Smart Data Collective

FEBRUARY 14, 2021

New SaaS businesses have discovered that data analytics is important for facilitating many aspects of their models. Outline Your Product with Deep Learning Modeling. Deep learning tools can make it easier to model these products. Many design tools have helped with design modeling for years.

Data-driven 87

Data-driven Big Data Deep Learning Software 87

Cloudera

SEPTEMBER 20, 2022

The state of the art in AI systems for artistic tasks almost universally use deep-learning models, which presuppose a significant amount of compute resources both to create them, and once created to continue to use them for producing images. The content of these data sets may introduce problematic bias into a model’s results.

Deep Learning 89

Deep Learning Cost-Benefit Risk Data-driven 89

Insight

OCTOBER 25, 2019

My client also specified that CAD model files of the T-LESS dataset be used for this project, and that one object per class be reserved for testing (Objects 4, 8, 12, 18, 23, 30). The latter is particularly restricting, as it violates the prerequisite of many deep learning methods for image classification?—?a not just images?—?unseen

Snapshot 57

Snapshot Testing Deep Learning Visualization 57

Insight

JANUARY 14, 2020

a “pre-trained” model) to be recycled and reused for many different tasks. Keras is an open source deep learning API that was written in Python and runs on top of Tensorflow, so it’s a little more user-friendly and high-level than Tensorflow. We pass 3 parameters: loss, optimizer , and metrics.

Metrics 58

Metrics Optimization Modeling Testing 58

Domino Data Lab

AUGUST 10, 2021

Models are the central output of data science, and they have tremendous power to transform companies, industries, and society. At the center of every machine learning or artificial intelligence application is the ML/AI model that is built with data, algorithms and code. The process of creating models is called modeling.

Modeling 64

Modeling IT Risk Management Deep Learning 64

Rocket-Powered Data Science

JULY 6, 2021

2) MLOps became the expected norm in machine learning and data science projects. MLOps takes the modeling, algorithms, and data wrangling out of the experimental “one off” phase and moves the best models into deployment and sustained operational phase.

Data-driven 279

Data-driven Machine Learning IoT Deep Learning 279

Smarten

APRIL 19, 2024

The use of Generative AI, LLM and products such as ChatGPT capabilities has been applied to all kinds of industries, from publishing and research to targeted marketing and healthcare. OpenAI – Azure OpenAI as the foundational entity for creating GPT models and is based on Large Language Models (LLM). in next several years.

Analytics 57

Analytics Predictive Modeling KPI Machine Learning 57

Domino Data Lab

JULY 22, 2019

why data governance, in the context of machine learning is no longer a “dry topic” and how the WSJ’s “global reckoning on data governance” is potentially connected to “premiums on leveraging data science teams for novel business cases”. What can we learn from that? Key highlights from the session include. Transcript.

Data Science 109

Data Science Machine Learning Data Governance Modeling 109

Insight

MARCH 25, 2020

After reading this, I hope you can learn how to build deep learning models using TensorFlow Keras, productionalize the model as a Streamlit app, and deploy it as a Docker container on Google Cloud Platform (GCP) using Google Kubernetes Engines (GKE). specifically convolutional neural networks (CNN)?—?can

Deep Learning 53

Deep Learning Metrics Data Processing Interactive 53

Insight

MARCH 4, 2020

In this blog, I’ll describe how to solve this with Contextual Topic Identification, leveraging machine learning methods to identify semantically similar groups and surface relevant category tags of the reviews. Steam review screenshot Dataset I used the Steam Review Dataset , published on Kaggle as a .csv

Visualization 81

Visualization Modeling Metrics Measurement 81

Domino Data Lab

MARCH 3, 2019

We had big surprises at several turns and have subsequently published a series of reports. O’Reilly Media published our analysis as free mini-books: The State of Machine Learning Adoption in the Enterprise (Aug 2018). The first survey started as a simple exploration into mainstream adoption of machine learning (ML).

Data Science 49

Data Science Deep Learning Machine Learning Modeling 49

DataRobot

NOVEMBER 9, 2021

“One look is worth a Thousand Words” This phrase was used in 1913 to convey that graphics had a place in newspaper publishing. There are some interesting insights in adding unstructured features to the models. For adding image based features on the models, we use the Pyts library, which provides functions to generate GAF matrices.

Visualization 52

Visualization Unstructured Data Statistics Metrics 52

Insight

NOVEMBER 5, 2019

Such a model would be able to answer the difficult questions above. In order to train the model, positive and negative patients need to be identified, and timepoints for those events need to be saved for later extraction of features. So how did the composite model perform? At every timepoint, the composite model performs best.

Deep Learning 61

Deep Learning Modeling Insurance Metrics 61

Domino Data Lab

MAY 8, 2019

Paco Nathan’s latest article features several emerging threads adjacent to model interpretability. I’ve been out themespotting and this month’s article features several emerging threads adjacent to the interpretability of machine learning models. Machine learning model interpretability. Introduction.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

Insight

MARCH 28, 2019

These labeled sentences were then used to train a pair of custom word2vec models (one for Glassdoor pro reviews, and one for cons) that converted each of the remaining sentences to vector representations that encoded the meaning of the sentence. After his time at Insight, John was hired by Deep Macro as a data scientist.

Dashboards 70

Dashboards Visualization Data Science Machine Learning 70

O'Reilly on Data

JANUARY 25, 2021

This study is based on title usage on O’Reilly online learning. The data includes all usage of our platform, not just content that O’Reilly has published, and certainly not just books. Models are neither code nor data, and we don’t have adequate tools for versioning models either (though tools like MLflow are a start).

Machine Learning 363

Machine Learning Software Testing Technology 363