- Machine Learning - Stanford - Coursera
- Deep Learning Specialization - Coursera
- Machine Learning A-Z™: Hands-On Python & R In Data Science - Udemy
- Deep Learning A-Z™: Hands-On Artificial Neural Networks - Udemy
- Practical Deep Learning for Coders, v3 - https://course.fast.ai/
- CS230 Deep Learning- https://cs230.stanford.edu/
- CS231n: Convolutional Neural Networks for Visual Recognition - http://cs231n.stanford.edu/
- CS224n: Natural Language Processing with Deep Learning - http://web.stanford.edu/class/cs224n/
- CS236:Deep Generative Models - https://deepgenerativemodels.github.io/
- CS234: Reinforcement Learning - https://web.stanford.edu/class/cs234/CS234Win2018/index.html
- Google ML CrashCourse - https://developers.google.com/machine-learning/crash-course
- Computational Linear Algebra For Coders - https://www.fast.ai/2017/07/17/num-lin-alg/
- CS229: Machine Learning - https://see.stanford.edu/Course/CS229
- NLP Specialization coursera
- Tensorflow specialization coursera
- Numpy practice - https://campus.datacamp.com/courses/intro-to-python-for-data-science/chapter-4-numpy?ex=2
- Pandas - https://www.youtube.com/playlist?list=PL5-da3qGB5ICCsgW1MxlZ0Hq8LL5U3u9y
- Test Pandas - https://www.kaggle.com/learn/pandas
- Pytorch - https://www.youtube.com/watch?v=v5cngxo4mIg&list=PLZbbT5o_s2xrfNyHZsM6ufI0iZENK9xgG
- DL with Pytorch - Coursera, Udacity
- Practice challenges - https://www.kaggle.com/learn/overview
- fast.ai resources https://forums.fast.ai/t/recommended-python-learning-resources/26888
- https://campus.datacamp.com/courses/intro-to-python-for-data-science/chapter-1-python-basics?ex=1
- Deep Learning with python - Franchois chollet
- Python Data Science Handbook - Jake vanderplas
- Deep learning with python – Jason BrownLee
- Master machine learning algorithms – Jason BrownLee
- Deep learning for nlp – Jason Brownlee
- Machine learning mastery with python -Jason brownlee
- Introduction to time series forecasting with python – Jason brownlee
- Hands-on Machine learning with scikit-learn and tensorflow
- The hundred page machine learning book – andriy burkov
- Deep learning with pytorch- Vishnu Subramanian -Packt
- NLP with pytorch – Packt
- Machine learning yearning – Andrew NG
- neuralnetworksanddeeplearning - http://neuralnetworksanddeeplearning.com/index.html
- Deep Learning Book - Ian GoodFellow http://faculty.neu.edu.cn/yury/AAI/Textbook/DeepLearningBook.pdf
- http://colah.github.io/
- https://ruder.io/
- https://cs231n.github.io/
- https://github.com/jantic/DeOldify
- https://deepai.org/ - updates in DL and AI
- Research Papers - http://www.arxiv-sanity.com/
- http://karpathy.github.io/
- https://www.machinelearningisfun.com/
- http://deeplearning.net/
- https://distill.pub/
- Art Generation - https://deepart.io/ 12.http://jalammar.github.io/
- Visualizing and Understanding Convolutional Networks - https://arxiv.org/pdf/1311.2901.pdf
- Network in Network - https://arxiv.org/pdf/1312.4400.pdf
- ResNet - Deep Residual Learning for Image Recognition - https://arxiv.org/pdf/1512.03385.pdf
- AlexNet - ImageNet Classification with Deep Convolutional Neural Networks - https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf
- VGGNet - Very Deep Convolutional Networks for Large-Scale Image Recognition - https://arxiv.org/pdf/1409.1556.pdf
- Cats and Dogs Dataset - https://www.robots.ox.ac.uk/~vgg/publications/2012/parkhi12a/parkhi12a.pdf
- Cyclical Learning Rates for Training Neural Networks - https://arxiv.org/pdf/1506.01186.pdf
- Universal Language Model Fine-tuning for Text Classification - https://arxiv.org/pdf/1801.06146.pdf
- U-Net: Convolutional Networks for Biomedical Image Segmentation - https://arxiv.org/pdf/1505.04597.pdf
- Dropout: A Simple Way to Prevent Neural Networks from Overfitting - http://jmlr.org/papers/volume15/srivastava14a/srivastava14a.pdf
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift - https://arxiv.org/pdf/1502.03167.pdf
- Grad-CAM: Visual Explanations from Deep Networksvia Gradient-based Localization - https://arxiv.org/pdf/1610.02391.pdf
- A guide to convolution arithmetic for deep learning - https://arxiv.org/pdf/1603.07285.pdf
- Visualizing the Loss Landscape of Neural Nets - https://arxiv.org/pdf/1712.09913.pdf
- Wasserstein GAN - https://arxiv.org/pdf/1701.07875.pdf
- Perceptual Losses for Real-Time Style Transfer and Super-Resolution - https://arxiv.org/pdf/1603.08155.pdf
- Transformers - https://arxiv.org/pdf/1706.03762.pdf
- BERT - https://arxiv.org/pdf/1810.04805.pdf
- GPT-2 - https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf
- GPT-3 - https://arxiv.org/pdf/2005.14165.pdf
- Covariate shift - https://dkopczyk.quantee.co.uk/covariate_shift/
- Types, causes and remediations for dataset shift - https://towardsdatascience.com/understanding-dataset-shift-f2a5a262a766
- https://mlexplained.com/2018/11/30/an-overview-of-normalization-methods-in-deep-learning/
- https://medium.com/techspace-usict/normalization-techniques-in-deep-neural-networks-9121bf100d8
- Implementation with back prop - https://kratzert.github.io/2016/02/12/understanding-the-gradient-flow-through-the-batch-normalization-layer.html
- https://www.youtube.com/watch?v=nUUqwaxLnWs
- https://www.youtube.com/watch?v=gYpoJMlgyXA&feature=youtu.be&list=PLkt2uSq6rBVctENoVBg1TpCC7OQi31AlC&t=3078
- Emergence of GD - https://cs231n.github.io/optimization-1/
- Gradient Checks - https://cs231n.github.io/neural-networks-3/
- Gradient Descent Optimization algorithms - https://ruder.io/optimizing-gradient-descent/index.html
- Improvements - https://ruder.io/deep-learning-optimization-2017/
- https://johnchenresearch.github.io/demon/
- https://mc.ai/what-is-orthogonalization-in-machine-learning/
- https://medium.com/@rajathbharadwaj/how-does-orthogonalization-relate-to-machine-learning-a61a8aedf0e6
- Image Classification, Object Localization, Object Detection, Sliding Window(Overfeat), R-CNN , Fast R-CNN , Faster R-CNN - CS231n (winter 2016) lecture & Dl.ai(C4W3). papers - Alexnet, VGG, Resnet, Rcnn, fast rcnn, faster rcnn, yolo.
- Semantic Segmentation - CS231n (2017) lecture 11 - Papers: Fully CNN for SS , learning deconv for SS
- Face Verification - Papers: DeepFace, FaceNet
- One Shot learning using siamese Network with basic loss function - https://towardsdatascience.com/one-shot-learning-with-siamese-networks-using-keras-17f34e75bb3d
- Siamese Network with Triplet Loss Function - https://medium.com/@crimy/one-shot-learning-siamese-networks-and-triplet-loss-with-keras-2885ed022352
- Art Generation (Neural Style Transfer) - paper: A neural algorithm of artistic style, last part of lecture CS231n 2017.
- Visualization of CNNs - CS230 Lecture 7
- Explanation of RNNs, Types , BP , LSTM, Image Captioning - CS231n 2017 lecture 10
- LSTMs - chris olah blog , Paper:LSTM
- Visualizing and Understanding RNNs - paper
- Speech Recognition - CTC Paper
- NLP , word Embeddings -
- Blue Score - paper: A method for automatic evaluation of machine translation
- Attention Model - paper:Neural machine translation by jointly learning to align and translate, for image captioning - Show , attend and Tell: Neural image caption generation with visual attention.
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