Issue #191
February 19, 2023
Dear friends,
Welcome to the Mardi Gras edition of the Sunday Briefing. We’re on hiatus from blogging, but you’re welcome to catch up on our latest Medium post “Top 10 Books we read in 2022”, the latest post on the G4Sci series: Network Attacks: Breaking up a network without observing it completely or the latest Viz4Sci in the series: Photo Color Scheme.
Don’t forget we have the next edition of the NLP For Everyone webinar coming up this Tuesday, Feb 21st. Be sure to Register so we don’t miss out!
We’re proud to announce a brand new webinar series : Interactive Data Visualization with Python where we explore different ways in which we can build visualizations that we can interact with to better explore our data. You can still be one of the first ones to Sign Up! The next edition of the Timeseries For Everyone webinar has just been announced for Apr 11 and you can already Register.
On our regularly scheduled content, we dive into an interactive explanation of quadtrees, the Bias-Variance Tradeoff and into a Critical Field Guide for Working with Machine Learning Datasets.
On the academic front, we look toward a taxonomy of trust for probabilistic machine learning, explore Zero-shot causal learning and discover dialectograms: Machine Learning Differences between Discursive Communities.
This week’s Data Science Book is “AI and Machine Learning for Coders” by L. Moroney. As always you can find all the previous book recommendations on our website. In the video of the week, we have a tutorial on Plotting Choropleth Maps using Python.
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Semper discentes,
The D4S Team
This week’s Data Science Book is " AI and Machine Learning for Coders " by L. Moroney and with a foreword by none other than Andrew Ng. This is a book that exceeds expectations with excellent explanations on how to code machine learning using TensorFlow and different ML techniques. The book covers various topics, including computer vision, natural language processing, and time series forecasting, and even includes a section on text generation.
The book is aimed specifically at coders with Python experience and explains how neural networks work at a high level without overwhelming readers with too much math. The author does an excellent job of explaining convolution, maxpooling, interpretability, bias/fairness, and Google's AI principles.
Overall, anyone who wants to learn about deep learning using TensorFlow, will find here an excellent resource that provides a solid foundation in deep learning and is suitable for hands-on practitioners without overwhelming them with math.
- 1. The Bias-Variance Tradeoff, Explained [towardsdatascience.com]
- 2. ChatGPT Is an Extra-Ordinary Python Programmer [betterprogramming.pub]
- 3. Is Artificial Consciousness Possible? A Summary of Selected Books [sentienceinstitute.org]
- 4. An interactive explanation of quadtrees [jimkang.com]
- 5. A Critical Field Guide for Working with Machine Learning Datasets [knowingmachines.org]
- 6. How Deadly Was China's Covid Wave? [nytimes.com]
- 7. What's new in Matplotlib 3.7.0 [matplotlib.org]
- • Toward a taxonomy of trust for probabilistic machine learning (T. Broderick, A. Gelman, R. Meager, A. L. Smith, T. Zheng)
- • A time evolving online social network generation algorithm (P. Shirzadian, B. Antony, A. G. Gattani, N. Tasnina, L. S. Heath)
- • Identifying influential nodes based on resistance distance (M. Li, S. Zhou, D. Wang, G. Chen)
- • Transformer models: an introduction and catalog (X. Amatriain)
- • SHEEP: Signed Hamiltonian Eigenvector Embedding for Proximity (S. Babul, R. Lambiotte)
- • Zero-shot causal learning (H. Nilforoshan, M. Moor, Y. Roohani, Y. Chen, A. Šurina, M. Yasunaga, S. Oblak, J. Leskovec)
- • Dialectograms: Machine Learning Differences between Discursive Communities (T. Enggaard, A. Lohse, M. A. Pedersen, S. Lehmann)
Plotting Choropleth Maps using Python
All our videos are also available in our
YouTube playlist.
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