KDnuggets

OCTOBER 10, 2019

We illustrate how to show the activation maps of various layers in a deep CNN model with just a couple of lines of code.

Deep Learning 108

Deep Learning Modeling 108

O'Reilly on Data

MARCH 20, 2019

Apply fair and private models, white-hat and forensic model debugging, and common sense to protect machine learning models from malicious actors. Like many others, I’ve known for some time that machine learning models themselves could pose security risks.

Modeling 225

Modeling Machine Learning Predictive Modeling Consulting 225

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

DECEMBER 12, 2019

Not least is the broadening realization that ML models can fail. And that’s why model debugging, the art and science of understanding and fixing problems in ML models, is so critical to the future of ML. Because all ML models make mistakes, everyone who cares about ML should also care about model debugging. [1]

Machine Learning 196

Machine Learning Modeling Testing Risk Management 196

O'Reilly on Data

FEBRUARY 18, 2020

Growth is still strong for such a large topic, but usage slowed in 2018 (+13%) and cooled significantly in 2019, growing by just 7%. But sustained interest in cloud migrations—usage was up almost 10% in 2019, on top of 30% in 2018—gets at another important emerging trend. Still cloud-y, but with a possibility of migration.

Data-driven 340

Data-driven Software Statistics Marketing 340

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 68

Data Architecture Deep Learning Machine Learning Big Data 68

Dataiku

JUNE 26, 2020

In the last few years, we’ve seen a lot of breakthroughs in reinforcement learning (RL). From 2013 with the first deep learning model to successfully learn a policy directly from pixel input using reinforcement learning to the OpenAI Dexterity project in 2019, we live in an exciting moment in RL research.

Deep Learning 105

Deep Learning Modeling 105

Smart Data Collective

MAY 2, 2019

Big data is vital for helping SEO companies identify and rectify inefficiencies in their models. There are a number of deep learning tools that evaluate social media activity. Local businesses need to rely heavily on SEO in 2019. Deep learning and other big data tools will be essential in the year is moving forward.

Big Data 111

Big Data Optimization Modeling Deep Learning 111

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 114

Experimentation Enterprise Deep Learning Modeling 114

O'Reilly on Data

FEBRUARY 4, 2019

In this post, I’ll describe some of the key areas of interest and concern highlighted by respondents from Europe, while describing how some of these topics will be covered at the upcoming Strata Data conference in London (April 29 - May 2, 2019). Machine Learning model lifecycle management. Deep Learning.

Machine Learning 278

Machine Learning Enterprise IoT Big Data 278

CIO Business Intelligence

OCTOBER 4, 2023

We’ve been working on this for over a decade, including transformer-based deep learning,” says Shivananda. An example of the impact of AI can be seen from 2019 to 2022, when the company’s loss rate reduced by almost half, in part thanks to advances in algorithms and AI technology.

Digital Transformation 130

Digital Transformation Deep Learning Data Lake Risk 130

The Unofficial Google Data Science Blog

NOVEMBER 17, 2020

by TAMAN NARAYAN & SEN ZHAO A data scientist is often in possession of domain knowledge which she cannot easily apply to the structure of the model. On the one hand, basic statistical models (e.g. On the other hand, sophisticated machine learning models are flexible in their form but not easy to control.

Machine Learning 108

Machine Learning Deep Learning Modeling Interactive 108

KDnuggets

DECEMBER 2, 2019

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

Deep Learning 106

Deep Learning Modeling Machine Learning 106

CIO Business Intelligence

JANUARY 23, 2023

As OpenAI’s exclusive cloud provider it will see additional revenue for its Azure services, as one of OpenAI’s biggest costs is providing the computing capacity to train and run its AI models. The deal, announced by OpenAI and Microsoft on Jan. Additionally, it may not always be able to understand or respond to certain inputs correctly.”

Enterprise 131

Enterprise Deep Learning Recreation/Entertainment Interactive 131

KDnuggets

JULY 24, 2019

Recently, researchers from the Google Brain team published a paper proposing a new method called Concept Activation Vectors (CAVs) that takes a new angle to the interpretability of deep learning models.

Deep Learning 119

Deep Learning Publishing Modeling 119

O'Reilly on Data

JUNE 27, 2019

Improvements in documentation, ease-of-use, and its production-ready implementation of key deep learning models, combined with speed, scalability, and accuracy has made Spark NLP a viable option for enterprises needing an NLP library. A three-part series on “Comparing production-grade NLP libraries”.

Deep Learning 128

Deep Learning Enterprise Modeling IT 128

DataKitchen

APRIL 13, 2021

DataOps needs a directed graph-based workflow that contains all the data access, integration, model and visualization steps in the data analytic production process. ModelOps and MLOps fall under the umbrella of DataOps,with a specific focus on the automation of data science model development and deployment workflows.

Testing 304

Testing Machine Learning Consulting Data Quality 304

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

TDAN

JUNE 2, 2020

With multiple technologies involved, even deep learning algorithms can’t do the trick. 2019 witnessed record-breaking AI funding, and it’s mostly possible because, over the years, decision making has […]. The domain of AI and data science so far has created significant value in the technological landscape.

Data Science 52

Data Science Deep Learning Technology Business Intelligence 52

bridgei2i

MAY 14, 2019

BANGALORE, May 14, 2019. BRIDGEi2i is pleased to host Alex Smola – VP & Distinguished Scientist at AWS for an informative and hands-on learning session on Computer Vision GluconCV & D2L.ai on 18th May 2019 at the BRIDGEi2i auditorium. For more details on the meetup, please click here. About Alex Smola.

Contextual Data 63

Contextual Data Deep Learning Data Processing Consulting 63

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 95

Unstructured Data Deep Learning Insurance Interactive 95

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 92

Deep Learning Modeling Machine Learning 92

KDnuggets

NOVEMBER 18, 2019

When training a neural network in deep learning, its performance on processing new data is key. Improving the model's ability to generalize relies on preventing overfitting using these important methods.

Deep Learning 87

Deep Learning Modeling IT 87

KDnuggets

AUGUST 9, 2019

Anyone working on non-trivial deep learning models in Pytorch such as industrial researchers, Ph.D. The models we're talking about here might be taking you multiple days to train or even weeks or months. Who is this guide for? students, academics, etc.

Deep Learning 88

Deep Learning Modeling 88

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 87

Deep Learning Modeling Technology Machine Learning 87

Domino Data Lab

JULY 2, 2019

Paco Nathan ‘s latest article covers program synthesis, AutoPandas, model-driven data queries, and more. See also: Caroline Lemieux’s slides for that NeurIPS talk, and Rohan Bavishi’s video from the RISE Summer Retreat 2019. Introduction. Welcome back to our monthly burst of themespotting and conference summaries.

Metadata 105

Metadata Machine Learning Data Science Data-driven 105

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

Cloudera

OCTOBER 1, 2022

It is pretty impressive just how much has changed in the enterprise machine learning and AI landscape. Thinking back to the conversations I had in late 2019, early 2020, most of the mainstream organizations I was talking to, meaning not the Facebooks and the Googles of the world, had very similar machine learning and AI journeys.

Data Governance 73

Data Governance Strategy Enterprise Machine Learning 73

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 57

Metrics Deep Learning Machine Learning Modeling 57

O'Reilly on Data

MARCH 31, 2020

Instead of writing code with hard-coded algorithms and rules that always behave in a predictable manner, ML engineers collect a large number of examples of input and output pairs and use them as training data for their models. The model is produced by code, but it isn’t code; it’s an artifact of the code and the training data.

Management 338

Management Machine Learning Experimentation Metrics 338

Domino Data Lab

MARCH 3, 2019

O’Reilly Media published our analysis as free mini-books: The State of Machine Learning Adoption in the Enterprise (Aug 2018). Evolving Data Infrastructure: Tools and Best Practices for Advanced Analytics and AI (Jan 2019). AI Adoption in the Enterprise: How Companies Are Planning and Prioritizing AI Projects in Practice (Feb 2019).

Data Science 49

Data Science Deep Learning Machine Learning Modeling 49

CIO Business Intelligence

APRIL 15, 2022

Derek Driggs, a machine learning researcher at the University of Cambridge, together with his colleagues, published a paper in Nature Machine Intelligence that explored the use of deep learning models for diagnosing the virus. The paper determined the technique not fit for clinical use.

Analytics 145

Analytics Machine Learning Insurance Testing 145

O'Reilly on Data

SEPTEMBER 11, 2019

Our call for speakers for Strata NY 2019 solicited contributions on the themes of data science and ML; data engineering and architecture; streaming and the Internet of Things (IoT); business analytics and data visualization; and automation, security, and data privacy. 2 in frequency in proposal topics; a related term, “models,” is No.

IoT 20

IoT Big Data Data Warehouse Uncertainty 20

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. Introduction.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

KDnuggets

OCTOBER 14, 2019

Also: Activation maps for deep learning models in a few lines of code; The 4 Quadrants of Data Science Skills and 7 Principles for Creating a Viral Data Visualization; OpenAI Tried to Train AI Agents to Play Hide-And-Seek but Instead They Were Shocked by What They Learned; 10 Great Python Resources for Aspiring Data Scientists.

Deep Learning 48

Deep Learning Visualization Data Science Modeling 48

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

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 Reporting Experimentation 98