Data Science at Home
Episodes
Tuesday Jun 25, 2019
Episode 66: More intelligent machines with self-supervised learning
Tuesday Jun 25, 2019
Tuesday Jun 25, 2019
In this episode I talk about a new paradigm of learning, which can be found a bit blurry and not really different from the other methods we know of, such as supervised and unsupervised learning. The method I introduce here is called self-supervised learning.
Enjoy the show!
Don't forget to subscribe to our Newsletter at amethix.com and get the latest updates in AI and machine learning. We do not spam. Promise!
References
Deep Clustering for Unsupervised Learning of Visual Features
Self-supervised Visual Feature Learning with Deep Neural Networks: A Survey
Sunday Jun 23, 2019
Episode 65: AI knows biology. Or does it?
Sunday Jun 23, 2019
Sunday Jun 23, 2019
The successes of deep learning for text analytics, also introduced in a recent post about sentiment analysis and published here are undeniable. Many other tasks in NLP have also benefitted from the superiority of deep learning methods over more traditional approaches. Such extraordinary results have also been possible due to the neural network approach to learn meaningful character and word embeddings, that is the representation space in which semantically similar objects are mapped to nearby vectors. All this is strictly related to a field one might initially find disconnected or off-topic: biology.
Don't forget to subscribe to our Newsletter at amethix.com and get the latest updates in AI and machine learning. We do not spam. Promise!
References
[1] Rives A., et al., “Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences”, biorxiv, doi: https://doi.org/10.1101/622803
[2] Vaswani A., et al., “Attention is all you need”, Advances in neural information processing systems, pp. 5998–6008, 2017.
[3] Bahdanau D., et al., “Neural machine translation by jointly learning to align and translate”, arXiv, http://arxiv.org/abs/1409.0473.
Friday Jun 14, 2019
Episode 64: Get the best shot at NLP sentiment analysis
Friday Jun 14, 2019
Friday Jun 14, 2019
The rapid diffusion of social media like Facebook and Twitter, and the massive use of different types of forums like Reddit, Quora, etc., is producing an impressive amount of text data every day.
There is one specific activity that many business owners have been contemplating over the last five years, that is identifying the social sentiment of their brand, by analysing the conversations of their users.
In this episode I explain how one can get the best shot at classifying sentences with deep learning and word embedding.
Additional material
Schematic representation of how to learn a word embedding matrix E by training a neural network that, given the previous M words, predicts the next word in a sentence.
Word2Vec example source code
https://gist.github.com/rlangone/ded90673f65e932fd14ae53a26e89eee#file-word2vec_example-py
References
[1] Mikolov, T. et al., "Distributed Representations of Words and Phrases and their Compositionality", Advances in Neural Information Processing Systems 26, pages 3111-3119, 2013.
[2] The Best Embedding Method for Sentiment Classification, https://medium.com/@bramblexu/blog-md-34c5d082a8c5
[3] The state of sentiment analysis: word, sub-word and character embedding https://amethix.com/state-of-sentiment-analysis-embedding/
Tuesday Jun 04, 2019
Episode 63: Financial time series and machine learning
Tuesday Jun 04, 2019
Tuesday Jun 04, 2019
In this episode I speak to Alexandr Honchar, data scientist and owner of blog https://medium.com/@alexrachnogAlexandr has written very interesting posts about time series analysis for financial data. His blog is in my personal list of best tutorial blogs. We discuss about financial time series and machine learning, what makes predicting the price of stocks a very challenging task and why machine learning might not be enough.As usual, I ask Alexandr how he sees machine learning in the next 10 years. His answer - in my opinion quite futuristic - makes perfect sense.
You can contact Alexandr on
Twitter https://twitter.com/AlexRachnog
Facebook https://www.facebook.com/rachnog
Medium https://medium.com/@alexrachnog
Enjoy the show!