CNN

Basic_CNN_Implementation

Basic_CNN_Implementation

Hyper-Flexible Convolutional Neural Networks Based on Generalized Lehmer and Power Means

Vectorized CNN implementation from scratch using only numpy

Test for the speed to do maxpooling in pure go , goroutine, and Cgo.

No description provided.

This Project is based on Neural Network to classify between Dogs and Cats.

This project aims to develop an advanced DL model using CNN to accurately detect and classify brain tumors from MRI scans.

Lab6 of AI computing Architecture and System (2024 spring) around riscv emulator and implementation of fibonacci, sudoku (2x2) and maxpool in RISC-V

Image classification using neural networks. Completed for school. Part 1 is a classical 80s style shallow network, and part 2 is a more modern network. For part 2, I added activation functions, implemented L2 Regularization, changed network depth and width, and used Convolutional Neural Nets to improve performance. Check README

A simple study on the use of CNNs for a simple handwritten number image classification task using the Keras framework (with Tensorflow background).

A minimal NumPy-based implementation of a 3-layer convolutional neural network (CNN) from scratch — including custom forward and backward passes for conv, ReLU, pooling, affine, and softmax layers. Perfect for learning how CNNs actually work under the hood.

CNN with transfer learning for animal image detection

Lip reading using TensorFlow, OpenCV, and Keras involves training a deep learning model to recognize spoken words by analyzing lip movements from video frames. The process starts with OpenCV for capturing and preprocessing video frames, focusing on the speaker’s lips. These frames are then fed into a neural network built using Keras and TensorFlow.

Neural networks

Deep Learning-Driven Leaf Photo Analysis for Plant Disease Prediction is an innovative deep learning project that harnesses the power of Convolutional Neural Networks (CNNs) to revolutionize plant disease prediction.