after initializing from a pre-trained COCO checkpoint. Training runs in eager mode.

This workshop is tailored for university graduates eager to explore the synergy between artificial intelligence, deep learning, and robotics using NVIDIA Jetson devices and the robust Metropolis SDK.

The goal of the project is to benchmark and optimize BERT inference using different backends—PyTorch eager mode, TorchDynamo (Inductor backend), and NVIDIA Triton Inference Server. We use GLUE SST-2 samples for evaluation and compare performance through profiling, kernel timing, and latency analysis.

This repository contains Various Techniques that can be used for object detection.