Archive for the 'Deep Learning' Category

In this episode, I am with Aaron Gokaslan, computer vision researcher, AI Resident at Facebook AI Research. Aaron is the author of OpenGPT-2, a parallel NLP model to the most discussed version that OpenAI decided not to release because too accurate to be published.

We discuss about image-to-image translation, the dangers of the GPT-2 model and the future of AI.
Moreover, 
Aaron provides some very interesting links and demos that will blow your mind!

Enjoy the show! 

References

Multimodal image to image translation (not all mentioned in the podcast but recommended by Aaron)

Pix2Pix: 
 
CycleGAN:
 

GANimorph

 

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Training neural networks faster usually involves the usage of powerful GPUs. In this episode I explain an interesting method from a group of researchers from Google Brain, who can train neural networks faster by squeezing the hardware to their needs and making the training pipeline more dense.

Enjoy the show!

 

References

Faster Neural Network Training with Data Echoing
https://arxiv.org/abs/1907.05550

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In this episode I am with Jadiel de Armas, senior software engineer at Disney and author of Videflow, a Python framework that facilitates the quick development of complex video analysis applications and other series-processing based applications in a multiprocessing environment. 

I have inspected the videoflow repo on Github and some of the capabilities of this framework and I must say that it’s really interesting. Jadiel is going to tell us a lot more than what you can read from Github 

 

References

Videflow Github official repository
https://github.com/videoflow/videoflow

 

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Training neural networks faster usually involves the usage of powerful GPUs. In this episode I explain an interesting method from a group of researchers from Google Brain, who can train neural networks faster by squeezing the hardware to their needs and making the training pipeline more dense.

Enjoy the show!

 

References

Faster Neural Network Training with Data Echoing
https://arxiv.org/abs/1907.05550

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In this episode, I am with Dr. Charles Martin from Calculation Consulting a machine learning and data science consulting company based in San Francisco. We speak about the nuts and bolts of deep neural networks and some impressive findings about the way they work. 

The questions that Charles answers in the show are essentially two:

  1. Why is regularisation in deep learning seemingly quite different than regularisation in other areas on ML?

  2. How can we dominate DNN in a theoretically principled way?

 

References 

 

 

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In this episode I am with Jadiel de Armas, senior software engineer at Disney and author of Videflow, a Python framework that facilitates the quick development of complex video analysis applications and other series-processing based applications in a multiprocessing environment. 

I have inspected the videoflow repo on Github and some of the capabilities of this framework and I must say that it’s really interesting. Jadiel is going to tell us a lot more than what you can read from Github 

 

References

Videflow Github official repository
https://github.com/videoflow/videoflow

 

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Today I am with David Kopec, author of Classic Computer Science Problems in Python, published by Manning Publications.

His book deepens your knowledge of problem solving techniques from the realm of computer science by challenging you with interesting and realistic scenarios, exercises, and of course algorithms.
There are examples in the major topics any data scientist should be familiar with, for example search, clustering, graphs, and much more.

Get the book from https://www.manning.com/books/classic-computer-science-problems-in-python and use coupon code poddatascienceathome19 to get 40% discount.

 

References

Twitter https://twitter.com/davekopec

GitHub https://github.com/davecom

classicproblems.com

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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!

 

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References

Deep Clustering for Unsupervised Learning of Visual Features

Self-supervised Visual Feature Learning with Deep Neural Networks: A Survey

 

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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.

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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_training.png?w=702&ssl=1

 

 

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/

 

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