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Top 14 Must-Read Data Science Books You Need On Your Desk

datapine

We gave you a curated list of our top 15 data analytics books , top 18 data visualization books , top 16 SQL books – and, as promised, we’re going to tell you all about the world’s best books on data science. 2) “Deep Learning” by Ian Goodfellow, Yoshua Bengio and Aaron Courville. click for book source**.

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Deep Learning Illustrated: Building Natural Language Processing Models

Domino Data Lab

Many thanks to Addison-Wesley Professional for providing the permissions to excerpt “Natural Language Processing” from the book, Deep Learning Illustrated by Krohn , Beyleveld , and Bassens. The excerpt covers how to create word vectors and utilize them as an input into a deep learning model. Introduction.

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Get Maximum Value from Your Visual Data

DataRobot

Image recognition is one of the most relevant areas of machine learning. Deep learning makes the process efficient. However, not everyone has deep learning skills or budget resources to spend on GPUs before demonstrating any value to the business. Who Can Benefit from the Visual Data? DataRobot Visual AI.

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Interview with: Sankar Narayanan, Chief Practice Officer at Fractal Analytics

Corinium

A playbook for this is to run multiple experiments in parallel and create ‘MVPs’ (fail/learn fast), as well as incorporate feedback mechanisms to enable an improvement loop, and scaling the ones that show the fastest path to ROI. What advances do you see in Visual Analytics in the next five years?

Insurance 250
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The DataOps Vendor Landscape, 2021

DataKitchen

DataOps needs a directed graph-based workflow that contains all the data access, integration, model and visualization steps in the data analytic production process. A complete DataOps program will have a unified, system-wide view of process metrics using a common data store. Meta-Orchestration .

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Why you should care about debugging machine learning models

O'Reilly on Data

If you’re using Python and deep learning libraries, the CleverHans and Foolbox packages can also help you debug models and find adversarial examples. For model training and selection, we recommend considering fairness metrics when selecting hyperparameters and decision cutoff thresholds.

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The Future of Data Science – Mining GTC 2021 for Trends

Domino Data Lab

Deep learning enthusiasts are increasingly putting NVIDIA’s GTC at the top of their gotta-be-there conference list. Three of them were particularly compelling and inspired a new point of view on transfer learning that I feel is important for analytical practitioners and leaders to understand. Deep Learning Trends from GTC21.