Data Science 101

DECEMBER 27, 2019

AWS Deep Learning Containers now support Tensorflow 2.0 AWS Deep Learning Containers are docker images which are preconfigured for deep learning tasks. It walks through classifying Yahoo Answers into different categories. Here are the few bits of information I could find. Signup for the Newsletter.

Data Science 86

Data Science Deep Learning Data-driven IT 86

Analytics Vidhya

FEBRUARY 22, 2022

Introduction Every supervised machine learning technique basically solves either classification or regression problems. Classification is a kind of technique where we classify the outcome into distinct categories whose range is usually finite. Whereas a regression technique involves predicting a real number whose range is […].

Machine Learning 210

Machine Learning Data Science Publishing Analytics 210

Laminar Security

DECEMBER 13, 2023

Machine learning algorithms can be trained to recognize patterns in the data and classify data accordingly. For example, an AI system could be trained to classify emails into categories like “sensitive” or “restricted” based on patterns it has learned from a training dataset.

Data-driven 119

Data-driven Machine Learning Risk Deep Learning 119

MLWhiz

DECEMBER 16, 2018

With the problem of Image Classification is more or less solved by Deep learning, Text Classification is the next new developing theme in deep learning. How could you use that? To find sentiment of a review.

Deep Learning 85

Deep Learning 85

Cloudera

JANUARY 7, 2021

And in the world of e-commerce, assigning product descriptions to the most fitting product category ensures quality control. . Very few real-world use cases have categories that are eternally set in stone. The reality is that new classes, topics, and categories will likely emerge over time as business needs evolve.

Machine Learning 105

Machine Learning Deep Learning IT 105

CIO Business Intelligence

JUNE 2, 2022

The report, based on survey responses from tech decision makers in banks and their technology vendors, categorizes 30 different technologies into three main categories: “hot,” “on-the-radar,” and “hype.”. RPA, blockchain are on the radar for banks.

Predictive Analytics 142

Predictive Analytics Technology Analytics Machine Learning 142

CIO Business Intelligence

MAY 28, 2022

It is not always the newest technologies that pay the best. Some of the fastest-rising pay premiums during the quarter were for knowledge of the venerable DB2 and Apache Sqoop, a command-line interface for loading Hadoop databases that ceased development last year.

IT 119

IT Prescriptive Analytics Deep Learning Reporting 119

Smart Data Collective

JUNE 4, 2021

Whereas in machine learning, the algorithm understands the data and creates the logic. Learning the various categories of machine learning, associated algorithms, and their performance parameters is the first step of machine learning. Machine learning is broadly classified into three types – Supervised.

Data Science 104

Data Science Statistics Deep Learning Machine Learning 104

CIO Business Intelligence

MAY 26, 2022

It is not always the newest technologies that pay the best. Some of the fastest-rising pay premiums during the quarter were for knowledge of the venerable DB2 and Apache Sqoop, a command-line interface for loading Hadoop databases that ceased development last year.

IT 92

IT Prescriptive Analytics Deep Learning Reporting 92

CIO Business Intelligence

OCTOBER 4, 2023

This volatility can make it hard for IT workers to decide where to focus their career development efforts, but there are at least some areas of stability in the market: despite all other changes in pay premiums, workers with AI skills and security certifications continued to reap rich rewards.

Prescriptive Analytics 126

Prescriptive Analytics IT Machine Learning Risk 126

Cloudera

AUGUST 20, 2020

The imperative to deliver meaningful change and value through innovation is why the Data for Enterprise AI category at the Data Impact Awards has never been more of the moment than it is today. UOB used deep learning to improve detection of procurement fraud, thereby fighting financial crime.

Enterprise 121

Enterprise Experimentation Uncertainty Deep Learning 121

DataRobot

APRIL 6, 2020

If you’re a regular reader of the DataRobot blog, you likely fall into one of two categories.

Deep Learning 69

Deep Learning Forecasting Data Science Interactive 69

Cloudera

OCTOBER 27, 2020

Our goal is to take the incredible data science and machine learning research developments we see emerging from academia and large industrial labs, and bridge the gap to products and processes that are useful to practitioners working across industries. Evolving Research At Cloudera Fast Forward. Structural Time Series.

Deep Learning 59

Deep Learning Machine Learning Forecasting Uncertainty 59

Smart Data Collective

MARCH 16, 2022

In other cases, advanced AI applications use a deep-learning approach to sift through big data to predict the prices of stocks in the near future. However, deep-learning approaches are comprehensive in theory. For instance, real-time car purchases can help predict the price of Rolls Royce shares in the near future.

Big Data 102

Big Data Deep Learning Finance Data-driven 102

Data Science 101

MAY 20, 2019

Evaluation of Linear Regression Evaluation of Classification Positive/negative skew Poisson regression Fisher’s exact test Pearson PCA Deep learning – high-level, what is is for Neural Networks (RNN vs CNN vs DCN vs GAN). There were a number of questions from this category. Azure ML Studio. Azure Products.

Data Science 111

Data Science Machine Learning Deep Learning Testing 111

Smart Data Collective

SEPTEMBER 23, 2020

Type of Data: structured and unstructured from different sources of data Purpose: Cost-efficient big data storage Users: Engineers and scientists Tasks: storing data as well as big data analytics, such as real-time analytics and deep learning Sizes: Store data which might be utilized. Data Warehouse.

Data Lake 105

Data Lake Data Warehouse Unstructured Data Big Data 105

CIO Business Intelligence

FEBRUARY 10, 2023

Predictive analytics definition Predictive analytics is a category of data analytics aimed at making predictions about future outcomes based on historical data and analytics techniques such as statistical modeling and machine learning. Models can be designed, for instance, to discover relationships between various behavior factors.

Predictive Analytics 140

Predictive Analytics Analytics Predictive Modeling Forecasting 140

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. How do artificial intelligence, machine learning, deep learning and neural networks relate to each other?

Deep Learning 59

Deep Learning Machine Learning Unstructured Data Modeling 59

Smart Data Collective

AUGUST 26, 2021

Deep learning technology is changing the future of small businesses around the world. A growing number of small businesses are using deep learning technology to address some of their most pressing challenges. New advances in deep learning are integrated into various accounting algorithms.

Deep Learning 96

Deep Learning Finance Cost-Benefit Advertising 96

Occam's Razor

MARCH 30, 2017

People tend to use these phrases almost interchangeably: Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning. Deep Learning is a specific ML technique. Most Deep Learning methods involve artificial neural networks, modeling how our bran works.

Marketing Analytics 166

Marketing Analytics Marketing Analytics Deep Learning 166

Rocket-Powered Data Science

DECEMBER 16, 2018

It is focused on learning the right tasks to perform and on tuning the modeling hyper-parameters. Workshop on Meta-Learning (MetaLearn 2018). Finally, in addition to the awesome 750-page book “ Encyclopedia of Distances “, please check out some of these top-selling books on Data Science, AI, and Machine Learning :

Machine Learning 203

Machine Learning Metrics Optimization Data Science 203

Smart Data Collective

APRIL 24, 2020

The categories, the tags, patterns, and much more could all be recognized by deep learning algorithms, which is part of the reason why data ingestion is so effective. A good perk to point out about data lakes is that they can use powerful algorithms to help your analysts comprehend the gathered data.

Data Lake 98

Data Lake Data Collection Deep Learning Management 98

Insight

OCTOBER 25, 2019

Problem Statement For this project, my client specified a public dataset ( T-LESS ) of 30 industry-relevant objects and categorized them into seven distinct categories. Among these categories, one consists of only two objects (objects 27 and 29). Meta-Learning v.s. One being the 3-D nature of our images.

Snapshot 57

Snapshot Testing Deep Learning Visualization 57

O'Reilly on Data

APRIL 19, 2021

Unfortunately, we don’t know which industries are represented by that category—but it shows that the spread of AI has indeed become broad! First, 82% of the respondents are using supervised learning, and 67% are using deep learning. 58% claimed to be using unsupervised learning. Industries using AI. Techniques.

Enterprise 356

Enterprise Modeling Risk Manufacturing 356

IBM Big Data Hub

OCTOBER 16, 2023

Filtering email ML algorithms in Google’s Gmail automate filtering customers’ email into Primary, Social and Promotions categories while also detecting and rerouting spam to a spam folder. Reinforcement learning uses ML to train models to identify and respond to cyberattacks and detect intrusions.

Machine Learning 51

Machine Learning Marketing Forecasting Modeling 51

IBM Big Data Hub

AUGUST 28, 2023

Text classification: Useful for tasks like sentiment classification, spam filtering and topic classification, text classification involves categorizing documents into predefined classes or categories. And with advanced software like IBM Watson Assistant , social media data is more powerful than ever.

Data mining 56

Data mining Machine Learning Deep Learning Marketing 56

Rocket-Powered Data Science

DECEMBER 15, 2018

In deep learning, as in typical neural network models, the method by which those adjustments to the model parameters are estimated ( i.e., for each of the edge weights between the network nodes) is called backpropagation.

Machine Learning 171

Machine Learning Optimization Metrics Modeling 171

O'Reilly on Data

JUNE 18, 2019

Even if we boosted the quality of the available data via unification and cleaning, it still might not be enough to power the even more complex analytics and predictions models (often built as a deep learning model). An important paradigm for solving both these problems is the concept of data programming.

Machine Learning 259

Machine Learning Data Quality Statistics Modeling 259

CDW Research Hub

NOVEMBER 12, 2020

CPUs are sufficient for basic AI workloads, but GPUs are more ideally suited for deep learning workloads, which can require multiple large datasets and scalable neural networks. Perhaps one of the biggest issues that falls into this category is not having a full understanding of the scalability requirements.

ROI 79

ROI Deep Learning Machine Learning Business Objectives 79

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 ]. I also applied this model to videos and real-time detection with webcam.

Deep Learning 84

Deep Learning Machine Learning Metadata Data Science 84

DataRobot Blog

FEBRUARY 14, 2022

Model explainability is typically grouped into the categories of Global Explainability and Local Explainability. Because our dataset contains image data, DataRobot used models that contain deep learning based image featurizers. Local Explainability tells you why the model made a certain prediction for an individual row.

Machine Learning 52

Machine Learning Metrics Sales Modeling 52

Domino Data Lab

MARCH 6, 2019

Niels Kasch , cofounder of Miner & Kasch , an AI and Data Science consulting firm, provides insight from a deep learning session that occurred at the Maryland Data Science Conference. Deep Learning on Imagery and Text. Deep Learning on Imagery. So how does representation learning in DL work?

Deep Learning 55

Deep Learning Data Science Machine Learning Modeling 55

CIO Business Intelligence

MAY 16, 2022

Computer vision systems can also collect data on parking availability or allow for enhanced quality of access for certain categories of fans through enhanced recognition capabilities. . Modern data analytics spans a range of technologies, from dedicated analytics platforms and databases to deep learning and artificial intelligence (AI).

Recreation/Entertainment 82

Recreation/Entertainment Broadcasting Cost-Benefit Advertising 82

DMBS2

DECEMBER 12, 2017

The importance of AI and of recent AI advances differs greatly according to application or data category. . Machine learning and AI have little relevance to most traditional transactional apps. The most advanced organizations developing and using those rely on machine learning. Machine vision or autonomous vehicles.

Machine Learning 50

Machine Learning Predictive Modeling Deep Learning Modeling 50

Insight

JANUARY 14, 2020

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. ImageNet contains over 14 million images that fall into over 20,000 categories. Check out the Keras documentation here.

Metrics 58

Metrics Optimization Modeling Testing 58

Cloudera

NOVEMBER 15, 2021

Deep Learning, a subset of AI algorithms, typically requires large amounts of human annotated data to be useful. Here we briefly describe some of the challenges that data poses to AI. Data annotation. Abundance of data has been one of the main facilitators of the AI boom of the last decade.

Unstructured Data 86

Unstructured Data Recreation/Entertainment Structured Data Reporting 86

O'Reilly on Data

MARCH 18, 2020

Supervised learning is the most popular ML technique among mature AI adopters, while deep learning is the most popular technique among organizations that are still evaluating AI. The “Other” category (~22%) comprises 12 separate industries. Supervised learning is dominant, deep learning continues to rise.

Enterprise 309

Enterprise Deep Learning Data Governance Risk 309