Machine learning books and papers

#Deep Set Prediction Networks #paper #DL @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

@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

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

@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

@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 Memory-Efficient Adaptive Optimization Source: https://arxiv.org/abs/1901.11150 Code: https://github.com/google-research/google-research/tree/master/sm3

@Machine_leaen ai ,machine learning #code #datasets #paper • 1146 leaderboards • 1223 tasks • 1105 datasets • 14779 papers with code https://paperswithcode.com/sota

@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_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

@Machine_learn How to Implement Progressive Growing GAN Models in Keras https://machinelearningmastery.com/how-to-implement-progressive-growing-gan-models-in-keras/

@Machine_learn Interpreting Latent Space of GANs for Semantic Face Editing https://shenyujun.github.io/InterFaceGAN/ code: https://github.com/ShenYujun/InterFaceGAN.git