Hello r/machinelearning,

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This talk is my best attempt to strip down the core concepts of word2vec, and explain how the algorithm is being to power recommendation engines. The power of recent NLP models is a testament to how far we’ve come in extracting patterns from sequential data. This application of the algorithm treats other sequences of data (e.g. website click sessions, songs in user-created playlists) as sentences leading us to create embeddings (for items in an ecommerce store, or songs/artists in a music service) that we can use for similarity and recommendation. My Illustrated Word2vec post built on the materials I created for this talk. I hope you find it useful:

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https://www.infoq.com/presentations/nlp-word-embedding

Generative models are often used to sample high-dimensional data points from a manifold with small intrinsic dimension. Existing techniques for comparing generative models focus on global data properties such as mean and covariance; in that sense, they are extrinsic and uni-scale. We develop the first, to our knowledge, intrinsic and multi-scale method for characterizing and comparing underlying data manifolds, based on comparing all data moments by lower-bounding the spectral notion of the Gromov-Wasserstein distance between manifolds. In a thorough experimental study, we demonstrate that our method effectively evaluates the quality of generative models; further, we showcase its efficacy in discerning the disentanglement process in neural networks.

TL;DR: We introduce a metric based on heat kernels that is able to compare data manifolds in unaligned spaces. We test it on GAN evaluation (it works), and on tracking the disentanglement and training progress of NNs (also works!).

ArXiv page: https://arxiv.org/abs/1905.11141 Code is available here: https://github.com/xgfs/msid I am here to answer your questions about the paper! 🙂

Extra shoutout to /u/augustushimself who wrote a brilliant GAN post, and the discussion that followed on this sub. It gave a lot of inspiration for writing this.

I am so much confused and tired for looking online courses…Idk where to start and what to do..i tried udacity intro to machine learning but they use python 2 and it gets very hectic to change to python 3 all time … Neither the teachers teach well …😞😭Pls guide me ….im a beginner

I’m looking for a database that has sufficient information to mathematically prove things like: “[young man] == [a boy] == [male child]”.

This database should have basic information such as “[a king] is [male], [person], [sovereign], [ruler], etc..” but also strict definitions like “[quick] == [moving fast] or [in short time]”.

Any thoughts?

Hi MachineLearning,

I have also created a GAN music generator (seen a few on here in the past few months)

Link: https://hookgen.com

It is basically a GAN in TensorFlow r1.13, however, I have two separate discriminative networks, i.e. one network feeds the piano score, while the other the duration the song was played on the website in milliseconds

Please let me know if you have any questions

I will deploy the code tomorrow on GitHub if you have any interest