带你从零实现一个高性能的深度学习推理库，支持大模型 llama2 、Unet、Yolov5、Resnet等模型的推理。Implement a high-performance deep learning inference library step by step

Compute Sentence Embeddings Fast!

An algorithm that facilitates communication between a speech-impaired person and someone who doesn't understand sign language using convolution neural networks

**DeepLearning** (CNN, RNN) + Bayesian Neural Network

Kaggle Machine Learning Competition Project : In this project, we will create a classifier to classify fashion clothing into 10 categories learned from Fashion MNIST dataset of Zalando's article images

A Deep Learning framework for CNNs and LSTMs from scratch, using NumPy.

Hi! Thanks for checking out my tutorial where I walk you through the process of coding a convolutional neural network in java from scratch. After building a network for a university assignment, I decided to create a tutorial to (hopefully) help others do the same and improve my own understanding of neural networks.

Python implementation of the neural networks without using any libraries from scratch, for prediction using the pre-trained weights

Extensive Vision AI Program from The School Of AI

implementation of neural network from scratch only using numpy (Conv, Fc, Maxpool, optimizers and activation functions)

Given an image of a dog, our algorithm will identify an estimate of the canine’s breed. If supplied an image of a human, the code will identify the resembling dog breed.

This repository consists of models of CNN for classifying different types of charts. Moreover, it also includes script of fine-tuned VGG16 for this task. On top of that CradCAM implementation of fine-tuned VGG16.

This is a hybrid variety of detection models which is inspired from bothe centrenet and EfficientDet. This model is as fast as centrenet and much accurate due to the fusion blocks.

Face detection using convolutional neural networks

This model helps us classify 10 different real-life objects by undergoing training under tensorflow's CIFAR dataset which contains 60,000 32x32 color images with 6000 images of each class. I have made use of a stack of Conv2D and MaxPooling2D layers followed by a few densely connected layers.

Using convolutional neural networks to build and train a bird species classifier on bird pics data with corresponding species labels, also build GUI for the same.