Deep feature flow for video recognition
#Dl #Book
@Machine_learn

discriminative :
1:#Regression
2:#Logistic regression
3:#decision tree(Hunt)
4:#neural network(traditional network, deep network)
5:#Support Vector Machine(SVM)
Generative:
1:#Hidden Markov model
2:#Naive bayes
3:#K-nearest neighbor(KNN)
4:#Generative adversarial networks(GANs)
Deep learning:
1:CNN
R_CNN
Fast-RCNN
Mask-RCNN
2:RNN
3:LSTM
4:CapsuleNet
5:Siamese:
siamese cnn
siamese lstm
siamese bi-lstm
siamese CapsuleNet
6:time series data
SVR
DT(cart)
Random Forest linear
Bagging
Boosting

جهت درخواست و راهنمایی در رابطه با پیاده سازی مقالات و پایان نامه ها در رابطه با مباحث deep learning و machine learning با ایدی زیر در ارتباط باشید
@Raminmousa

Hamiltonian Neural Networks

https://eng.uber.com/research/hamiltonian-neural-networks/

paper: https://arxiv.org/pdf/1906.01563.pdf

code: https://github.com/greydanus/hamiltonian-nn

🔥OpenAI realesed the 1.5billion parameter GPT-2 model

Post: https://openai.com/blog/gpt-2-1-5b-release/
GPT-2 output detection model: https://github.com/openai/gpt-2-output-dataset/tree/master/detector
Research from partners on potential malicious uses: https://d4mucfpksywv.cloudfront.net/papers/GPT_2_Report.pdf

#NLU #GPT2 #OpenAI #NLP

HoloGAN (A new generative model) learns 3D representation from natural images

Article: https://arxiv.org/pdf/1904.01326.pdf

Code: https://github.com/thunguyenphuoc/HoloGAN

Dataset: http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html

Applied Deep Learning — Umberto Michelucci (en) 2018

@Machine_learn

Stacked Capsule Autoencoders

https://github.com/google-research/google-research/tree/master/stacked_capsule_autoencoders

paper : https://arxiv.org/abs/1906.06818

http://akosiorek.github.io/ml/2019/06/23/stacked_capsule_autoencoders.html

This AI Learned To Animate Humanoids 🚶

https://www.youtube.com/watch?v=cTqVhcrilrE

code: https://github.com/sebastianstarke/AI4Animation

Check out Lambda here and sign up for their GPU Cloud : https://lambdalabs.com/papers

👌Finding label errors in datasets and learning with noisy labels.

https://github.com/cgnorthcutt/cleanlab/

New book 🔥DEEP LEARNING WITH PYTORCH 2019 #DL #Python
#Book #CNN #RNN

@Machine_learn

GNNExplainer: Generating Explanations for Graph Neural Networks

https://arxiv.org/abs/1903.03894

Github : https://github.com/RexYing/gnn-model-explainer/

Practical Machine Learning with Python
#ML #Python
@Machine_learn

Machine learning for ios
#apple #ios #book
@Machine_learn

Connections between Support Vector Machines, Wasserstein distance and gradient-penalty GANs


https://arxiv.org/abs/1910.06922

SIte : https://ajolicoeur.wordpress.com/

Github : https://github.com/AlexiaJM/MaximumMarginGANs

A collection of anomaly detection methods #Code #Python #Anomaly_detection @Machine_learn

T5: Text-To-Text Transfer Transformer

Github: https://github.com/google-research/text-to-text-transfer-transformer

Paper: https://arxiv.org/abs/1910.10683

@Machine_learn

@Machine_learn
A new open benchmark for speech recognition with limited or no supervision

https://ai.facebook.com/blog/a-new-open-benchmark-for-speech-recognition-with-limited-or-no-supervision/

Code and dataset: https://ai.facebook.com/tools/libri-light

Full paper: https://arxiv.org/abs/1912.07875

discriminative :
1:#Regression
2:#Logistic regression
3:#decision tree(Hunt)
4:#neural network(traditional network, deep network)
5:#Support Vector Machine(SVM)
Generative:
1:#Hidden Markov model
2:#Naive bayes
3:#K-nearest neighbor(KNN)
4:#Generative adversarial networks(GANs)
Deep learning:
1:CNN
R_CNN
Fast-RCNN
Mask-RCNN
2:RNN
3:LSTM
4:CapsuleNet
5:Siamese:
siamese cnn
siamese lstm
siamese bi-lstm
siamese CapsuleNet
6:time series data
SVR
DT(cart)
Random Forest linear
Bagging
Boosting

جهت درخواست و راهنمایی در رابطه با پیاده سازی مقالات و پایان نامه ها در رابطه با مباحث deep learning و machine learning با ایدی زیر در ارتباط باشید
@Raminmousa

# Histogram-based Outlier Score (HBOS): A fastUnsupervised Anomaly Detection Algorithm #Paper #HBOS #Anomaly_Detection @Machine_learn