https://sadanand-singh.github.io/posts/treebasedmodels/

https://arxiv.org/pdf/1707.04035.pdf

https://github.com/titu1994/Snapshot-Ensembles - Snapshot Ensembles

https://arxiv.org/pdf/1609.04747.pdf - An overview of gradient descent optimization algorithms∗

Semantic Segmentation using Fully Convolutional Networks over the years https://meetshah1995.github.io/semantic-segmentation/deep-learning/pytorch/visdom/2017/06/01/semantic-segmentation-over-the-years.html

A 2017 Guide to Semantic Segmentation with Deep Learning http://blog.qure.ai/notes/semantic-segmentation-deep-learning-review

https://arxiv.org/pdf/1707.02968.pdf - Revisiting Unreasonable Effectiveness of Data in Deep Learning Era

https://drive.google.com/file/d/0B2A1tnmq5zQdQU1YRFpwWFNISDQ/view - Deep Learning in the Brain

https://spandan-madan.github.io/DeepLearningProject/ - And to end implementation of ML Pipeline

https://docs.google.com/presentation/d/1aeMBpICriLOQQerbthpKvbB2TOk8Vqy901xm__NVr-o/edit#slide=id.p - Optimization

http://ruder.io/optimizing-gradient-descent/ - Overview of Gradient Descent Algorithms

https://github.com/csxeba/brainforge - ANN library in Python/Numpy

https://arxiv.org/pdf/1706.04313v1.pdf - Teaching Compositionality to CNNs∗

https://ml.berkeley.edu/blog/2017/07/13/tutorial-4/ - Bias Variance Dilemma

https://github.com/idiap/importance-sampling Importance Sampling

https://arxiv.org/pdf/1708.02002.pdf Focal Loss For Dense Object Detection

https://www.youtube.com/playlist?list=PL3FW7Lu3i5JvHM8ljYj-zLfQRF3EO8sYv Convolutional Neural Networks for Visual Recognition (Spring 2017)

https://arxiv.org/pdf/1709.01507.pdf (http://image-net.org/challenges/talks_2017/SENet.pdf) - Squeeze-and-Excitation Networks

https://arxiv.org/abs/1709.01894 - Convolutional Gaussian Processes

https://arxiv.org/pdf/1709.01412.pdf - Deep Learning Technical Introduction

http://theorangeduck.com/page/neural-network-not-working - My Neural Network isn't working! What should I do?

https://www.youtube.com/watch?v=bLqJHjXihK8 - 18. Information Theory of Deep Learning. Naftali Tishby

https://sigmoidal.io/beginners-review-of-gan-architectures/ - 2017 Beginner's Review of GAN Architectures

https://arxiv.org/abs/1710.05941 - Swift activation function

https://github.com/ilkarman/DeepLearningFrameworks/#rnn-gru-on-imdb---natural-language-processing-sentiment-analysis - RNNs/CNNs implemented in different frameworks

https://arxiv.org/pdf/1710.05468.pdf - Generalization in Deep Learning (Bengio)

https://arxiv.org/abs/1710.04773 - Residual Connections Encourage Iterative Inference

https://arxiv.org/abs/1710.06451 - Understanding Generalization and Stochastic Gradient Descent

https://blog.statsbot.co/real-time-object-detection-yolo-cd348527b9b7 - Improving YOLO

http://www.mitpressjournals.org/doi/pdf/10.1162/neco_a_00990 - Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review

https://arxiv.org/pdf/1710.05381.pdf - A systematic study of the class imbalance problem in convolutional neural networks

https://www.youtube.com/playlist?list=PLJOzdkh8T5kqCNV_v1w2tapvtJDZYiohW - Deep Learning: Theory, Algorithms, Application. Video Series.

https://github.com/RedditSota/state-of-the-art-result-for-machine-learning-problems - SOTA resources

http://blog.christianperone.com/2017/11/the-effective-receptive-field-on-cnns/

https://joshgreaves.com/reinforcement-learning/introduction-to-reinforcement-learning/

https://github.com/jadore801120/attention-is-all-you-need-pytorch - Attention is all you need

https://hackernoon.com/facial-similarity-with-siamese-networks-in-pytorch-9642aa9db2f7 - Siamese Networks

https://medium.com/huggingface/understanding-emotions-from-keras-to-pytorch-3ccb61d5a983

https://drive.google.com/drive/folders/0B41Zbb4c8HVyUndGdGdJSXd5d3M - PyTorch Mini Lectures (12 Slides)

https://github.com/vahidk/EffectiveTensorflow Effective Tensorflow (Tutorials)

https://github.com/lmcinnes/umap - Uniform Manifold Approximation and Projection (dim reduction better than T-SNE?)

http://www.pnas.org/content/early/2017/08/28/1700770114.full.pdf - Robust continuous clustering

https://myurasov.github.io/2017/09/24/wasserstein-gan-keras.html?r - Wasserstein GAN in Keras

https://github.com/XifengGuo/CapsNet-Keras - CapsNet

https://medium.com/searchink-eng/keras-horovod-distributed-deep-learning-on-steroids-94666e16673d - Keras + Horovod (distributed learning)

https://arxiv.org/pdf/1710.09829.pdf Paper

https://www.youtube.com/watch?v=pPN8d0E3900 - Very good video

https://jhui.github.io/2017/11/03/Dynamic-Routing-Between-Capsules/

https://medium.com/mlreview/deep-neural-network-capsules-137be2877d44

https://medium.com/@mike_ross/a-visual-representation-of-capsule-network-computations-83767d79e737

http://www.sumsar.net/blog/2017/05/introduction-to-bayesian-data-analysis-part-three/ - Introduction to Bayesian Data Analysis [Video]

http://www.cs.ubc.ca/~nando/papers/thesis.pdf - Bayesian Methods for Neural Networks

http://www.stats.ox.ac.uk/~doucet/doucet_defreitas_gordon_smcbookintro.pdf - An introduction to Sequential Monte Carlo Methods

https://ermongroup.github.io/cs228-notes/ introductory course on probabilistic graphical models (Stanford, 3 notes)

https://blog.statsbot.co/bayesian-nonparametrics-9f2ce7074b97 - Bayesian Non-Parametrics

https://drive.google.com/file/d/0B653sCwrWAVNOE1ZTFBJUlRybGc/view - ???

https://arxiv.org/pdf/1710.07406.pdf - First Order Methods Almost Always Avoid Saddle Points

https://psyarxiv.com/85ywt - Introduction to the concept of likelihood and its applications

https://github.com/biancasubion/jupyshare - Jupyter Notebooks in the Cloud

http://gouthamanbalaraman.com/blog/optimizing-python-numba-vs-cython.html - Python vs. Cython vs. Numba

https://www.reddit.com/r/MachineLearning/comments/6x6tln/p_artemis_a_python_package_for_organizing_your_ml/?st=j7bkxmf8&sh=3792e9b2 - Artemis, Python package for ML experiments

https://github.com/ilkarman/DeepLearningFrameworks/ - Neural Network in 7 different Frameworks

http://intermediatepythonista.com/

https://github.com/explosion/lightnet - Python Interface for Yolo

https://research.googleblog.com/2017/12/tfgan-lightweight-library-for.html

http://learningsys.org/nips17/assets/slides/dean-nips17.pdf - Jeff Dean

https://arxiv.org/pdf/1710.09412.pdf Mixup Paper (reduce memorization and adversarial errors)

https://github.com/leehomyc/mixup_pytorch Mixup PyTorch

https://buzzrobot.com/using-t-sne-to-visualise-how-your-deep-model-thinks-4ba6da0c63a0 T-SNE to visualize how DNN thinks

http://parrt.cs.usfca.edu/doc/matrix-calculus/index.html - The Matrix Calculus You Need For Deep Learning

https://www.pyimagesearch.com/2018/01/29/scalable-keras-deep-learning-rest-api/ - Scalable Keras Deep Learning Rest API

https://ee227c.github.io/code/lecture4.html - Notes on Optimization (full lecture https://ee227c.github.io/)

https://medium.com/applied-data-science/how-to-build-your-own-alphazero-ai-using-python-and-keras-7f664945c188 - How to build your own AlphaZero AI using Python and Keras

https://github.com/Microsoft/AutonomousDrivingCookbook/tree/master/AirSimE2EDeepLearning - Autonomous Driving in Keras

https://blog.insightdatascience.com/how-to-solve-90-of-nlp-problems-a-step-by-step-guide-fda605278e4e - Solving 90% of NLP problems, Step-by-Step

https://www.kaggle.com/learn/overview - Kaggle Learning Platform

http://www.inference.vc/sharp-vs-flat-minima-are-still-a-mystery-to-me/ - Generalization Mystery, Sharp vs Flat Minima

https://github.com/emilwallner/Screenshot-to-code-in-Keras - Turning Website Mockup into Code

https://chrischoy.github.io/research/Matric-Calculus/ - Short Note on Matrix Differentials and Backpropagation

https://github.com/jwyang/faster-rcnn.pytorch - PyTorch Faster-RCNN

https://www.youtube.com/playlist?list=PLbN57C5Zdl6j_qJA-pARJnKsmROzPnO9V - Nonlinear Dynamics & Chaos Lectures

https://github.com/uber/horovod/blob/master/examples/keras_imagenet_resnet50.py - Resnet50 training in Keras + Horovod

https://github.com/vincentherrmann/pytorch-wavenet/blob/master/WaveNet_demo.ipynb - PyTorch Wavenet Demo

https://www.zabaras.com/statisticalcomputing - Statistical Computing for Scientists and Engineers

https://medium.com/@hamedmp/exporting-trained-tensorflow-models-to-c-the-right-way-cf24b609d183 - Exporting TF model to C++

https://docs.google.com/presentation/d/16kHNtQslt-yuJ3w8GIx-eEH6t_AvFeQOchqGRFpAD7U/edit#slide=id.p - Tensorflow tf.data Slides

https://arxiv.org/abs/1712.07628 - Improving Generalization Performance by Switching from Adam to SGD

https://arxiv.org/pdf/1705.09914.pdf

https://arxiv.org/pdf/1711.10284.pdf - Between-class Learning for Image classification

http://davischallenge.org/challenge2017/papers/DAVIS-Challenge-6th-Team.pdf - Learning to Segment Instances in Videos with Spatial Propagation Network

http://www.andrewjanowczyk.com/efficient-pixel-wise-deep-learning-on-large-images/

https://www.arxiv-vanity.com/

https://github.com/divamgupta/image-segmentation-keras

https://arxiv.org/pdf/1703.02628.pdf - Global Optimization of Lipschitz Functions

https://christophm.github.io/interpretable-ml-book/

http://bayesiandeeplearning.org/

https://www.arxiv-vanity.com/papers/1712.04741/ - Mathematics of Deep Learning

http://www.robots.ox.ac.uk/~vgg/decathlon/

https://buzzrobot.com/using-t-sne-to-visualise-how-your-deep-model-thinks-4ba6da0c63a0

http://bridg.land/posts/gaussian-processes-1

https://arxiv.org/abs/1712.07628 - https://arxiv.org/abs/1712.07628

https://lilianweng.github.io/lil-log/2017/08/20/from-GAN-to-WGAN.html - From GAN to WGAN

https://github.com/arogozhnikov/python3_with_pleasure

http://www.fast.ai/2018/01/26/v2-launch/

https://www.kaggle.com/learn/overview

http://camdavidsonpilon.github.io/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/ - Bayesian Methods for Hackers

http://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning/understanding-machine-learning-theory-algorithms.pdf

https://arxiv.org/abs/1707.06175 - DPM Conv Nets

http://noahgolmant.com/avoiding-saddle-points.html

https://medium.com/twentybn/building-a-gesture-recognition-system-using-deep-learning-video-d24f13053a1

https://int8.io/monte-carlo-tree-search-beginners-guide/

https://medium.com/@sozercan/tensorflow-object-detection-on-azure-part-2-using-kubernetes-to-run-distributed-tensorflow-ced5b9a6184a

https://arxiv.org/pdf/1711.08141.pdf

https://blog.paperspace.com/how-to-implement-a-yolo-object-detector-in-pytorch/

https://towardsdatascience.com/the-fall-of-rnn-lstm-2d1594c74ce0

http://akosiorek.github.io/ml/2018/04/03/norm_flows.html

https://techburst.io/improving-the-way-we-work-with-learning-rate-5e99554f163b

https://medium.com/@shamir.stav_83310/making-your-c-library-callable-from-python-by-wrapping-it-with-cython-b09db35012a3 - Cython, C, Python

https://medium.com/@devnag/generative-adversarial-networks-gans-in-50-lines-of-code-pytorch-e81b79659e3f 50 Lines GAN in PyTorch

https://mml-book.github.io/ - Math for ML

https://github.com/fritzlabs/fritz-models (A collection of machine and deep learning models designed to run on mobile device)

https://arxiv.org/abs/1810.09538 (Pyro Arxive Paper)

https://colinraffel.com/blog/you-don-t-know-jax.html

https://arxiv.org/pdf/1811.08883.pdf (Rethinking Imagenet Pretraining [FAIR Paper])

https://github.com/pytorch/ignite

https://github.com/gidariss/FewShotWithoutForgetting

https://speakerdeck.com/perone/pytorch-under-the-hood

https://colab.research.google.com/github/google/jax/blob/master/notebooks/neural_network_and_data_loading.ipynb (Jax)

https://arxiv.org/pdf/1812.01216.pdf (Parameter Re-Initialization through Cyclical BatchSize Schedules)

https://arxiv.org/pdf/1811.10959.pdf (Dataset Distillation)

https://arxiv.org/pdf/1811.12231v1.pdf (Imagenet pretrained networks biased towards textures)

https://arxiv.org/pdf/1805.11604.pdf (How does BatchNorm help Optimization)

http://openaccess.thecvf.com/content_ECCV_2018/papers/Pei_Wang_Towards_Realistic_Predictors_ECCV_2018_paper.pdf

https://github.com/peiwang062/end2end_realistic_predictors

https://twistedkeyboardsoftware.com/?p=147 (Bandit Swarm Networks)

https://arxiv.org/pdf/1712.06080v1.pdf (Scene Understanding)

https://arxiv.org/pdf/1812.07179.pdf (Pseudo-LiDAR from Visual Depth Estimation:Bridging the Gap in 3D Object Detection for Autonomous Driving)

https://arxiv.org/pdf/1709.04347v2.pdf (Zoom in out object detection)

https://github.com/lexfridman/mit-deep-learning

https://github.com/lexfridman/mit-deep-learning/blob/master/tutorial_driving_scene_segmentation/tutorial_driving_scene_segmentation.ipynb

https://arxiv.org/pdf/1901.01341.pdf (Shieves - Topological Approach to Big Data)

https://arxiv.org/abs/1810.12894 (Exploration by Random Network Distillaton)

https://arxiv.org/pdf/1811.03804.pdf (Gradient Descent Finds Global Minima)

http://deepbayes.ru/2018/

https://www.lyrn.ai/2018/12/21/slowfast-dual-mode-cnn-for-video-understanding/

https://arxiv.org/pdf/1812.04914.pdf

https://www.reddit.com/r/MachineLearning/comments/a7egt8/d_whats_a_good_way_of_getting_started_with/?st=jqdyaz7s&sh=19ad3f50

https://rkevingibson.github.io/blog/neural-networks-as-ordinary-differential-equations/

https://arxiv.org/pdf/1710.11029.pdf (STOCHASTIC GRADIENT DESCENT PERFORMS VARIATIONALINFERENCE,CONVERGES TO LIMIT CYCLES FOR DEEP NETWORKS)

https://github.com/kumar-shridhar/PyTorch-BayesianCNN

https://github.com/tomgoldstein/loss-landscape

https://github.com/SimonKohl/probabilistic_unet

https://medium.com/bethgelab/neural-networks-seem-to-follow-a-puzzlingly-simple-strategy-to-classify-images-f4229317261f

https://github.com/bluesky314/Cyclical_LR_Scheduler_With_Decay_Pytorch

http://mlg.eng.cam.ac.uk/zoubin/papers/lds.pdf (A Unifying Review of Linear Gaussian Models)

https://mml-book.github.io/

https://medium.com/causal-data-science/causal-data-science-721ed63a4027 (Causal Data Science)

https://medium.com/@m.alzantot/deep-reinforcement-learning-demysitifed-episode-2-policy-iteration-value-iteration-and-q-978f9e89ddaa

http://rail.eecs.berkeley.edu/deeprlcourse/

https://www.kdnuggets.com/2016/08/tutorial-expectation-maximization-algorithm.html

https://explained.ai/gradient-boosting/index.html

http://fa.bianp.net/blog/2018/fw2/ Notes on the Frank-Wolfe Algorithm, Part II: A Primal-dual Analysis

https://github.com/VietTran86/Copula-Variational-Bayes

https://medium.com/google-cloud/how-to-run-deep-learning-models-on-google-cloud-platform-in-6-steps-4950a57acfa5

http://bjlkeng.github.io/posts/probabilistic-interpretation-of-regularization/

https://towardsdatascience.com/understanding-the-scaling-of-l%C2%B2-regularization-in-the-context-of-neural-networks-e3d25f8b50db

http://www.stats.ox.ac.uk/~teh/research/npbayes/Teh2010a.pdf (Dirichlet Process)

https://www.datascience.com/blog/introduction-to-bayesian-inference-learn-data-science-tutorials

https://kevinbinz.com/2014/06/12/an-introduction-to-bayesian-inference/

https://xcelab.net/rm/statistical-rethinking/

https://www.tensorflow.org/beta/tutorials/generative/cyclegan

https://myrtle.ai/learn/how-to-train-your-resnet-6-weight-decay/

https://www.tensorflow.org/beta/tutorials/generative/adversarial_fgsm

https://colab.research.google.com/github/davidcpage/cifar10-fast/blob/master/batch_norm_post.ipynb#scrollTo=MScciU2Ivinn

https://colab.research.google.com/github/reiinakano/adversarially-robust-neural-style-transfer/blob/master/Robust_Neural_Style_Transfer.ipynb

https://github.com/xinshuoweng/AB3DMOT - A Baseline for 3D Multi-Object Tracking

https://paperswithcode.com/paper/unsupervised-data-augmentation (https://github.com/google-research/uda)

https://paperswithcode.com/paper/random-erasing-data-augmentation

https://github.com/ddbourgin/numpy-ml

https://weightagnostic.github.io/papers/turing1948.pdf (Turing Paper)

https://francisbach.com/the-%ce%b7-trick-or-the-effectiveness-of-reweighted-least-squares/

https://locuslab.github.io/2019-07-09-uniform-convergence/ (Uniform convergence may be unable to explain generalization in deep learning)

https://users.aalto.fi/~ssarkka/pub/cup_book_online_20131111.pdf (Bayesian Filtering and Smoothing)

http://www.stats.ox.ac.uk/~steffen/teaching/bs2HT9/kalman.pdf (Kalman Slides)

https://github.com/ispgroupucl/layer-rotation-paper-experiments

https://gist.github.com/jctosta/af918e1618682638aa82 (Screen Quick Reference)

https://deepgenerativemodels.github.io/

https://imbalanced-learn.readthedocs.io/en/stable/api.html

https://arxiv.org/pdf/1906.07413v1.pdf

https://towardsdatascience.com/few-shot-learning-in-cvpr19-6c6892fc8c5 (Overview of FewShotLearning Papers in CVPR19)

https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python

https://towardsdatascience.com/get-started-with-using-cnn-lstm-for-forecasting-6f0f4dde5826

http://www.econ.uiuc.edu/~roger/research/rq/rq.pdf (Quantile Regression)

https://www.youtube.com/watch?v=IqQT8se9ofQ (Keras HyperOpt)