Education
133 items from guillaume-chevalier/awesome-deep-learning-resources ★1,821
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Growing Neat Software Architecture from Jupyter Notebooks youtube.com
A primer on how to structure your Machine Learning projects when using Jupyter Notebooks.
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Gradient Descent youtube.com
Okay, I already listed Andrew NG's Coursera class above, but this video especially is quite pertinent as an introduction and defines the gradient descent algorithm.
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Deep Learning Lecture 12: Recurrent Neural Nets and LSTMs youtube.com
Unfolding of RNN graphs is explained properly, and potential problems about gradient descent algorithms are exposed.
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Two Minute Papers' Channel youtube.com
Interesting and shallow overview of some research papers, for example about WaveNet or Neural Style Transfer.
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Computer Science youtube.com
Yet another YouTube playlist I composed, this time about various CS topics.
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Signal Processing Playlist youtube.com
A YouTube playlist I composed about DFT/FFT, STFT and the Laplace transform - I was mad about my software engineering bachelor not including signal processing classes (except a bit in the quantum physics class).
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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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Tensor Calculus and the Calculus of Moving Surfaces youtube.com
Generalize properly how Tensors work, yet just watching a few videos already helps a lot to grasp the concepts.
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Siraj's Channel youtube.com
Siraj has entertaining, fast-paced video tutorials about deep learning.
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What is the Best Multi-Stage Architecture for Object Recognition? yann.lecun.com
Awesome for the use of "local contrast normalization".
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Neuralink and the Brain’s Magical Future waitbutwhy.com
Thought provoking article about the future of the brain and brain-computer interfaces.
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Why do 87% of data science projects never make it into production? venturebeat.com
Data is not to be overlooked, and communication between teams and data scientists is important to integrate solutions properly.
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SOLID Machine Learning umaneo.com
The SOLID principles applied to Machine Learning.
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GLO-4030/7030 Apprentissage par réseaux de neurones profonds ulaval-damas.github.io
This is a class given by Philippe Giguère, Professor at University Laval. I especially found awesome its rare visualization of the multi-head attention mechanism, which can be contemplated at the slide 28 of week 13's class.
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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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François Chollet's Twitter twitter.com
Author of Keras - has interesting Twitter posts and innovative ideas.
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The real reason most ML projects fail towardsdatascience.com
Focus on clear business objectives, avoid pivots of algorithms unless you have really clean code, and be able to know when what you coded is "good enough".
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NLP's ImageNet moment has arrived thegradient.pub
All hail NLP's ImageNet moment.
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Gradient descent algorithms in a saddle point sebastianruder.com
Visualize how different optimizers interacts with a saddle points.
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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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Announcing SyntaxNet: The World’s Most Accurate Parser Goes Open Source research.googleblog.com
Parsey McParseface's birth, a neural syntax tree parser.
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Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour research.fb.com
Incredibly fast distributed training of a CNN.
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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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LibriSpeech ASR corpus openslr.org
Huge free English speech dataset with balanced genders and speakers, that seems to be of high quality.
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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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Hacker News news.ycombinator.com
Maybe how I discovered ML - Interesting trends appear on that site way before they get to be a big deal.
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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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Yes you should understand backprop medium.com
Exposing backprop's caveats and the importance of knowing that while training models.
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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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ML / DL repositories I starred github.com
GitHub is full of nice code samples & projects.
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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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Window Functions en.wikipedia.org
Wikipedia page that lists some of the known window functions - note that the Hann-Poisson window is specially interesting for greedy hill-climbing algorithms (like gradient descent for example).
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Predictions made by Ray Kurzweil en.wikipedia.org
List of mid to long term futuristic predictions made by Ray Kurzweil.
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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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Gradient descent algorithms in an almost flat landscape devblogs.nvidia.com
Visualize how different optimizers interacts with an almost flat landscape.
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WaveNet: A Generative Model for Raw Audio deepmind.com
Realistic talking machines: perfect voice generation.
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DataTau datatau.com
This is a hub similar to Hacker News, but specific to data science.
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Going Deeper with Convolutions cv-foundation.org
GoogLeNet: Appearance of "Inception" layers/modules, the idea is of parallelizing conv layers into many mini-conv of different size with "same" padding, concatenated on depth.
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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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