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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.

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AI In Analytics: Today and Tomorrow!

Smarten

Benefits include customized and optimized models, data, parameters and tuning. This approach does demand skills, data curation, and significant funding, but it will serve the market for third-party, specialized models. This technology can be a valuable tool to automate functions and to generate ideas.

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Explaining black-box models using attribute importance, PDPs, and LIME

Domino Data Lab

The interest in interpretation of machine learning has been rapidly accelerating in the last decade. This can be attributed to the popularity that machine learning algorithms, and more specifically deep learning, has been gaining in various domains. Methods for explaining Deep Learning.

Modeling 139
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Data science vs. machine learning: What’s the difference?

IBM Big Data Hub

Other challenges include communicating results to non-technical stakeholders, ensuring data security, enabling efficient collaboration between data scientists and data engineers, and determining appropriate key performance indicator (KPI) metrics. Python is the most common programming language used in machine learning.

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Leveraging user-generated social media content with text-mining examples

IBM Big Data Hub

Text representation In this stage, you’ll assign the data numerical values so it can be processed by machine learning (ML) algorithms, which will create a predictive model from the training inputs. If necessary, make adjustments to the preprocessing, representation and/or modeling steps to improve the results.

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10 everyday machine learning use cases

IBM Big Data Hub

Machine learning in marketing and sales According to Forbes , marketing and sales teams prioritize AI and ML more than any other enterprise department. Marketers use ML for lead generation, data analytics, online searches and search engine optimization (SEO). Then, it can tailor marketing materials to match those interests.

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Of Muffins and Machine Learning Models

Cloudera

By logging the performance of every combination of search parameters within an experiment, we can choose the optimal set of parameters when building a model. The greater our understanding of how a model works, the better we are able to predict what the output will be for a range of inputs or changes to the model’s parameters.