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2d-and-3d-face-alignment

This repository implements a demo of the networks described in "How far are we from solving the 2D & 3D Face Alignment problem? (and a dataset of 230,000 3D facial landmarks)" paper.

caffe

Caffe: a fast open framework for deep learning.

cnn-facial-landmark

Facial landmark detection based on deep convolutional neural network.

cnn-for-face-anti-spoofing

python and Tensorflow implementation of a CNN for face anti-spoofing

cnn-for-image-retrieval

:sunrise:The code of post "Image retrieval using MatconvNet and pre-trained imageNet"

cnn-master

CNN based ensemble classifiers trained on multiple data sets with different color spaces

cs-notes

😋 技术面试必备基础知识

deep-head-pose

:fire::fire: Deep Learning Head Pose Estimation using PyTorch.

deeplearning-500-questions

深度学习500问，以问答形式对常用的概率知识、线性代数、机器学习、深度学习、计算机视觉等热点问题进行阐述，以帮助自己及有需要的读者。 全书分为18个章节，近30万字。由于水平有限，书中不妥之处恳请广大读者批评指正。 未完待续............ 如有意合作，联系[email protected] 版权所有，违权必究 Tan 2018.06

enas-pytorch

PyTorch implementation of "Efficient Neural Architecture Search via Parameters Sharing"

examples

A set of examples around pytorch in Vision, Text, Reinforcement Learning, etc.

fast-slow-lstm

Fast-Slow Recurrent Neural Networks

illuminationsingleimage

MATLAB implementation of our llumination estimation technique from a single image (ICCV'09 and IJCV'12 papers)

image-classification

texture image classification with python

image-outpainting

Keras Implementation of Painting outside the box

lasagne-draw

Implementation of the DRAW network in lasagne

lbj

new record

machine-learning-a-case-study-approach

In this course, you will get hands-on experience with machine learning from a series of practical case-studies. At the end of the first course you will have studied how to predict house prices based on house-level features, analyze sentiment from user reviews, retrieve documents of interest, recommend products, and search for images. Through hands-on practice with these use cases, you will be able to apply machine learning methods in a wide range of domains. This first course treats the machine learning method as a black box. Using this abstraction, you will focus on understanding tasks of interest, matching these tasks to machine learning tools, and assessing the quality of the output. In subsequent courses, you will delve into the components of this black box by examining models and algorithms. Together, these pieces form the machine learning pipeline, which you will use in developing intelligent applications. Learning Outcomes: By the end of this course, you will be able to: -Identify potential applications of machine learning in practice. -Describe the core differences in analyses enabled by regression, classification, and clustering. -Select the appropriate machine learning task for a potential application. -Apply regression, classification, clustering, retrieval, recommender systems, and deep learning. -Represent your data as features to serve as input to machine learning models. -Assess the model quality in terms of relevant error metrics for each task. -Utilize a dataset to fit a model to analyze new data. -Build an end-to-end application that uses machine learning at its core. -Implement these techniques in Python.

mask_rcnn

Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow

mybian

mystars

neural-style

Torch implementation of neural style algorithm

oop

一个新项目

pic2char

将微信好友头像拼接成汉字；拼图成字

plotneuralnet

Latex code for making neural networks diagrams

pysyft

A library for encrypted, privacy preserving machine learning

talking-face-generation-davs

Code for Talking Face Generation by Adversarially Disentangled Audio-Visual Representation (AAAI 2019)

test

train_cnn-rnn-attention

cnn+rnn+attention: vgg(vgg16,vgg19)+rnn(LSTM, GRU)+attention, resnet(resnet_v2_50,resnet_v2_101,resnet_v2_152)+rnnrnn(LSTM, GRU)+attention, inception_v4+rnn(LSTM, GRU)+attention, inception_resnet_v2+rnn(LSTM, GRU)+attention,.....

video-classification

Tutorial for video classification/ action recognition using 3D CNN/ CNN+RNN on UCF101