O'Reilly on Data

DECEMBER 12, 2019

For all the excitement about machine learning (ML), there are serious impediments to its widespread adoption. If you’re using Python and deep learning libraries, the CleverHans and Foolbox packages can also help you debug models and find adversarial examples. 2] The Security of Machine Learning. [3]

Machine Learning 285

Machine Learning Modeling Testing Risk Management 285

datapine

MAY 14, 2019

In 2013, less than 0.5% 2) “Deep Learning” by Ian Goodfellow, Yoshua Bengio and Aaron Courville. Best for: This best data science book is especially effective for those looking to enter the data-driven machine learning and deep learning avenues of the field. click for book source**.

Data Science 350

Data Science Machine Learning Big Data Data-driven 350

Domino Data Lab

AUGUST 22, 2019

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.

Deep Learning 111

Deep Learning Modeling Metrics Testing 111

Cloudera

JANUARY 7, 2021

However, collecting annotations for your use case is typically one of the most costly parts of the machine learning life cycle. These emerging categories may not contain enough examples for a traditional machine learning algorithm to learn from, making high-quality classification difficult or prohibitive. .

Machine Learning 107

Machine Learning Deep Learning IT 107

Sisense

MAY 26, 2020

As both words are semantically close to each other, machine learning models can easily understand that “delicious” also refers to the pasta tasting good. Word embedding is a type of word representation that allows words with similar meanings to be understood by machine learning algorithms. Dealing with spelling mistakes.

Unstructured Data 98

Unstructured Data Big Data Testing Machine Learning 98

CIO Business Intelligence

JUNE 24, 2024

Machine learning requires fewer resources, while deep learning and generative AI require massive environments due to their complexity. Taking an energy-efficient facility that we built in 2013, we were able to meet their demanding requirements for high-power density and connectivity with minimal changes to our facility.

Strategy 111

Strategy Machine Learning Technology Deep Learning 111

Domino Data Lab

DECEMBER 1, 2019

In the context of machine learning, we consider data drift 1 to be the change in model input data that leads to a degradation of model performance. A Survey on Concept Drift Adaptation” ACM Computing Survey Volume 1 , Article 1 (January 2013). Detecting image drift. LeCun, Yann; Corinna Cortes; Christopher J.C.

Machine Learning 77

Machine Learning Modeling Deep Learning Testing 77