Issue #28
December 8, 2019
- 1. Understanding Transfer Learning for Medical Imaging [ai.googleblog.com]
- 2. Biased Algorithms Are Easier to Fix Than Biased People [nytimes.com]
- 3. Towards a new theory of learning: Statistical Mechanics of deep neural networks [calculatedcontent.com]
- 4. The Power of 10 — NASA's Rules for Coding [medium.com/better-programming]
- 5. Humans-in-the-loop forecasting: integrating data science and business planning [unofficialgoogledatascience.com]
- 6. Better intuition for information theory [blackhc.net]
- 7. An Epidemic of AI Misinformation [thegradient.pub]
- • Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019 (O. B. Sezer, M. U. Gudelek, A. M. Ozbayoglu)
- • Neural Machine Translation: A Review (F. Stahlberg)
- • Universality of power-law exponents by means of maximum-likelihood estimation (V. Navas-Portella, Á. González, I. Serra, E. Vives, Á. Corral)
- • Using Machine Learning to Assess Short Term Causal Dependence and Infer Network Links (A. Banerjee, J. Pathak, R. Roy, J. G. Restrepo, E. Ott)
- • Scalable Graph Algorithms (C. Schulz)
- • PyTorch: An Imperative Style, High-Performance Deep Learning Library (A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, A. Desmaison, A. Köpf, E. Yang, Z. DeVito, M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. Bai, S. Chintala)
The Future of Deep Learning with Sara Hooker (Future of Finance Summit '19)
All our videos are also available in our YouTube playlist.
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