Issue #87
January 24, 2021
Our very first Data Science Book is " Deep Learning with Python " by François Chollet. Deep Learning is conceptual framework responsible for most recent successes in Machine Learning and Artificial Intelligence. Unfortunately, it is not the easiest field to come to grips with. In this book, and in great part thanks to the modular structure of Keras, Chollet, a Google Engineer and brains behind Keras, manages to maintain the perfect balance between breath and depth. The book provides plenty of practical real world examples to explore the concepts as they are introduced and to provide the conceptual basis allowing you to quickly and successfully venture further in your own work. I hope you enjoy reading it as much as I did.
- 1. Controlled Experiments - Why Bother? [blog.harterrt.com]
- 2. Uber’s Real-time Data Intelligence Platform At Scale: Improving Gairos Scalability/Reliability [eng.uber.com]
- 3. The State of State Machines [googleprojectzero.blogspot.com]
- 4. Basic Scripting With Awk And Gnuplot [cyberchris.xyz]
- 5. The Famous Rain Problem and the Importance of Mathematical Reasoning Ability [medium.com/however-mathematics]
- 6. How can Machine Learning algorithms include better Causality? [medium.com/swlh]
- 7. The Goldbach Conjecture [medium.com/cantors-paradise]
- • The golden age of social science (A. Buyalskaya, M. Gallo, C. F. Camerer)
- • Bayesian statistics and modelling (R. van de Schoot, S. Depaoli, R. King, B. Kramer, K. Märtens, M. G. Tadesse, M. Vannucci, A. Gelman, D. Veen, J. Willemsen, C. Yau)
- • The joint dynamics of investor beliefs and trading during the COVID-19 crash (S. Giglio, M. Maggiori, J. Stroebel, S. Utkus)
- • Random graphs with arbitrary clustering and their applications (P. Mann, V. A. Smith, J. B. O. Mitchell, S. Dobson)
- • Three mysteries in deep learning: Ensemble, knowledge distillation, and self-distillation (Z. Allen-Zhu, Y. Li)
- • Community Detection in Blockchain Social Networks (S. X. Wu, Z. Wu, S. Chen, G. Li, S. Zhang)
- • The Gospel According to Q: Understanding the QAnon Conspiracy from the Perspective of Canonical Information (M. Aliapoulios, A. Papasavva, C. Ballard, E. De Cristofaro, G. Stringhini, S. Zannettou, J. Blackburn)
- • Implicit Bias of Linear RNNs (M. Emami, M. Sahraee-Ardakan, P. Pandit, S. Rangan, A. K. Fletcher)
Causal Bayesian NetworkX
All our videos are also available in our YouTube playlist.
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