Smart Data Collective

JULY 30, 2020

In December, Springer published an insightful article about the value of deep learning for VPNs. The article “Deep learning-based real-time VPN encrypted traffic identification methods” delves into the use of machine learning to improve encryption models. It is also used for testing more effectively.

Machine Learning 120

Machine Learning Testing Deep Learning Advertising 120

Analytics Vidhya

APRIL 6, 2023

From the Turing machine to modern-day AI marvels like ChatGPT, the landscape of AI has evolved to encompass a wide range of applications in […] The post From Turing Test to ChatGPT: The Remarkable Journey of AI appeared first on Analytics Vidhya.

Testing 318

Testing Technology Analytics IT 318

Analytics Vidhya

FEBRUARY 25, 2023

Introduction Often while working on predictive modeling, it is a common observation that most of the time model has good accuracy for the training data and lesser accuracy for the test data.

Predictive Modeling 313

Predictive Modeling Testing Machine Learning Modeling 313

DataKitchen

APRIL 13, 2021

Testing and Data Observability. 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. .

Testing 300

Testing Machine Learning Consulting Data Science 300

Domino Data Lab

MARCH 11, 2021

Machine learning (ML) frameworks are interfaces that allow data scientists and developers to build and deploy machine learning models faster and easier. Machine learning is used in almost every industry, notably finance , insurance , healthcare , and marketing. How to choose the right ML Framework.

Machine Learning 143

Machine Learning Deep Learning Insurance Modeling 143

O'Reilly on Data

MARCH 24, 2020

On the machine learning side, we are entering what Andrei Karpathy, director of AI at Tesla, dubs the Software 2.0 ” One of his more egregious errors was to continually test already collected data for new hypotheses until one stuck, after his initial hypothesis failed [4]. Let’s get everybody to do X.

Machine Learning 364

Machine Learning Statistics Data Quality Data Collection 364

O'Reilly on Data

JUNE 19, 2019

A look at the landscape of tools for building and deploying robust, production-ready machine learning models. Our surveys over the past couple of years have shown growing interest in machine learning (ML) among organizations from diverse industries. Model operations, testing, and monitoring.

Modeling 257

Modeling Machine Learning Testing Metrics 257

Smart Data Collective

NOVEMBER 9, 2022

There are a number of great applications of machine learning. One of the biggest benefits is testing processes for optimal effectiveness. The main purpose of machine learning is to partially or completely replace manual testing. Machine learning is used in many industries. Top ML Companies.

Machine Learning 122

Machine Learning Testing Cost-Benefit Data-driven 122

Cloudera

MAY 19, 2021

In the previous blog post in this series, we walked through the steps for leveraging Deep Learning in your Cloudera Machine Learning (CML) projects. As a machine learning problem, it is a classification task with tabular data, a perfect fit for RAPIDS. Introduction. See < [link] > for more details.

Machine Learning 137

Machine Learning Data Science Data Lake Modeling 137

O'Reilly on Data

MARCH 7, 2019

Language understanding benefits from every part of the fast-improving ABC of software: AI (freely available deep learning libraries like PyText and language models like BERT ), big data (Hadoop, Spark, and Spark NLP ), and cloud (GPU's on demand and NLP-as-a-service from all the major cloud providers). IBM Watson NLU.

Deep Learning 279

Deep Learning Testing Machine Learning Modeling 279

O'Reilly on Data

OCTOBER 19, 2021

Much has been written about struggles of deploying machine learning projects to production. This approach has worked well for software development, so it is reasonable to assume that it could address struggles related to deploying machine learning in production too. An Overarching Concern: Correctness and Testing.

IT 364

IT Testing Experimentation Software 364

Corinium

JUNE 6, 2019

Fractal’s recommendation is to take an incremental, test and learn approach to analytics to fully demonstrate the program value before making larger capital investments. There is usually a steep learning curve in terms of “doing AI right”, which is invaluable. What is the most common mistake people make around data?

Insurance 250

Insurance Analytics Forecasting Deep Learning 250

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 363

Management Machine Learning Experimentation Metrics 363

Domino Data Lab

JULY 10, 2019

New tools are constantly being added to the deep learning ecosystem. For example, there have been multiple promising tools created recently that have Python APIs, are built on top of TensorFlow or PyTorch , and encapsulate deep learning best practices to allow data scientists to speed up research.

Deep Learning 110

Deep Learning Visualization Recreation/Entertainment Data Processing 110

Smart Data Collective

FEBRUARY 23, 2023

Although there are plenty of tech jobs out there at the moment thanks to the tech talent gap and the Great Resignation, for people who want to secure competitive packages and accelerate their software development career with sought-after java jobs , a knowledge of deep learning or AI could help you to stand out from the rest.

Deep Learning 111

Deep Learning Software Risk Testing 111

Insight

MAY 14, 2020

In this post, I demonstrate how deep learning can be used to significantly improve upon earlier methods, with an emphasis on classifying short sequences as being human, viral, or bacterial. As I discovered, deep learning is a powerful tool for short sequence classification and is likely to be useful in many other applications as well.

Deep Learning 98

Deep Learning Statistics Modeling Machine Learning 98

Smart Data Collective

SEPTEMBER 9, 2019

The majority of machine learning and deep learning solutions have focused on fundamental analysis of securities. However, deep learning and other artificial intelligence technologies will also change the future of technical analysis as well. New developments in deep learning with technical analysis.

Deep Learning 92

Deep Learning Forecasting Machine Learning Publishing 92

The Unofficial Google Data Science Blog

NOVEMBER 17, 2020

On the other hand, sophisticated machine learning models are flexible in their form but not easy to control. Introduction Machine learning models often behave unpredictably, as data scientists would be the first to tell you. A more general approach is to learn a Generalized Additive Model (GAM).

Machine Learning 111

Machine Learning Deep Learning Modeling Interactive 111

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

Smart Data Collective

FEBRUARY 24, 2021

However, this ever-evolving machine learning technology might surprise you in this regard. The truth is that machine learning is now capable of writing amazing content. Machine Learning to Write your College Essays. Machine Learning to Write your College Essays.

Machine Learning 103

Machine Learning Deep Learning Strategy Reporting 103

Insight

MAY 16, 2019

It is a high-level, multifaceted field that allows machines to iteratively learn and understand complex representations from images and videos to automate human visual tasks. How Deep Learning scales based on the amount of Data [Copyright: Andrew Ng ]. Transfer Learning?—?YOLO. Precision?—?Recall

Deep Learning 84

Deep Learning Machine Learning Metadata Data Science 84

Smart Data Collective

AUGUST 20, 2021

As we said in the past, big data and machine learning technology can be invaluable in the realm of software development. Machine learning technology has become a lot more important in the app development profession. Machine learning can be surprisingly useful when it comes to monetizing apps.

Machine Learning 90

Machine Learning Data mining Advertising Deep Learning 90

CIO Business Intelligence

FEBRUARY 6, 2025

Think about it: LLMs like GPT-3 are incredibly complex deep learning models trained on massive datasets. Even basic predictive modeling can be done with lightweight machine learning in Python or R. From automating tedious tasks to unlocking insights from unstructured data, the potential seems limitless.

Unstructured Data 115

Unstructured Data Manufacturing Data Governance Sales 115

IBM Big Data Hub

JULY 6, 2023

To keep up with the pace of consumer expectations, companies are relying more heavily on machine learning algorithms to make things easier. How do artificial intelligence, machine learning, deep learning and neural networks relate to each other? Machine learning is a subset of AI.

Deep Learning 56

Deep Learning Machine Learning Unstructured Data Modeling 56

DataRobot

APRIL 3, 2020

At DataRobot, we meticulously test all the models we build on thousands of real-world datasets to ensure they meet our customers high expectations across performance, generalization, and reliability. It also provides powerful automated insights to provide transparency under the hood.

Deep Learning 71

Deep Learning Machine Learning Testing Data Science 71

DataRobot Blog

APRIL 22, 2022

In our previous post , we talked about how red AI means adding computational power to “buy” more accurate models in machine learning , and especially in deep learning. testing every possible combination) Hyperparameter tuning is beneficial to some extent, but the real efficiency gains are in finding the right data.

Machine Learning 98

Machine Learning Measurement Deep Learning Manufacturing 98

CIO Business Intelligence

SEPTEMBER 29, 2023

Most relevant roles for making use of NLP include data scientist , machine learning engineer, software engineer, data analyst , and software developer. TensorFlow Developed by Google as an open-source machine learning framework, TensorFlow is most used to build and train machine learning models and neural networks.

Deep Learning 143

Deep Learning Machine Learning Consulting Modeling 143

Rocket-Powered Data Science

JULY 6, 2023

These AI applications are essentially deep machine learning models that are trained on hundreds of gigabytes of text and that can provide detailed, grammatically correct, and “mostly accurate” text responses to user inputs (questions, requests, or queries, which are called prompts).

Statistics 152

Statistics Deep Learning Machine Learning Enterprise 152

Analytics Vidhya

DECEMBER 26, 2023

In this article, we will explore the origins, background, and implications of this experiment, as well as its relationship with the Symbol Grounding Problem and the Turing Test. We […] The post Exploring the Intricacies of the Chinese Room Experiment in AI appeared first on Analytics Vidhya.

Testing 329

Testing Analytics IT Deep Learning 329

Cloudera

FEBRUARY 16, 2022

In this example, the Machine Learning (ML) model struggles to differentiate between a chihuahua and a muffin. We will learn what it is, why it is important and how Cloudera Machine Learning (CML) is helping organisations tackle this challenge as part of the broader objective of achieving Ethical AI.

Machine Learning 85

Machine Learning Modeling Metadata Recreation/Entertainment 85

Cloudera

DECEMBER 4, 2024

Today, Artificial Intelligence (AI) and Machine Learning (ML) are more crucial than ever for organizations to turn data into a competitive advantage. Users can deploy trained models, including GenAI models or predictive deep learning models, directly to the Cloudera AI Inference service. Why did we build it?

Metrics 73

Metrics Data Processing Machine Learning Deep Learning 73

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

CIO Business Intelligence

APRIL 12, 2024

These roles include data scientist, machine learning engineer, software engineer, research scientist, full-stack developer, deep learning engineer, software architect, and field programmable gate array (FPGA) engineer.

Deep Learning 124

Deep Learning IT Machine Learning IoT 124

Smart Data Collective

JUNE 10, 2022

Machine Learning Engineer. As a machine learning engineer, you would create data funnels and deliver software solutions. As well as designing and building machine learning systems, you could be responsible for running tests and monitoring the functionality and performance of systems. Data Architect.

Data Science 144

Data Science Machine Learning Statistics Business Intelligence 144

CIO Business Intelligence

OCTOBER 13, 2023

Machine learning engineer Machine learning engineers are tasked with transforming business needs into clearly scoped machine learning projects, along with guiding the design and implementation of machine learning solutions.

Deep Learning 143

Deep Learning Machine Learning Modeling Software 143

Analytics Vidhya

JANUARY 2, 2024

Test your knowledge with our A-Z Guide to 110 Key Data Science Terms.Let’s embark on this educational adventure together and uncover the rich tapestry of terms that power the engines of artificial intelligence and analytics. Are you new to Data Science or a seasoned data scientist?

Data Science 306

Data Science Testing Analytics Deep Learning 306

CIO Business Intelligence

APRIL 25, 2022

The data science path you ultimately choose will depend on your skillset and interests, but each career path will require some level of programming, data visualization, statistics, and machine learning knowledge and skills. It offers a bootcamp in data science and machine learning for individuals with experience in Python and coding.

Data Science 145

Data Science Machine Learning Deep Learning Statistics 145