AI
Artificial intelligence: machine learning, neural networks, large language models, deep learning, AI research, and AI tools.
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Deep Learning Tutorial by LISA lab, University of Montreal deeplearning.net
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Pylearn2 deeplearning.net
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Deep down the rabbit hole: CVPR 2015 and beyond@Tombone's Computer Vision Blog computervisionblog.com
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Deep Learning by Prof. Yann LeCun cilvr.cs.nyu.edu
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[Paper-arXiv15] arxiv.org
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Deep Learning & Machine Learning (Advanced topics) youtube.com
A list of videos about deep learning that I found interesting or useful, this is a mix of a bit of everything.
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Siraj's Channel youtube.com
Siraj has entertaining, fast-paced video tutorials about deep learning.
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SOLID Machine Learning umaneo.com
The SOLID principles applied to Machine Learning.
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Machine Learning for Trading by Georgia Tech udacity.com
Interesting class for acquiring basic knowledge of machine learning applied to trading and some AI and finance concepts. I especially liked the section on Q-Learning.
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Deep Learning by Google udacity.com
Good intermediate to advanced-level course covering high-level deep learning concepts, I found it helps to get creative once the basics are acquired.
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NLP's ImageNet moment has arrived thegradient.pub
All hail NLP's ImageNet moment.
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Improving Inception and Image Classification in TensorFlow research.googleblog.com
Very interesting CNN architecture (e.g.: the inception-style convolutional layers is promising and efficient in terms of reducing the number of parameters).
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Sequence to Sequence Learning with Neural Networks papers.nips.cc
4 stacked LSTM cells of 1000 hidden size with reversed input sentences, and with beam search, on the WMT’14 English to French dataset.
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Deep learning for lazybones oduerr.github.io
Transfer learning tutorial in TensorFlow for vision from high-level embeddings of a pretrained CNN, AlexNet 2012.
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The Annotated Transformer nlp.seas.harvard.edu
Good for understanding the "Attention Is All You Need" (AIAYN) paper.
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Deep Learning & Recurrent Neural Networks (DL&RNN) neuraxio.com
The most richly dense, accelerated course on the topic of Deep Learning & Recurrent Neural Networks (scroll at the end).
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Neural Networks and Deep Learning neuralnetworksanddeeplearning.com
This book covers many of the core concepts behind neural networks and deep learning.
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Neural Networks and Deep Learning, ch.4 neuralnetworksanddeeplearning.com
A visual proof that neural nets can compute any function.
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Neural Networks and Deep Learning, ch.2 neuralnetworksanddeeplearning.com
Overview on how does the backpropagation algorithm works.
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Naver naver.com
This is a Korean search engine - best used with Google Translate, ironically. Surprisingly, sometimes deep learning search results and comprehensible advanced math content shows up more easily there than on Google search.
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Hybrid computing using a neural network with dynamic external memory nature.com
Improvements on differentiable memory based on NTMs: now it is the Differentiable Neural Computer (DNC).
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Estimating an Optimal Learning Rate For a Deep Neural Network medium.com
Clever trick to estimate an optimal learning rate prior any single full training.
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The Illustrated Transformer jalammar.github.io
Also good for understanding the "Attention Is All You Need" (AIAYN) paper.
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The Illustrated BERT, ELMo, and co. (How NLP Cracked Transfer Learning) jalammar.github.io
Understand the different approaches used for NLP's ImageNet moment.
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carpedm20's repositories github.com
Many interesting neural network architectures are implemented by the Korean guy Taehoon Kim, A.K.A. carpedm20.
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DL&RNN Course dl-rnn-course.neuraxio.com
I created this richely dense course on Deep Learning and Recurrent Neural Networks.
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Attention and Augmented Recurrent Neural Networks distill.pub
Interesting for visual animations, it is a nice intro to attention mechanisms as an example.
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Bidirectional Recurrent Neural Networks di.ufpe.br
Better classifications with RNNs with bidirectional scanning on the time axis.
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WaveNet: A Generative Model for Raw Audio deepmind.com
Realistic talking machines: perfect voice generation.
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Exploring the Depths of Recurrent Neural Networks with Stochastic Residual Learning cs224d.stanford.edu
Basically, residual connections can be better than stacked RNNs in the presented case of sentiment analysis.
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ImageNet Classification with Deep Convolutional Neural Networks cs.toronto.edu
AlexNet, 2012 ILSVRC, breakthrough of the ReLU activation function.
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Neural Networks, Manifolds, and Topology colah.github.io
Fresh look on how neurons map information.
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Artificial Neural Networks: Mathematics of Backpropagation briandolhansky.com
Picturing backprop, mathematically.
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Some other books I have read books.google.ca
Some books listed here are less related to deep learning but are still somehow relevant to this list.
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Improving Language Understanding with Unsupervised Learning blog.openai.com
SOTA across many NLP tasks from unsupervised pretraining on huge corpus.
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The future of deep learning blog.keras.io
François Chollet's thoughts on the future of deep learning.
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Recommending music on Spotify with deep learning benanne.github.io
Awesome for doing clustering on audio - post by an intern at Spotify.
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Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions arxiv.org
A WaveNet used as a vocoder can be conditioned on generated Mel Spectrograms from the Tacotron 2 LSTM neural network with attention to generate neat audio from text.
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Self-Normalizing Neural Networks arxiv.org
Appearance of the incredible SELU activation function.
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The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation arxiv.org
Merges the ideas of the U-Net and the DenseNet, this new neural network is especially good for huge datasets in image segmentation.
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Densely Connected Convolutional Networks arxiv.org
Best Paper Award at CVPR 2017, yielding improvements on state-of-the-art performances on CIFAR-10, CIFAR-100 and SVHN datasets, this new neural network architecture is named DenseNet.
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Learning to learn by gradient descent by gradient descent arxiv.org
RNN as an optimizer: introducing the L2L optimizer, a meta-neural network.
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Adaptive Computation Time for Recurrent Neural Networks arxiv.org
Let RNNs decide how long they compute. I would love to see how well would it combines to Neural Turing Machines. Interesting interactive visualizations on the subject can be found here.
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Pixel Recurrent Neural Networks arxiv.org
Nice for photoshop-like "content aware fill" to fill missing patches in images.
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Effective Approaches to Attention-based Neural Machine Translation arxiv.org
Exploring different approaches to attention mechanisms.
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Neural Turing Machines arxiv.org
Outstanding for letting a neural network learn an algorithm with seemingly good generalization over long time dependencies. Sequences recall problem.
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Deep Learning in Neural Networks: An Overview arxiv.org
You_Again's summary/overview of deep learning, mostly about RNNs.
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SentEval: An Evaluation Toolkit for Universal Sentence Representations arxiv.org
A Python framework to benchmark your sentence representations on many datasets (NLP tasks).
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ProjectionNet: Learning Efficient On-Device Deep Networks Using Neural Projections arxiv.org
Replace word embeddings by word projections in your deep neural networks, which doesn't require a pre-extracted dictionnary nor storing embedding matrices.
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ParlAI: A Dialog Research Software Platform arxiv.org
Another Python framework to benchmark your sentence representations on many datasets (NLP tasks).
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