Build your neural network easy and fast, 莫烦Python中文教学
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Updated
Mar 23, 2023 - Jupyter Notebook
Build your neural network easy and fast, 莫烦Python中文教学
Deep Learning Specialization courses by Andrew Ng, deeplearning.ai
ImageNet pre-trained models with batch normalization for the Caffe framework
MNIST classification using Convolutional NeuralNetwork. Various techniques such as data augmentation, dropout, batchnormalization, etc are implemented.
Educational deep learning library in plain Numpy.
My workshop on machine learning using python language to implement different algorithms
Adaptive Affinity Fields for Semantic Segmentation
The first machine learning framework that encourages learning ML concepts instead of memorizing class functions.
TensorFlow implementation of real-time style transfer using feed-forward generation. This builds on the original style-transfer algorithm and allows for common personal computers to transform images.
Batch normalization fusion for PyTorch
Interesting python codes to tackle simple machine/deep learning tasks
Synchronized Multi-GPU Batch Normalization
MXNet Code For Demystifying Neural Style Transfer (IJCAI 2017)
Single (i) Cell R package (iCellR) is an interactive R package to work with high-throughput single cell sequencing technologies (i.e scRNA-seq, scVDJ-seq, scATAC-seq, CITE-Seq and Spatial Transcriptomics (ST)).
Using slim to perform batch normalization
An image recognition/object detection model that detects handwritten digits and simple math operators. The output of the predicted objects (numbers & math operators) is then evaluated and solved.
Short description for quick search
MNIST classification using Multi-Layer Perceptron (MLP) with 2 hidden layers. Some weight-initializers and batch-normalization are implemented.
Implementing an Image classification neural network to classify Street House View Numbers
This is a fork of caffe added some useful layers, the original caffe site is https://github.com/BVLC/caffe.
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