Analytics Vidhya

JANUARY 5, 2022

Objective This blog post will learn how to use the Hugging face transformers functions to perform prolonged Natural Language Processing tasks. Prerequisites Knowledge of Deep Learning and Natural Language Processing (NLP) Introduction Transformers was introduced in the paper Attention is all you need; it is […].

Deep Learning 299

Deep Learning Data Science Publishing Analytics 299

Analytics Vidhya

NOVEMBER 12, 2020

Introduction In this short article, I will talk about unsupervised learning especially in the energy domain. The blog would mainly focus on the application. The post Deep Unsupervised Learning in Energy Sector – Autoencoders in Action appeared first on Analytics Vidhya.

Analytics 301

Analytics Deep Learning 301

Smart Data Collective

JANUARY 20, 2020

It goes without saying that blogging has slowly and steadily evolved into an indispensable marketing tool. While marketers have been continually using the best possible strategies to improve the existing global blogging landscape, the inclusion of artificial intelligence has taken the ballgame to a whole different level.

Deep Learning 99

Deep Learning ROI Data-driven Marketing 99

Cloudera

MARCH 6, 2024

Add a Human To The Loop: An Introduction to RLHF & DPO. With a deep level of expertise and understanding when it comes to tapping into AI, the conference presents a unique chance to highlight learnings from those who have reached those goals successfully.

Data Analytics 103

Data Analytics Analytics Snapshot Data Processing 103

DataKitchen

FEBRUARY 22, 2024

Introduction to Advanced Finger-Pointing In this foundational course, students will learn the subtle nuances of directing attention away from themselves with the grace of a ballet dancer avoiding a puddle. Learn five easy ways to say ‘Garbage In, Garbage Out’ in calm, soothing business terms.

Analytics 190

Analytics Data Governance Strategy Testing 190

datapine

MAY 14, 2019

By acquiring a deep working understanding of data science and its many business intelligence branches, you stand to gain an all-important competitive edge that will help to position your business as a leader in its field. 2) “Deep Learning” by Ian Goodfellow, Yoshua Bengio and Aaron Courville.

Data Science 350

Data Science Machine Learning Data-driven Big Data 350

Insight

AUGUST 16, 2019

see below for the top 10 blog posts/resources! Getting Started with Data Engineering This blog post by Richard Taylor starts with a discussion of what big data and data engineering really mean before delving into an overview of the current landscape. What is Data Engineering and what is the role of a Data Engineer?

Big Data 51

Big Data Data Science Data Processing Machine Learning 51

Domino Data Lab

JULY 10, 2019

Introduction. 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

Data Science 101

AUGUST 27, 2023

Our front-end is (deep breath) written in Ember. I once had a former co-worker give me a quick introduction to Unity back in the day and I was blown away. Use the GitHub documentation to learn how to use Git and GitHub. I find the best way to learn development and programming is to just do it. These can be Websites?

Software 98

Software Enterprise IT 98

Dataiku

NOVEMBER 9, 2020

They are (rightfully) getting the attention of a big portion of the deep learning community and researchers in Natural Language Processing (NLP) since their introduction in 2017 by the Google Translation Team.

Deep Learning 90

Deep Learning Modeling 90

Cloudera

APRIL 19, 2021

Introduction. In our previous blog post in this series , we explored the benefits of using GPUs for data science workflows, and demonstrated how to set up sessions in Cloudera Machine Learning (CML) to access NVIDIA GPUs for accelerating Machine Learning Projects. pip install scikit-learn pandas.

Deep Learning 70

Deep Learning Machine Learning Data Science Modeling 70

IBM Big Data Hub

DECEMBER 4, 2023

So, why do millions of small enterprises believe that impactful AI is only accessible to big companies with deep pockets?   Learn how IBM can help your company overcome the myths surrounding AI adoption AI adoption doesn’t have to be complicated or costly.

Enterprise 81

Enterprise Risk Modeling Technology 81

Cloudera

SEPTEMBER 20, 2022

Introduction. This blog is intended to serve as an ethics sheet for the task of AI-assisted comic book art generation, inspired by “ Ethics Sheets for AI Tasks.” AI-assisted comic book art generation is a task I proposed in a blog post I authored on behalf of my employer, Cloudera. Access — who can use it?

Deep Learning 84

Deep Learning Cost-Benefit Risk Data-driven 84

Cloudera

NOVEMBER 25, 2021

With the introduction of ML and Deep Learning (DL), it is now possible to build AI systems that have no ethical considerations at all. In part 2 of this blog post, we explore the challenges in ensuring ethical AI systems and some ways that these can be overcome. So why is it so hard to build ethical systems?

Insurance 92

Insurance Machine Learning Deep Learning Reporting 92

Ontotext

DECEMBER 15, 2023

Introduction InnoGraph will build a holistic knowledge graph of innovation based on Artificial Intelligence (AI), and more generally of the global “hitech” ecosystem. The official (first) repo is tensorflow/tensor2tensor that has topics: machine-learning reinforcement-learning deep-learning machine-translation tpu.

Machine Learning 69

Machine Learning Interactive Deep Learning Statistics 69

Sisense

APRIL 2, 2020

Tools of the Trade is your destination for data and analytics skill building: From dashboards and reports to embedding analytics and building custom analytic apps to SQL secrets and data deep-dives, whatever you need to know to be better at your job, you can find it here. Get an in-depth introduction to this important process.

Data Warehouse 83

Data Warehouse Big Data Testing Software 83

IBM Big Data Hub

JANUARY 26, 2024

Maintenance workers need to consume a lot of energy to check up the equipment data to understand and learn new skills, resulting in not only low maintenance efficiency, but also leading to a decline in employee satisfaction and loyalty.

Cost-Benefit 77

Cost-Benefit Enterprise Technology KPI 77

Alation

SEPTEMBER 7, 2021

This is the last of the 4-part blog series. In the previous blog , we discussed how Alation provides a platform for data scientists and analysts to complete projects and analysis at speed. In this blog we will discuss how Alation helps minimize risk with active data governance. Find Trusted Data.

Data Governance 59

Data Governance Risk Data Quality Dashboards 59

Domino Data Lab

JANUARY 20, 2019

Introduction. What if there was a way to quantitatively measure whether your machine learning (ML) model reflects specific domain expertise or potential bias? TCAV provides quantitative importance of a concept if and only if your network has learned about it”. with post-training explanations? Conclusion: Why Consider TCAV?

Testing 63

Testing Modeling Machine Learning Data Science 63

datapine

JULY 17, 2019

Looking for a bite-sized introduction to reporting? They are customizable and thus offer a powerful means of drilling down deep into very specific pockets of information. Also, explore our guide to KPI management and learn from a host of helpful best practices. Looking for a bite-sized introduction to reporting?

KPI 223

KPI Reporting Key Performance Indicator Dashboards 223

IBM Big Data Hub

JUNE 23, 2023

IBM’s hybrid multicloud approach—when combined with the best-in-class security and compliance control features enabled for IBM Industry Cloud Platforms workloads and our deep industry expertise in mission-critical processes—offers a compelling value proposition to large insurers in all geographies.

Insurance 59

Insurance Cost-Benefit Digital Transformation Marketing 59

Insight

AUGUST 20, 2019

Data Engineering is a discipline notorious for being framework-driven and it is often hard for newcomers to find the right ones to learn. As part of their fellowship training and transition into data engineering, Fellows spend three weeks working on data engineering projects where they dive deep into these frameworks.

Data Warehouse 71

Data Warehouse Big Data Data-driven Data Processing 71

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. classification problem.

Machine Learning 81

Machine Learning Modeling Metadata Recreation/Entertainment 81

datapine

JUNE 5, 2019

Learn all about data dashboards with our executive bite-sized summary! To summarize, in the context of BI, data dashboards are used for: Deep-level insight: Drilling down deeper into key aspects of your business’s daily, weekly and monthly operation to create initiatives for increased efficiency. What Is A Data Dashboard?

Dashboards 316

Dashboards KPI Sales Visualization 316

Data Science and Beyond

JULY 5, 2015

This page summarises the deep learning resources I’ve consulted in my album cover classification project.

Deep Learning 40

Deep Learning Consulting Optimization Visualization 40

DataRobot Blog

JUNE 3, 2022

Generative Pre-trained Transformer 3 (GPT-3) is a language model that utilizes deep-structured learning to predict human-like text. It was also fed the following introduction: “I am not a human. Quadrant Solutions SPARK Matrix: Data Science and Machine Learning Platform. What is GPT-3? I am Artificial Intelligence.

Risk 52

Risk Predictive Modeling Modeling Technology 52

bridgei2i

JULY 14, 2021

Embedded AI refers to the application of ML and deep learning in software at the device level. An autonomous model can change an entire workflow through the introduction of an AI-infused system. You can deploy it quickly into your existing infrastructure.

Modeling 52

Modeling Deep Learning Enterprise Software 52

Insight

AUGUST 12, 2019

For a good overview of what DevOps entails and how to transition, check out this blog post. Let’s say you’ve learned the fundamentals and are ready to start your job hunt. Introduction Motivation is a great start, but you’ll need the discipline to prepare effectively. Others lend themselves to deep study.

Software 65

Software Data-driven Testing Interactive 65

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. This blog post motivates this problem more fully, and discusses monotonic splines and lattices as a solution. Introduction Machine learning models often behave unpredictably, as data scientists would be the first to tell you.

Machine Learning 108

Machine Learning Deep Learning Modeling Interactive 108

IBM Big Data Hub

DECEMBER 1, 2023

The introduction of ChatGPT capabilities has generated a lot of interest in generative AI foundation models. Foundation models are pre-trained on unlabeled datasets and leverage self-supervised learning using neural network s. The supervised learning that is used to train AI requires a lot of human effort.

Insurance 104

Insurance Digital Transformation Unstructured Data Risk 104

Cloudera

MAY 19, 2021

Introduction. 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. See < [link] > for more details.

Machine Learning 136

Machine Learning Data Science Data Lake Modeling 136

O'Reilly on Data

MAY 14, 2020

This role includes everything a traditional PM does, but also requires an operational understanding of machine learning software development, along with a realistic view of its capabilities and limitations. In our previous article, What You Need to Know About Product Management for AI , we discussed the need for an AI Product Manager.

Management 363

Management Experimentation B2B Machine Learning 363

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. Introduction. Deep Learning on Imagery and Text. And yeah … Miner & Kasch was founded by UMBC alumni.

Deep Learning 55

Deep Learning Data Science Machine Learning Modeling 55

Domino Data Lab

DECEMBER 17, 2019

Introduction. This blog post provides high-level insights into GANs. and webinar provide more depth than this blog post as they cover an implementation of a basic GAN model and demonstrate how adversarial networks can be used to generate training samples. Both the Domino project. Domino Project: [link].

Deep Learning 64

Deep Learning Machine Learning Modeling Statistics 64

datapine

NOVEMBER 19, 2019

Get the inside scoop and learn all the new buzzwords in tech for 2020! AI refers to the autonomous intelligent behavior of software or machines that have a human-like ability to make decisions and to improve over time by learning from experience. Exclusive Bonus Content: Download our Top 10 Technology Buzzwords! Computer Vision.

Technology 279

Technology Internet of Things IT IoT 279

datapine

AUGUST 29, 2022

Best for : Software engineers looking to learn the fundamentals of designing data-intensive applications, the pros, and cons of the different technologies available, as well as key concepts needed to succeed in the process. 2) Designing Data-Intensive Applications by Martin Kleppman. 5) Data Analytics Made Accessible, by Dr. Anil Maheshwari.

Big Data 263

Big Data Data Analytics Analytics Data mining 263

Domino Data Lab

JANUARY 14, 2019

This blog post provides insights on how to use the SHAP and LIME Python libraries in practice and how to interpret their output, helping readers prepare to produce model explanations in their own work. Introduction. xgb_model = xgb.train({'objective':'reg:linear'}, xgb.DMatrix(X_train, label=y_train)) # GBT from scikit-learn?

Testing 89

Testing Modeling Deep Learning Data Science 89