CIO Business Intelligence

SEPTEMBER 29, 2023

Relevant job roles include machine learning engineer, deep learning engineer, AI research scientist, NLP engineer, data scientists and analysts, AI product manager, AI consultant, AI systems architect, AI ethics and compliance analyst, among others.

Deep Learning 143

Deep Learning Machine Learning Modeling Consulting 143

Domino Data Lab

JANUARY 20, 2019

These are the kind of questions Been Kim , Senior Research Scientist at Google Brain, poised in the MLConf 2018 talk , “Interpretability Beyond Feature Attribution: Testing with Concept Activation Vectors (TCAV)”. Testing with Concept Activation Vectors (TCAV): The Zebra. Would industry people be interested?

Testing 63

Testing Modeling Machine Learning Data Science 63

CIO Business Intelligence

OCTOBER 13, 2023

The role of algorithm engineer requires knowledge of programming languages, testing and debugging, documentation, and of course algorithm design. Deep learning is a subset of AI , and vital to the development of gen AI tools and resources in the enterprise.

Deep Learning 143

Deep Learning Machine Learning Modeling Software 143

IBM Big Data Hub

FEBRUARY 13, 2024

Generative AI represents a significant advancement in deep learning and AI development, with some suggesting it’s a move towards developing “ strong AI.” In addition, Well-developed prompts and inputs direct generative models to output creative content for emails, blogs, social media posts and websites.

Enterprise 125

Enterprise Modeling Testing Data-driven 125

Dataiku

MARCH 25, 2024

In this blog post, we delve into the workings of M-LLMs, unraveling the intricacies of their architecture, with a particular focus on text and vision integration. One limitation observed while testing the LENS approach, particularly in VQA, is its heavy reliance on the output of the first modules, namely CLIP and BLIP captions.

Visualization 132

Visualization Modeling Experimentation Testing 132

IBM Big Data Hub

SEPTEMBER 19, 2023

It uses deep learning algorithms and large neural networks trained on vast datasets of diverse existing source code. Some generative AI for code tools automatically create unit tests to help with this. Generative AI can quickly and efficiently test and debug computer code. How does generative AI code generation work?

Software 89

Software IT Visualization Deep Learning 89

IBM Big Data Hub

JULY 11, 2023

Implementing AI can help recognize unusual or suspicious patterns in insurance claims, such as billing for costly services or procedures not performed, unbundling (which is billing for the individual steps of a procedure as though they were separate procedures), and performing unnecessary tests to take advantage of insurance payments.

Deep Learning 59

Deep Learning Cost-Benefit Insurance Machine Learning 59

IBM Big Data Hub

AUGUST 11, 2023

Machine learning engineers take massive datasets and use statistical methods to create algorithms that are trained to find patterns and uncover key insights in data mining projects. These insights can help drive decisions in business, and advance the design and testing of applications.

Data Science 58

Data Science Machine Learning Cost-Benefit Deep Learning 58

IBM Big Data Hub

JULY 6, 2023

The evolution of machine learning The start of machine learning, and the name itself, came about in the 1950s. In 1950, data scientist Alan Turing proposed what we now call the Turing Test , which asked the question, “Can machines think?” Python is the most common programming language used in machine learning.

Machine Learning 60

Machine Learning Data Science Statistics Deep Learning 60

Dataiku

AUGUST 28, 2020

A few years ago, I scraped a beer rating website, and at the time, I wanted to test different recommendation algorithms. More recently, I was advised to follow this excellent class by Charles Ollion ( Heuritech ) and Olivier Grisel to learn more about some specific aspects of deep learning.

Deep Learning 51

Deep Learning Testing Data Science IT 51

Smart Data Collective

FEBRUARY 20, 2022

With these tools, your SaaS can: Merge and improve the application code constantly Automate the development, testing, and release of software Integrate operations and developer workflows And much more. AWS also offers developers the technology to develop smart apps using machine learning and complex algorithms. Conclusions.

Cost-Benefit 114

Cost-Benefit Data Lake Software Machine Learning 114

IBM Big Data Hub

NOVEMBER 29, 2023

Creating synthetic test data to expedite testing, optimization and validation of new applications and features. Unlock your free trial today The post Synthetic data generation: Building trust by ensuring privacy and quality appeared first on IBM Blog. Check out our synthetic data generator service on watsonx.ai

Metrics 89

Metrics Machine Learning Risk Statistics 89

Domino Data Lab

JULY 17, 2021

While more advanced models for speaker verification exist, this blog will form a basis of speech signal processing. Further, deep learning methods are built on the foundation of signal processing. NOTE : The scope of this blog is a machine learning approach to TISV. hours long from around 60 speakers.

Modeling 64

Modeling Testing Machine Learning Measurement 64

Domino Data Lab

APRIL 7, 2019

A good NLP library will, for example, correctly transform free text sentences into structured features (like cost per hour and is diabetic ), that easily feed into a machine learning (ML) or deep learning (DL) pipeline (like predict monthly cost and classify high risk patients ). Image Credit: Parsa Ghaffari on the Raylien Blog.

Deep Learning 110

Deep Learning Machine Learning Experimentation Modeling 110

CIO Business Intelligence

APRIL 24, 2023

How we got here The most notable enabling technologies in generative AI are deep learning, embeddings, transfer learning (all of which emerged in the early to mid-2000s), and neural net transformers (invented in 2017). Deep learning emerged in academia in the early 2000s, with broader industry adoption starting around 2010.

Enterprise 52

Enterprise Deep Learning Modeling Machine Learning 52

Domino Data Lab

OCTOBER 7, 2020

MANOVA, for example, can test if the heights and weights in boys and girls is different. This statistical test is correct because the data are (presumably) bivariate normal. In this blog we show what the changes in behavior of data are in high dimensions. Statistical methods for analyzing this two-dimensional data exist.

Statistics 145

Statistics Testing Predictive Modeling Modeling 145

Domino Data Lab

JULY 28, 2021

To model anything highly technical and computationally — machine learning, deep learning, big data analytics, and natural-language processing, to name a few — code-based tools (such as R and Python) are usually preferred. After cleaning, the data is now ready for processing.

Data Science 110

Data Science Statistics Recreation/Entertainment Predictive Analytics 110

IBM Big Data Hub

SEPTEMBER 6, 2023

AI used for content generation can save marketing teams time and money by creating blogs, marketing messages, copywriting materials, emails, subject lines, subtitles for videos, website copy and many other kinds of content aimed at a target audience.

Marketing 86

Marketing Technology Advertising ROI 86

IBM Big Data Hub

JULY 6, 2023

While artificial intelligence (AI), machine learning (ML), deep learning and neural networks are related technologies, the terms are often used interchangeably, which frequently leads to confusion about their differences. This blog post will clarify some of the ambiguity. Machine learning is a subset of AI.

Deep Learning 61

Deep Learning Machine Learning Unstructured Data Modeling 61

CIO Business Intelligence

JUNE 2, 2022

Specifically, they are interested in electric utility response to cyber and physical threats, and they are working to develop an algorithm that can be used as a tested, trusted safeguard. Modern data analytics spans a range of technologies, from dedicated analytics platforms and databases to deep learning and artificial intelligence (AI).

Informatics 98

Informatics Modeling Testing Technology 98

Insight

OCTOBER 25, 2019

Image credit: [link] As the title suggests, this is a story about a question that may resonate well with many machine learning practitioners trying to build applications in the real world, where clean and annotated data on a specific problem can be sparse— How do we leverage the power of AI when we have very little data? not just images?—?unseen

Snapshot 57

Snapshot Testing Deep Learning Visualization 57

Smarten

JUNE 14, 2023

While it has only recently received public attention, this deep learning model architecture was launched more than five years ago by The American Artificial Intelligence Organization (OpenAI), and recent innovations like ChatGPT are based on the latest version of GPT (GPT-4).

Optimization 52

Optimization IT Digital Transformation Consulting 52

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. appeared first on Cloudera Blog. The post Introducing the 2019 Data Heroes – EMEA!

Data Architecture 69

Data Architecture Deep Learning Machine Learning Big Data 69

IBM Big Data Hub

APRIL 18, 2024

Building an in-house team with AI, deep learning , machine learning (ML) and data science skills is a strategic move. The doctor uploads the patient’s medical history and recent test results to an AGI-powered medical analysis system. Example : A patient visits a doctor with concerning symptoms.

Cost-Benefit 96

Cost-Benefit Modeling Manufacturing Interactive 96

Cloudera

FEBRUARY 16, 2022

They define each stage from data ingest, feature engineering, model building, testing, deployment and validation. Figure 04: Applied Machine Learning Prototypes (AMPs). Given the complexity of some ML models, especially those based on Deep Learning (DL) Convolutional Neural Networks (CNNs), there are limits to interpretability.

Machine Learning 82

Machine Learning Modeling Metadata Recreation/Entertainment 82

DataRobot Blog

MAY 26, 2022

These methods provided the benefit of being supported by rich literature on the relevant statistical tests to confirm the model’s validity—if a validator wanted to confirm that the input predictors of a regression model were indeed relevant to the response, they need only to construct a hypothesis test to validate the input.

Risk 52

Risk Modeling Metrics Business Objectives 52

Insight

APRIL 9, 2019

NASA [Public domain] In this blog, I’ll discuss how I worked collaboratively with various domain experts, using reinforcement learning to develop innovative solutions in rocket engine development. The biggest categories of cost for hardware designers and manufacturers are testing, verification, and calibration of their control systems.

Manufacturing 75

Manufacturing Machine Learning Testing Software 75

IBM Big Data Hub

FEBRUARY 23, 2024

Machine learning (ML) and deep learning (DL) form the foundation of conversational AI development. Thoroughly testing the AI system before full deployment is critical. Experience the power of watsonx Assistant The post Conversational AI use cases for enterprises appeared first on IBM Blog.

Enterprise 106

Enterprise Interactive Data-driven Key Performance Indicator 106

Domino Data Lab

JULY 28, 2021

Today, the most advanced techniques used in data science are grouped under the term Artificial Intelligence (AI) Due to their information-acquiring nature, machine learning, deep learning, natural language processing (NLP) and computer vision are all considered branches within the field of AI. None of these techniques are new.

Data Science 45

Data Science Machine Learning Statistics Deep Learning 45

IBM Big Data Hub

OCTOBER 16, 2023

Reinforcement learning uses ML to train models to identify and respond to cyberattacks and detect intrusions. Machine learning in financial transactions ML and deep learning are widely used in banking, for example, in fraud detection.

Machine Learning 52

Machine Learning Marketing Forecasting Modeling 52

Insight

MAY 7, 2019

Here is another great blog post on BERT by a former Insight Fellow. BERT uses a deep bi-directional model using transformers. This API also takes an S3 bucket name as input and then performs inference on all inputs in the test file. The platform was tested on an Amazon p2.xlarge Batch Inference ?—?This

Modeling 75

Modeling Testing Machine Learning Interactive 75

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. These deep learning models can analyze large volumes of text and provide things like text summarization, language translation, context modeling, and sentiment analysis.

Deep Learning 49

Deep Learning Machine Learning Testing Modeling 49

IBM Big Data Hub

FEBRUARY 14, 2024

AI platforms can use machine learning and deep learning to spot suspicious or anomalous transactions. A manual approach to development and testing could lead to calculation errors and require a huge volume of resources.

Cost-Benefit 88

Cost-Benefit Insurance Unstructured Data Machine Learning 88

DataKitchen

APRIL 13, 2021

Read the complete blog below for a more detailed description of the vendors and their capabilities. Testing and Data Observability. It orchestrates complex pipelines, toolchains, and tests across teams, locations, and data centers. Testing and Data Observability. Production Monitoring and Development Testing.

Testing 307

Testing Machine Learning Consulting Data Quality 307

Cloudera

OCTOBER 17, 2017

Deep learning is in the news. But deep learning is a tool that enterprises use to solve practical problems. In this blog, we provide a few examples that show how organizations put deep learning to work. In this blog, we provide a few examples that show how organizations put deep learning to work.

Deep Learning 47

Deep Learning Data Science Machine Learning Modeling 47

Cloudera

SEPTEMBER 11, 2020

Due to COVID-19, retailers world-wide had their e-commerce capabilities put to the test with entire countries being in lockdown for weeks, even months, due to the pandemic. . From e-commerce and direct-to-consumer all the way to fulfillment, we’re witnessing retail execution capabilities being put to the test in real-time. .

Manufacturing 87

Manufacturing Forecasting Digital Transformation Sales 87

Domino Data Lab

AUGUST 9, 2021

With the right tools, your data science teams can focus on what they do best – testing, developing and deploying new models while driving forward-thinking innovation. Before selecting a tool, you should first know your end goal – machine learning or deep learning. This is no exaggeration by any means.

Modeling 111

Modeling Deep Learning Machine Learning Statistics 111