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Scaling False Peaks

O'Reilly on Data

In the 1950s, machine translation of Russian into English was considered to be no more complex than dictionary lookups and templated phrases. The claim is that AGI is now simply a matter of improving performance, both in hardware and software, and making models bigger, using more data and more kinds of data across more modes.

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Build efficient, cross-Regional, I/O-intensive workloads with Dask on AWS

AWS Big Data

Welcome to the era of data. The sheer volume of data captured daily continues to grow, calling for platforms and solutions to evolve. The Amazon Sustainability Data Initiative (ASDI) uses the capabilities of Amazon S3 to provide a no-cost solution for you to store and share climate science workloads across the globe.

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How to supercharge data exploration with Pandas Profiling

Domino Data Lab

Producing insights from raw data is a time-consuming process. The Importance of Exploratory Analytics in the Data Science Lifecycle. Exploratory analysis is a critical component of the data science lifecycle. For one, Python remains the leading language for data science research. ref: [link].

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A Picture Paints a Thousand Numbers

Peter James Thomas

The recent update of The Data & Analytics Dictionary featured an entry on Charts. Entries in The Dictionary are intended to be relatively brief [1] and also the layout does not allow for many illustrations. Bubble Charts are used to display three dimensions of data on a two dimensional chart. Introduction.

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Deep Learning Illustrated: Building Natural Language Processing Models

Domino Data Lab

Data scientists and researchers require an extensive array of techniques, packages, and tools to accelerate core work flow tasks including prepping, processing, and analyzing data. Utilizing NLP helps researchers and data scientists complete core tasks faster. Preprocessing Natural Language Data. Example 11.4

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How to Easily Understand Your Python Objects

Insight

I frequently run into this issue in my data science workflow with complex objects in libraries, like TensorFlow. kwonlydefaults is a dictionary with keyword-only arg default values. annotations is a dictionary specifying any type annotations. args contains the argument names. kwonlyargs lists names of keyword-only args.

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Manual Feature Engineering

Domino Data Lab

Many thanks to AWP Pearson for the permission to excerpt “Manual Feature Engineering: Manipulating Data for Fun and Profit” from the book, Machine Learning with Python for Everyone by Mark E. Feature engineering is useful for data scientists when assessing tradeoff decisions regarding the impact of their ML models.

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