IBM Big Data Hub

AUGUST 11, 2023

MLOps is the next evolution of data analysis and deep learning. Simply put, MLOps uses machine learning to make machine learning more efficient. Generative AI is a type of deep-learning model that takes raw data, processes it and “learns” to generate probable outputs.

Data Science 53

Data Science Machine Learning Cost-Benefit Deep Learning 53

Cloudera

FEBRUARY 16, 2022

For example, if you need to build a model for customer churn prediction, you can initiate a new churn modelling with scikit-learn project within Cloudera’s management console or via a call to CML’s RESTful API service. It is also possible to create your own AMP and publish it in the AMP catalogue for consumption.

Machine Learning 85

Machine Learning Modeling Metadata Recreation/Entertainment 85

Insight

OCTOBER 25, 2019

The latter is particularly restricting, as it violates the prerequisite of many deep learning methods for image classification?—?a A schematic illustration of the meta-learning approach to this problem using Prototypical Networks. Model performance is evaluated on aggregated metrics of individual tasks. Meta-Learning v.s.

Snapshot 57

Snapshot Testing Deep Learning Visualization 57

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. We pass 3 parameters: loss, optimizer , and metrics. Choosing your evaluation metric Lastly, metrics just refers to your choice of evaluation metric.

Metrics 58

Metrics Optimization Modeling Testing 58

Rocket-Powered Data Science

JULY 6, 2021

7) Deep learning (DL) may not be “the one algorithm to dominate all others” after all. There was some research published earlier in 2020 that found that traditional, less complex algorithms can be nearly as good or better than deep learning on some tasks. And the goodness doesn’t stop there.

Machine Learning 279

Machine Learning Data-driven IoT Deep Learning 279

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 Risk Management Deep Learning 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 57

Analytics Predictive Modeling KPI Machine Learning 57

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

AWS Big Data

OCTOBER 26, 2023

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. The latest published on-demand pricing is used for all data warehouses and not Reserved Instance pricing as noted earlier.

Data Warehouse 96

Data Warehouse Cost-Benefit Dashboards Optimization 96

Domino Data Lab

JULY 22, 2019

and drop your deep learning model resource footprint by 5-6 orders of magnitude and run it on devices that don’t even have batteries. They’d like to do something more efficient when they’re training a lot of deep learning models. Now, we have low-power devices and inference running on them.

Data Science 109

Data Science Machine Learning Data Governance Modeling 109

Domino Data Lab

MARCH 3, 2019

We had big surprises at several turns and have subsequently published a series of reports. O’Reilly Media published our analysis as free mini-books: The State of Machine Learning Adoption in the Enterprise (Aug 2018). What metrics are used to evaluate success? The data types used in deep learning are interesting.

Data Science 49

Data Science Deep Learning Machine Learning Modeling 49

DataRobot

NOVEMBER 9, 2021

“One look is worth a Thousand Words” This phrase was used in 1913 to convey that graphics had a place in newspaper publishing. When we convert the single channel audio signal time series into an energy spectrogram, it allows us to run state of the art deep learning architectures on the image. . Image courtesy towardsAI.

Visualization 52

Visualization Unstructured Data Statistics Metrics 52

Insight

MARCH 4, 2020

In this blog, I’ll describe how to solve this with Contextual Topic Identification, leveraging machine learning methods to identify semantically similar groups and surface relevant category tags of the reviews. Steam review screenshot Dataset I used the Steam Review Dataset , published on Kaggle as a .csv

Visualization 81

Visualization Modeling Metrics Measurement 81

Insight

NOVEMBER 5, 2019

A Multimodal Deep Learning Model A flexible, multimodal architecture for predicting diagnoses or survival from ICU data. Sign up to learn more about the Insight Fellows programs and start your application today. in the order in which they actually happened. so that the model performs well with other diagnoses.

Deep Learning 61

Deep Learning Modeling Insurance Metrics 61

Insight

MARCH 28, 2019

Yanxia then used a combination of quantitative and qualitative metrics to interpret each topic group identified by LDA. After his time at Insight, John was hired by Deep Macro as a data scientist. Categories cover subjects such as company product, training, compensation, career growth, office perks, and corporate management.

Dashboards 70

Dashboards Visualization Data Science Machine Learning 70

Domino Data Lab

MAY 8, 2019

For example, in the case of more recent deep learning work, a complete explanation might be possible: it might also entail an incomprehensible number of parameters. If your “performance” metrics are focused on predictive power, then you’ll probably end up with more complex models, and consequently less interpretable ones.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

O'Reilly on Data

JANUARY 25, 2021

This study is based on title usage on O’Reilly online learning. The data includes all usage of our platform, not just content that O’Reilly has published, and certainly not just books. We don’t see that in our data, though there are certainly some metrics to say that artificial intelligence has stalled. Web Development.

Machine Learning 363

Machine Learning Software Testing Technology 363