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

AWS Big Data

JUNE 1, 2023

The Deep Java Library (DJL) is an open-source, high-level, engine-agnostic Java framework for deep learning. In this blog post, we demonstrate how you can use DJL within Kinesis Data Analytics for Apache Flink for real-time machine learning inference. The model has been pre-trained on ImageNet with 1.2

Deep Learning 68

Deep Learning Data Analytics Analytics Machine Learning 68

Analytics Vidhya

SEPTEMBER 27, 2022

This article was published as a part of the Data Science Blogathon. ” In that blog, I have explained: how to create a dataset directory, train, test and validation dataset splitting, and training from scratch. This blog is […].

Testing 316

Testing Data Science Publishing Analytics 316

Analytics Vidhya

AUGUST 29, 2022

This article was published as a part of the Data Science Blogathon. Introduction When training a machine learning model, the model can be easily overfitted or under fitted. To avoid this, we use regularization in machine learning to properly fit the model to our test set.

Machine Learning 324

Machine Learning Testing Data Science Publishing 324

Insight

SEPTEMBER 20, 2019

Segmentation Since a few patients had multiple images in the dataset, the data were separated, by patient, into three parts: training (80%), validation (10%), and testing (10%). The box plot below shows a summary of the testing results. This shows that the model indeed learned where and what to look for in the images.

Deep Learning 53

Deep Learning Cost-Benefit Testing Modeling 53

DataRobot

FEBRUARY 10, 2021

The promise and power of AI lead many researchers to gloss over the ways in which things can go wrong when building and operationalizing machine learning models. As a data scientist, one of my passions is to reproduce research papers as a learning exercise. I treated the SARCOS test set (sarcos_inv_test) as a holdout.

Modeling 98

Modeling Deep Learning Machine Learning Metrics 98

CIO Business Intelligence

APRIL 17, 2024

Helping software developers write and test code Similarly in tech, companies are currently open about some of their use cases, but protective of others. They now use what they learn about a program to help build unit tests. And unit tests are too tedious for humans to build reliably.

Enterprise 128

Enterprise Testing Digital Transformation Software 128

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). R Power BI Publishing Azure ML models. Here are some of the specific topics I remember.

Data Science 111

Data Science Machine Learning Deep Learning Testing 111

Smart Data Collective

FEBRUARY 14, 2021

Outline Your Product with Deep Learning Modeling. Deep learning tools can make it easier to model these products. It will become even easier with deep learning algorithms at your fingertips. Make Sure Your Product Works with AI Testing. Big Data is Important for SaaS Publishers.

Data-driven 88

Data-driven Big Data Deep Learning Data mining 88

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 54

Deep Learning Cost-Benefit Insurance Machine Learning 54

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 53

Data Science Machine Learning Cost-Benefit Deep Learning 53

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.

Cost-Benefit 113

Cost-Benefit Data Lake Software Machine Learning 113

Insight

OCTOBER 25, 2019

This category was not considered for the purpose of this project as it does not allow for a 3-way partition for disjoint training, validation, and testing sets. My client also specified that CAD model files of the T-LESS dataset be used for this project, and that one object per class be reserved for testing (Objects 4, 8, 12, 18, 23, 30).

Snapshot 57

Snapshot Testing Deep Learning Visualization 57

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). It is also possible to create your own AMP and publish it in the AMP catalogue for consumption. Machine Learning Model Reproducibility .

Machine Learning 89

Machine Learning Modeling Metadata Recreation/Entertainment 89

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

Insight

APRIL 9, 2019

As a Data Product Management Fellow at Insight , I worked with a machine learning engineering Fellow, simulation engineer, and a propulsion engineer to improve efficiency in manufacturing rocket engines. 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

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 88

Machine Learning Testing Deep Learning Advertising 88

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 67

Deep Learning Forecasting Machine Learning Publishing 67

O'Reilly on Data

MARCH 31, 2020

This has serious implications for software testing, versioning, deployment, and other core development processes. You might establish a baseline by replicating collaborative filtering models published by teams that built recommenders for MovieLens, Netflix, and Amazon. Managing Machine Learning Projects” (AWS).

Management 341

Management Machine Learning Experimentation Metrics 341

Insight

MAY 7, 2019

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 The dataset contains 25,000 reviews for training and 25,000 for testing. Batch Inference ?—?This To support GPU training, Nvidia Docker 2 was used.

Modeling 75

Modeling Testing Machine Learning Interactive 75

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 96

Manufacturing Forecasting Digital Transformation Sales 96

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

Cloudera

SEPTEMBER 12, 2018

AbbVie’s platform uses analytics and machine learning, including natural language processing, deep learning, and unsupervised learning, to proactively identify issues and opportunities. Bob Gourley , Founder Crucial Point LLC and Publisher, CTOvision.com, ThreadBrief.com, @BobGourley. Cloud Success: .

Machine Learning 46

Machine Learning Big Data Data Science Data Warehouse 46

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. And the winner is… Both implementations of our gym equipment classifier achieved 99% accuracy on the test set. Check out the Keras documentation here.

Metrics 58

Metrics Optimization Modeling Testing 58

CIO Business Intelligence

APRIL 15, 2022

MIT Technology Review has chronicled a number of failures, most of which stem from errors in the way the tools were trained or tested. The use of mislabeled data or data from unknown sources was a common culprit. The paper determined the technique not fit for clinical use. In a statement on Oct. In a statement on Oct.

Analytics 145

Analytics Machine Learning Insurance Testing 145

Cloudera

APRIL 27, 2023

This is enabled through a Ray Module in cmlextensions python package published by our team. Its innovative architecture enables seamless integration with ML and deep learning libraries like TensorFlow and PyTorch. To make it easier for CML users to leverage Ray, Cloudera has published a Python package called CMLextensions.

Machine Learning 81

Machine Learning Dashboards Deep Learning Testing 81

AWS Big Data

OCTOBER 26, 2023

We ran between 1–200 concurrent tests of this benchmark, simulating between 1–200 users trying to load a dashboard at the same time. To quantify this, we look at the price-performance using published on-demand pricing for each of the warehouses in the preceding test, shown in the following chart.

Data Warehouse 107

Data Warehouse Cost-Benefit Dashboards Optimization 107

Cloudera

NOVEMBER 3, 2021

Thanks to pioneers like Andrew NG and Fei-Fei Li, GPUs have made headlines for performing particularly well with deep learning techniques. Today, deep learning and GPUs are practically synonymous. While deep learning is an excellent use of the processing power of a graphics card, it is not the only use.

Deep Learning 122

Deep Learning Machine Learning Data Science Cost-Benefit 122

Domino Data Lab

AUGUST 10, 2021

PyTorch: used for deep learning models, like natural language processing and computer vision. It’s used for developing deep learning models. Horovod: is a distributed deep learning training framework that can be used with PyTorch, TensorFlow, Keras, and other tools.

Modeling 64

Modeling IT Deep Learning Risk Management 64

Smarten

APRIL 19, 2024

The use of Generative AI, LLM and products such as ChatGPT capabilities has been applied to all kinds of industries, from publishing and research to targeted marketing and healthcare. Nothing…and I DO mean NOTHING…is more prominent in technology buzz today than Artificial Intelligence (AI). billion, with the market growing by 31.1%

Analytics 59

Analytics Predictive Modeling KPI Machine Learning 59

DataRobot Blog

MARCH 9, 2022

This article was originally published at Algorithimia’s website. With that said, recent advances in deep learning methods have allowed models to improve to a point that is quickly approaching human precision on this difficult task. The company was acquired by DataRobot in 2021. Find out more about DataRobot MLOps here.

Machine Learning 52

Machine Learning Deep Learning Modeling Measurement 52

BizAcuity

MARCH 31, 2022

There have been so many articles published about AI and its applications, you can find millions of articles from broad concepts to deep technical literature on the internet. More use-cases are being tried, tested and built everyday, the innovation in this field will not cease for the next few years. Applications of AI.

Data Analytics 52

Data Analytics Analytics Prescriptive Analytics Predictive Analytics 52

Domino Data Lab

APRIL 3, 2019

We find ways to improve machine learning so that it requires orders of magnitude more data, e.g., deep learning with neural networks. Agile Manifesto get published. Does machine learning change priorities? In short, the virtuous cycle is growing. We keep feeding the monster data. Disconnects, in a nutshell.

Data Governance 84

Data Governance Machine Learning Metadata Data Science 84

Insight

MARCH 25, 2020

After reading this, I hope you can learn how to build deep learning models using TensorFlow Keras, productionalize the model as a Streamlit app, and deploy it as a Docker container on Google Cloud Platform (GCP) using Google Kubernetes Engines (GKE). In this project, I was curious to see if deep learning approaches?—?specifically

Deep Learning 53

Deep Learning Metrics Data Processing Interactive 53

Domino Data Lab

MARCH 29, 2021

Maintaining the cluster and the underlying infrastructure configuration can be a complex and time-consuming task Lack of GPU acceleration – Complex machine workloads, especially the ones involving Deep Learning, benefit from GPU architectures that are well adapted for vector and matrix operations. GPU) and use bitnami/spark:2.4.6

Cost-Benefit 98

Cost-Benefit Machine Learning Testing Interactive 98

Data Science and Beyond

OCTOBER 15, 2017

To answer the question, here are some posts on things I’ve done: Joined Automattic by improving the Elasticsearch language detection plugin , calculated customer lifetime value , analysed A/B test results , built recommender systems (including one for Bandcamp music ), competed on Kaggle , and completed a PhD. I’m pretty busy.

Data Science 71

Data Science Deep Learning Machine Learning Data-driven 71

Insight

FEBRUARY 13, 2019

He is currently a machine learning engineer at Casetext where he works on natural language processing for the legal industry. In late 2018, Google open-sourced BERT, a powerful deep learning algorithm for natural language processing. Prior to Insight, he was at IBM Watson. Yay this is fun! Run training. export BERT_BASE_DIR=.

Testing 72

Testing Machine Learning Deep Learning Modeling 72