@Machine_learn
The HSIC Bottleneck: Deep Learning without Back-Propagation🥺

An alternative to conventional backpropagation, that has a number of distinct advantages.

Link: https://arxiv.org/abs/1908.01580

#backpropagation #DL

Learning_Tenserflow_building_deep
#book
@Machine_learn

Unsupervised learning with python,2019
#book
@Machine_learn

@Machine_learn
Rank-consistent Ordinal Regression for Neural Networks

Article: https://arxiv.org/abs/1901.07884

PyTorch: https://github.com/Raschka-research-group/coral-cnn

@Machine_leaen
ai ,machine learning
#code #datasets #paper
• 1146 leaderboards
• 1223 tasks
• 1105 datasets
• 14779 papers with code


https://paperswithcode.com/sota

@Machine_learn
Memory-Efficient Adaptive Optimization

Source: https://arxiv.org/abs/1901.11150

Code: https://github.com/google-research/google-research/tree/master/sm3

@Machine_learn
🚀 Introducing TF-GAN: A lightweight GAN library for TensorFlow 2.0

Tensorflow blog: https://medium.com/tensorflow/introducing-tf-gan-a-lightweight-gan-library-for-tensorflow-2-0-36d767e1abae

Code: https://github.com/tensorflow/gan

Free course: https://developers.google.com/machine-learning/gan/

Paper: https://arxiv.org/abs/1805.08318

@Machine_learn
DeepMind's OpenSpiel is a collection of environments and algorithms for research in general reinforcement learning and search/planning in games.

code: https://github.com/deepmind/open_spiel

article: https://arxiv.org/abs/1908.09453

Deep Learning with Python
The ultimate beginners guide to Learn Deep Learning with Python Step by Step
#book #DL #python
@Machine_learn

Deep Learning with Python
The ultimate beginners guide to Learn Deep Learning with Python Step by Step
#book #DL #python
@Machine_learn

@Machine_learn
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

How normalization applied to layers helps to reach faster convergence.

ArXiV: https://arxiv.org/abs/1502.03167

#NeuralNetwork #nn #normalization #DL

@Machine_learn
The largest publicly available language model: CTRL has 1.6B parameters and can be guided by control codes for style, content, and task-specific behavior.


code: https://github.com/salesforce/ctrl

article: https://einstein.ai/presentations/ctrl.pdf
C-write:ai_machinelearning_big_data
https://blog.einstein.ai/introducing-a-conditional-transformer-language-model-for-controllable-generation/

@Machine_learn
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

How normalization applied to layers helps to reach faster convergence.

ArXiV: https://arxiv.org/abs/1502.03167

#NeuralNetwork #nn #normalization #DL

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

#Deep Set Prediction Networks
#paper #DL
@Machine_learn

#Deep Set Prediction Networks
#paper #DL
@Machine_learn

#Deep Set Prediction Networks
#paper #DL
@Machine_learn

A Survey on semi-supervised feature selection methods
#paper #survey
@Machine_learn