Today pyconIL released all the videos from the pyConIL19 conference. I had the opportunity to watch, attend and speak at the conference this year and here are my picks of the five talks you won’t want to miss coming out of the start up nation.
For the Olim In Tech Audience all the selected talks are in English. I recommend watching the talks at 1.5x speed.
#5 Beyond Word Embeddings — The Future of Semantic Representation (My Talk)
Abstract: Since the advent of word2vec, word embeddings have become a go-to method for encapsulating distributional semantics in NLP applications. This presentation will review the strengths and weaknesses of using pre-trained word embeddings, and demonstrate how to incorporate more complex semantic representation schemes such as Semantic Role Labeling, Abstract Meaning Representation and Semantic Dependency Parsing into your applications.
For more information also check out the related blog series:
#4 PySnooper — Never use print for debugging again
Abstract:I had an idea for a debugging solution for Python that doesn’t require complicated configuration like PyCharm. I released PySnooper as a cute little open-source project that does that, and to my surprise, it became a huge hit overnight, hitting the top of Hacker News, r/python and GitHub trending. In this talk I’ll go into: How PySnooper can help you debug your code. How you can write your own debugging / code intelligence tools. How to make your open-source project go viral. How to use PuDB, another debugging solution, to find bugs in your code. A PEP idea for making debuggers easier to debug.
Speaker Bio: Ram Rachum is a software developer specializing in Python. When he’s not writing his biography in the third person, he’s doing consulting work for clients big and small, giving Python training to teams that would like to deepen their Python skills, and organizing the bi-monthly PyWeb-IL conference.
Check out pysnooper here:
#3 Is it safe ?” — python code security tools
Abstract: When asked about our python code “Is it safe ?”, we may use scanning tools (commercial and open source) to give a quantified answer, both for our code and external modules. We will follow regulatory and production use cases, with overview of available commercial tools, and review the “Howto” use of open source scan tools.
Speaker Bio: Python enthusiast, “to the point” manners, love for our planet and its habitats.
#2 Understanding Python’s Debugging Internals
Abstract: Knowing your enemies is as important as knowing your friends. Understanding your debugger is a little of both. Have you ever wondered how Python debugging looks on the inside? On our journey to building a Python debugger, we learned a lot about its internals, quirks and more.
During this session, we’ll share how debugging actually works in Python. We’ll discuss the differences between CPython and PyPy interpreters, explain the underlying debugging mechanism and show you how to utilize this knowledge at work and up your watercooler talk game.
Speaker Bio:Liran is the CTO and Co-Founder of Rookout, the Rapid Debugging Company. He’s an advocate of modern tech methodologies such as agile, lean and devops and his secret passion is to know how software actually works.
#1 Building text classifiers with state-of-the-art Deep Learning frameworks
Abstract: 2018 has been declared by many as the “ImageNet moment” of NLP. Novel attention and transformer-based Neural Network (NN) architectures significantly improved state-of-the-art performance in many tasks. NLP-oriented transfer learning techniques claim to make text classification easy by adapting pre-trained models, trained on huge corpora, to proprietary datasets with only a very small number of labels. Models that previously required significant computational power over vast periods of time can now be trained in several hours on standard CPUs. But with all of these models and frameworks to choose from, how does one make sense of it all? Where to begin? In this talk I will describe an end-to-end solution to a text classification problem. I will demonstrate how to employ the available classification methods, evaluating their performance and also (arguably more importantly) their ease of use. I will highlight common pitfalls, explaining what it takes to get a Deep Learning-based text classification model up and running.
Speaker Bio: Inbal completed her military service at one of the technological units of 8200. She holds a B.Sc in physics and electrical engineering from the Technion and an M.Sc in computer science from the Weizmann Institute of Science, where she focused on statistical machine learning with applications in computer vision. Later she completed a two-year research position at Tokyo University. In the past two years she has been working at Gong.io as a data scientist where she builds models to solve a wide range of problems in NLP/NLU.
If you find this talk on interesting check out this repo that I contributed to on how to fine tune these models on the cloud.
These are my picks of my 5 favorite talks others I enjoyed include NLP on legal contracts by Uri Goren and Social Network Analysis — From Graph Theory to Applications with Python by Dima Goldenberg and but there are many more that you should check see the whole collection below.
If you have any questions, comments, or topics you would like me to discuss feel free to follow me on Twitter if there is a milestone you feel I missed please let me know.
About the Author
Aaron (Ari) Bornstein is an avid AI enthusiast with a passion for history, engaging with new technologies and computational medicine. As an Open Source Engineer at Microsoft’s Cloud Developer Advocacy team, he collaborates with Israeli Hi-Tech Community, to solve real world problems with game changing technologies that are then documented, open sourced, and shared with the rest of the world.