Issue #191
February 19, 2023
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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