Links to useful online tutorials, blogposts, images etc. all about deep learning. This is a work in progress and I will continue to add to to this list as and when I find useful things.
If you have any nice tutorials or links you think I should add please submit as an issue and I will look at putting them in the list. Also if any of the links are broken please let me know so I can remove them or find alternatives.
- Introducition to Machine Learning
- Deep Learning
- Convolutional Neural Networks
- Tensorflow
- Pytorch
- Linear Algebra
- Optimisation
- Generative Models
- Projects
- Research papers
- Datasets
- Andrew Ng https://www.coursera.org/learn/machine-learning
- Elements of Statistical Learning (Ch.1-4/7) http://statweb.stanford.edu/%7Etibs/ElemStatLearn/printings/ESLII_print10.pdf
- Machine/Deep Learning cheatsheets https://github.com/kailashahirwar/cheatsheets-ai
- Geoffrey Hinton Neural Networks for Machine Learning https://www.coursera.org/learn/neural-networks
- Andrew Ng (2017) https://www.coursera.org/specializations/deep-learning
- CNN basics explained well (explaining kernels) https://adeshpande3.github.io/adeshpande3.github.io/A-Beginner%27s-Guide-To-Understanding-Convolutional-Neural-Networks/
- Explaining different convolution types https://towardsdatascience.com/types-of-convolutions-in-deep-learning-717013397f4d
- Experiments on placement of batchnorm in Resnets http://torch.ch/blog/2016/02/04/resnets.html
- Freezing/saving and serving a model https://blog.metaflow.fr/tensorflow-how-to-freeze-a-model-and-serve-it-with-a-python-api-d4f3596b3adc
- Various tutorials/ipynb https://github.com/aymericdamien/TensorFlow-Examples
- Various resources https://github.com/astorfi/Awsome-TensorFlow-Resources
- Various resources https://github.com/jtoy/awesome-tensorflow
- Various resources https://github.com/astorfi/TensorFlow-World-Resources
- TFRecord example http://warmspringwinds.github.io/tensorflow/tf-slim/2016/12/21/tfrecords-guide/
- TFRecord for images explained https://planspace.org/20170403-images_and_tfrecords/
- Visualise TF graphs in ipynb https://blog.jakuba.net/2017/05/30/tensorflow-visualization.html
- Profiling/tracing TF https://medium.com/towards-data-science/howto-profile-tensorflow-1a49fb18073d
- Eager mode (dynamic graphs) https://medium.com/@yaroslavvb/tensorflow-meets-pytorch-with-eager-mode-714cce161e6c
- Dataset api buffer_size meaning https://stackoverflow.com/questions/46444018/meaning-of-buffer-size-in-dataset-map-dataset-prefetch-and-dataset-shuffle
- Variety of pretrained models and training scripts https://github.com/aaron-xichen/pytorch-playground
- Intro to PyTorch for Kaggle competitions https://github.com/bfortuner/pytorch-kaggle-starter
- Cheat sheet https://github.com/Tgaaly/pytorch-cheatsheet/blob/master/README.md
- Cheat sheet with examples https://github.com/bfortuner/pytorch-cheatsheet/blob/master/pytorch-cheatsheet.ipynb
- https://medium.com/towards-data-science/linear-algebra-cheat-sheet-for-deep-learning-cd67aba4526c#.nfoveoe56
- http://parrt.cs.usfca.edu/doc/matrix-calculus/index.html
- Momentum - http://distill.pub/2017/momentum/
- Overview of differences in losses for different GANS https://github.com/hwalsuklee/tensorflow-generative-model-collections
- 17 hacks for training GANS https://github.com/soumith/ganhacks
- Picture of why GANs are cool https://ai2-s2-public.s3.amazonaws.com/figures/2016-11-08/546b3592f59b3445ef12fba506b729c832198c33/2-Figure3-1.png
- David Silver Deepmind lectures https://www.youtube.com/playlist?list=PLeJKOhW5z62XKURemUDc3N92Min9yaR12
- Intro to RL algorithms https://medium.com/@huangkh19951228/introduction-to-various-reinforcement-learning-algorithms-i-q-learning-sarsa-dqn-ddpg-72a5e0cb6287
- Collection of links https://github.com/terryum/awesome-deep-learning-papers/blob/master/README.md
- Collection of links https://github.com/sbrugman/deep-learning-papers
- Pose estimation https://arxiv.org/abs/1312.4659
- Object detection https://arxiv.org/abs/1311.2524
- Object detection https://arxiv.org/abs/1312.2249
- Object detection https://arxiv.org/abs/1412.1441
- Object detection https://arxiv.org/abs/1506.01497 (Faster R-CNN)
- Object detection https://arxiv.org/abs/1506.02640 (YOLO)
- Object detection https://arxiv.org/abs/1512.02325 (SSD)
- Object detection https://arxiv.org/abs/1611.10012 (Object detection comparison)
- Semantic segmentation https://arxiv.org/abs/1511.00561 (Segnet)
- Semantic segmentation https://arxiv.org/abs/1505.07293 (Segnet)
- Semantic segmentation https://arxiv.org/abs/1511.02680 (Bayesian Segnet)
- Network architectures https://arxiv.org/abs/1602.07360 (SqueezeNet)
- Network architectures https://arxiv.org/abs/1606.00373 (Fully Convolution ResNet)
- Re Identification https://arxiv.org/abs/1703.07737 (In defence of triplet loss)
- Depth estimation https://arxiv.org/abs/1406.2283 (Depth map prediction, multi-scale deep network)
- Depth estimation https://arxiv.org/abs/1411.4734 (Depth, surface normals & semnatic labels with common multi scale conv.)
- Depth estimation https://arxiv.org/abs/1411.6387 (Deep conv neural fields for depth estimation from a single image)
- Network understanding https://arxiv.org/abs/1701.04128 (Understanding effective receptive field in deep CNN)
- Self driving cars https://arxiv.org/pdf/1704.07911.pdf (Explaining How a DNN can steer a car NVIDIA)
- Resnet paper https://arxiv.org/pdf/1512.03385.pdf
- Quantization https://arxiv.org/pdf/1502.02551.pdf
- Quantization http://proceedings.mlr.press/v48/linb16.pdf
- Quantization https://www.microsoft.com/en-us/research/wp-content/uploads/2017/04/FxpNet-submitted.pdf
- Quantization https://arxiv.org/pdf/1703.03073.pdf
- Quantizatoin https://arxiv.org/pdf/1605.06402.pdf (ristretto)
- Super Resolution with GANs https://arxiv.org/pdf/1609.04802.pdf
- Instance segmentation https://arxiv.org/abs/1703.06870 (Mask R-CNN)
- NLP https://github.com/niderhoff/nlp-datasets
- MNIST png format https://github.com/myleott/mnist_png