Lightweight Python package for automatic differentiation
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Updated
May 19, 2024 - C++
Lightweight Python package for automatic differentiation
A modular C++17 framework for automatic differentiation
TensorFlow Eager implementation of NEAT and Adaptive HyperNEAT
PyTorch, TensorFlow, JAX and NumPy — all of them natively using the same code
Example code for using Tensorflow 2.0 with both numerical and categorical data
Get started with Tensorflow/Keras API.
This repository contains Various Techniques that can be used for object detection.
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.
TensorFlow implementation of Deformable Convolutional Layer
Eagerly Experimentable!!!
Header only lazy evaluation tensor math library with multi-backend parallel eager execution support (TBB, OpenMP, Parallel STL and in the future CUDA and OpenCL)
Just another YOLO V2 implementation. Train your own dataset in a jupyter notebook!
Implementing Style Transfer in Tensorflow 2.0, using the VGG19 network architecture, which composes the content image in the style of the reference picture, both input by the user.
Tensorflow implementation of Ordinary Differential Equation Solvers with full GPU support
Tensor utilities, reinforcement learning, and more!
Faster R-CNN R-101-FPN model was implemented with TensorFlow2.0 eager execution.
Major anomaly detection methods using neural networks are implemented in this repository 🔥
نقل النمط العصبوني: بناء طريقة للتعلم العميق باستخدام كيراس tf.keras و منفّذ إيجر eager execution
A Tensorflow Keras implementation (Graph and eager execution) of Mnasnet: MnasNet: Platform-Aware Neural Architecture Search for Mobile.
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