JAX compilation of RDDL description files, and a differentiable planner in JAX.
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Updated
Jul 4, 2024 - Python
JAX compilation of RDDL description files, and a differentiable planner in JAX.
This framework permits to easily create a neural network without coding, and to train it on any data. So, anyone who want to create a neural network but don't know how to code can use it as a first step to see how neural nets work ! However, I don't recommand it it's better to code lol.
Custom implementation of a neural network from scratch using Python
My DATAML300 - Computer vision solutions.
NYCU Deep Learning Spring 2024
Notes & simple python code to aid understanding the workings of neural networks
Multilayer perceptron deep neural network with feedforward and back-propagation for MNIST image classification using NumPy
A kind of 'study' of the XOR problem in neural networks😄
Implementation from scratch (using numpy arrays) of a framework based on keras interface which allows to build and train Fully Connected Networks and Convolutional Neural Networks (CNNs).
Implementation of the "Applying the Forward-Forward Algorithm to the Event-Based Sensing" paper.
Python implementation of a Feed-Forward Backpropagation neural network using only the standard library
A small deep learning framework written from scratch in python. Implements forward and backward propogation by hand. For those who are interested in learning how to do so.
scalar value gradient descent optimizer
Train and test a Multi-layer Perceptron (MLP) neural network.
Retrieval based biomedical chatbot to answer questions related to diseases
A simple way to understand and implement backpropogation
Educational Transformer from scratch (no autograd), with forward and backprop.
yet another scalar autograd engine - featuring complex numbers and fixed DAG
This repository has implementations of various alternatives to backpropagation for training neural networks.
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