Data Science 101

SEPTEMBER 24, 2019

Is the list missing a project released in 2019? Open Source Data Science Projects. If so, please leave a comment.

Data Science 104

Data Science Deep Learning Machine Learning Visualization 104

O'Reilly on Data

JANUARY 7, 2020

In fact, in our 2019 surveys, more than half of the respondents said AI (deep learning, specifically) will be part of their future projects and products—and a majority of companies are starting to adopt machine learning. To stay competitive, data scientists need to at least dabble in machine and deep learning.

Deep Learning 177

Deep Learning Machine Learning Experimentation Modeling 177

DataKitchen

APRIL 13, 2021

We have also included vendors for the specific use cases of ModelOps, MLOps, DataGovOps and DataSecOps which apply DataOps principles to machine learning, AI, data governance, and data security operations. . Dagster / ElementL — A data orchestrator for machine learning, analytics, and ETL. . Meta-Orchestration .

Testing 300

Testing Machine Learning Consulting Data Quality 300

Cloudera

MARCH 13, 2019

A Data Scientist : Organizations who show how they improved analytics, delivered new actionable intelligence, or designed systems for distributed deep learning and artificial intelligence to the organization’s business and customers. Stay tuned for March 19, 2019 as the winners are unveiled at the Luminaries dinner in Barcelona.

Data Architecture 69

Data Architecture Deep Learning Machine Learning Big Data 69

Insight

SEPTEMBER 20, 2019

The model was a modified U-Net and trained on GPU hosted by Amazon Web Services (AWS) EC2 instances. The loss function used is illustrated in the figure below, with “A” representing the ground truth (manually labeled mask) and “B” representing the model generated mask. Here, we built a model to mimic this process. on test data.

Deep Learning 53

Deep Learning Cost-Benefit Testing Modeling 53

O'Reilly on Data

MARCH 31, 2020

If you’re already a software product manager (PM), you have a head start on becoming a PM for artificial intelligence (AI) or machine learning (ML). AI products are automated systems that collect and learn from data to make user-facing decisions. We won’t go into the mathematics or engineering of modern machine learning here.

Management 327

Management Machine Learning Experimentation Metrics 327

O'Reilly on Data

FEBRUARY 18, 2020

This year’s growth in Python usage was buoyed by its increasing popularity among data scientists and machine learning (ML) and artificial intelligence (AI) engineers. Growth is still strong for such a large topic, but usage slowed in 2018 (+13%) and cooled significantly in 2019, growing by just 7%.

Data-driven 331

Data-driven Software Statistics Marketing 331

CIO Business Intelligence

APRIL 25, 2023

A transformer is a type of AI deep learning model that was first introduced by Google in a research paper in 2017. Five years later, transformer architecture has evolved to create powerful models such as ChatGPT. Meanwhile, however, many other labs have been developing their own generative AI models.

Experimentation 106

Experimentation Enterprise Deep Learning Modeling 106

O'Reilly on Data

JANUARY 25, 2021

Year-over-year (YOY) growth compares January through September 2020 with the same months of 2019. Let’s look at the data, starting at the highest level: O’Reilly online learning itself. O’Reilly Online Learning. Usage of O’Reilly online learning grew steadily in 2020, with 24% growth since 2019.

Machine Learning 362

Machine Learning Software Testing Technology 362

Corinium

JUNE 6, 2019

Beyond that, we recommend setting up the appropriate data management and engineering framework including infrastructure, harmonization, governance, toolset strategy, automation, and operating model. It is also important to have a strong test and learn culture to encourage rapid experimentation. What differentiates Fractal Analytics?

Insurance 250

Insurance Analytics Forecasting Deep Learning 250

CIO Business Intelligence

APRIL 15, 2022

And 20% of IT leaders say machine learning/artificial intelligence will drive the most IT investment. Insights gained from analytics and actions driven by machine learning algorithms can give organizations a competitive advantage, but mistakes can be costly in terms of reputation, revenue, or even lives.

Analytics 144

Analytics Machine Learning Insurance Testing 144

Cloudera

OCTOBER 1, 2022

Some organizations are choosing to confront these challenges with the help of tools like machine learning (ML) and artificial intelligence (AI) to automate, streamline, and scale compliance. . It is pretty impressive just how much has changed in the enterprise machine learning and AI landscape. Infrastructure.

Data Governance 73

Data Governance Strategy Enterprise Machine Learning 73

Domino Data Lab

OCTOBER 9, 2019

Overall, these public datasets provide a great resource for when you need interesting data to use in machine learning tutorials, product demos, etc. We had working sessions about how to retrofit the saildrones with low-power ML on edge devices, using transfer learning with deep learning models to detect and avoid hazards from sea ice.

Deep Learning 69

Deep Learning Metadata Machine Learning Data Science 69

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.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

CIO Business Intelligence

JUNE 7, 2022

Those numbers represent the projected growth of chatbot interactions among banking customers between 2019 to 2023 and the cost savings from 862 hours less of work by support personnel, according to research by Juniper Research. In business, when a trend is forecast to grow by more than 3000% and generate cost savings of $7.3

Unstructured Data 87

Unstructured Data Deep Learning Insurance Interactive 87

Domino Data Lab

JULY 2, 2019

Paco Nathan ‘s latest article covers program synthesis, AutoPandas, model-driven data queries, and more. Doesn’t this seem like a worthy goal for machine learning—to make the machines learn to work more effectively? Introduction. Welcome back to our monthly burst of themespotting and conference summaries.

Metadata 105

Metadata Machine Learning Data Science Data-driven 105

KDnuggets

OCTOBER 16, 2019

This week on KDnuggets: Beyond Word Embedding: Key Ideas in Document Embedding; The problem with metrics is a big problem for AI; Activation maps for deep learning models in a few lines of code; There is No Such Thing as a Free Lunch; 8 Paths to Getting a Machine Learning Job Interview; and much, much more.

Metrics 52

Metrics Deep Learning Machine Learning Modeling 52

KDnuggets

DECEMBER 2, 2019

The latest release of MobileNets incorporates AutoML and other novel ideas in mobile deep learning.

Deep Learning 101

Deep Learning Modeling Machine Learning 101

datapine

NOVEMBER 19, 2019

While we’ve seen traces of this in 2019, it’s in 2020 that computer vision will make a significant mark in both the consumer and business world. Already in our shortlist of tech buzzwords 2019, artificial intelligence is on the front scene for next year again. Artificial Intelligence (AI). Connected Retail. Hyperautomation.

Technology 279

Technology Internet of Things IT IoT 279

Domino Data Lab

SEPTEMBER 9, 2019

Data science teams in industry must work with lots of text, one of the top four categories of data used in machine learning. We have configured the default Compute Environment in Domino to include all of the packages, libraries, models, and data you’ll need for this tutorial. Getting Started.

Deep Learning 110

Deep Learning Machine Learning Data Science Visualization 110

Domino Data Lab

MARCH 3, 2019

Paco Nathan covers recent research on data infrastructure as well as adoption of machine learning and AI in the enterprise. O’Reilly Media published our analysis as free mini-books: The State of Machine Learning Adoption in the Enterprise (Aug 2018). What’s been the impact of using ML models on culture and organization?

Data Science 49

Data Science Deep Learning Machine Learning Modeling 49

KDnuggets

NOVEMBER 13, 2019

While the revolution of deep learning now impacts our daily lives, these networks are expensive. Approaches in transfer learning promise to ease this burden by enabling the re-use of trained models -- and this hands-on tutorial will walk you through a transfer learning technique you can run on your laptop.

Deep Learning 86

Deep Learning Modeling Machine Learning 86

CIO Business Intelligence

MAY 1, 2024

ChatGPT e le hallucination sui casi giudiziari I progressi compiuti nel 2023 dai large language models (LLM) hanno suscitato un interesse diffuso per il potenziale di trasformazione dell’IA generativa in quasi tutti i settori. Artificial Intelligence, Generative AI, Machine Learning Il problema?

Software 93

Software Deep Learning Machine Learning Modeling 93

KDnuggets

AUGUST 5, 2019

Generative Adversarial Networks are driving important new technologies in deep learning methods. With so much to learn, these two videos will help you jump into your exploration with GANs and the mathematics behind the modelling.

Deep Learning 85

Deep Learning Modeling Technology Machine Learning 85

CDW Research Hub

JULY 31, 2019

Recent advances in machine learning, and more specifically its subset, deep learning, have made it possible for computers to better understand natural language. First, we feed an NLU model with labeled data that provides the list of known intents and example sentences that correspond to those intents. goodbye.

Deep Learning 49

Deep Learning Machine Learning Testing Modeling 49

BizAcuity

MARCH 31, 2022

of organizations who participated in an executive survey back in 2019 claimed they are going to be investing in big data and AI. For accurate predictions, companies now use various data models, machine and deep learning techniques to continuously improve and refine the quality of the outcome. Source: TCS).

Data Analytics 52

Data Analytics Analytics Prescriptive Analytics Predictive Analytics 52

FineReport

DECEMBER 19, 2019

If you want to learn more about self-service BI tools, you can take a look at this review: 5 Most Popular Business Intelligence (BI) Tools in 2019 , to understand your own needs and then choose the tool that is right for you. Of course, other BI tools such as Power BI and Qlikview also have their own advantages. From Google.

Knowledge Discovery 98

Knowledge Discovery Visualization Experimentation Reporting 98

Smart Data Collective

OCTOBER 14, 2019

Ludwig is a tool that allows people to build data-based deep learning models to make predictions. Ludwig is a TensorFlow-based toolbox that aims to allow people to use machine learning during their data work without having extensive prior knowledge. Here are some open-source options to consider. Apache Drill.

Data Science 67

Data Science Machine Learning Big Data Interactive 67

Cloudera

NOVEMBER 15, 2021

In other words, structured data has a pre-defined data model , whereas unstructured data doesn’t. . It facilitates AI because, to be useful, many AI models require large amounts of data for training. Deep Learning, a subset of AI algorithms, typically requires large amounts of human annotated data to be useful.

Unstructured Data 88

Unstructured Data Recreation/Entertainment Structured Data Reporting 88

O'Reilly on Data

SEPTEMBER 11, 2019

Machine learning, artificial intelligence, data engineering, and architecture are driving the data space. The Strata Data Conferences helped chronicle the birth of big data, as well as the emergence of data science, streaming, and machine learning (ML) as disruptive phenomena. An ML-related topic, “models,” was No.

IoT 20

IoT Big Data Data Warehouse Uncertainty 20

Domino Data Lab

APRIL 3, 2019

Data governance shows up as the fourth-most-popular kind of solution that enterprise teams were adopting or evaluating during 2019. Plus, the more mature machine learning (ML) practices place greater emphasis on these kinds of solutions than the less experienced organizations. Those days are long gone if they ever existed.

Data Governance 84

Data Governance Machine Learning Metadata Big Data 84