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Paramount factors in successful machine learning projects. Part 2/2

Paramount factors in successful machine learning projects. Part 2/2

January 9, 2023/in Machine learning /by Robert Bogucki and Jan Kanty Milczek

In the first part of our guide we focused on properly executing the entire process of building and implementing machine learning models with a focus on the main goal – solving the overarching business challenge. In the second part of our material we dig deeper into the topic of modeling.

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https://deepsense.ai/wp-content/uploads/2022/12/Paramount-factors-in-successful-machine-learning-projects-part-2.jpeg 337 1140 Robert Bogucki https://deepsense.ai/wp-content/uploads/2019/04/DS_logo_color.svg Robert Bogucki2023-01-09 07:00:302023-01-09 09:27:36Paramount factors in successful machine learning projects. Part 2/2
How to perform self-supervised learning on high-dimensional data

How to perform self-supervised learning on high-dimensional data

October 28, 2022/in Computer vision, Artificial Intelligence, Machine learning /by Łukasz Kuśmierz

In this post, the self-supervised learning paradigm is discussed. This method of training machine learning models is emerging nowadays, especially for high-dimensional data. In order to focus the attention of this article, we will only work on examples from the computer vision area. However, the methods presented are general and may be successfully used for problems from other domains as well.

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https://deepsense.ai/wp-content/uploads/2022/10/How-to-perform-self-supervised-learning-on-high-dimensional-data.jpeg 337 1140 Łukasz Kuśmierz https://deepsense.ai/wp-content/uploads/2019/04/DS_logo_color.svg Łukasz Kuśmierz2022-10-28 10:00:502022-10-28 12:45:26How to perform self-supervised learning on high-dimensional data
The recent rise of diffusion-based models

The recent rise of diffusion-based models

September 5, 2022/in Machine learning /by Maciej Domagała

In this post, we will sum up the very recent history of solving the text-to-image generation problem and explain the latest developments regarding diffusion models, which are playing a huge role in the new, state-of-the-art architectures.

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https://deepsense.ai/wp-content/uploads/2022/07/The-recent-rise-of-diffusion-based-models.jpeg 337 1140 Maciej Domagała https://deepsense.ai/wp-content/uploads/2019/04/DS_logo_color.svg Maciej Domagała2022-09-05 08:00:592022-09-06 10:37:42The recent rise of diffusion-based models
Five solid reasons to outsource your AI software development

Five solid reasons to outsource your AI software development

August 21, 2022/in Artificial Intelligence /by deepsense.ai

Customized AI software development is one of the most powerful approaches to leveraging AI in business. Undoubtedly, the main difficulty of this approach is the ability to successfully develop and implement AI projects. Cooperation with an experienced AI vendor, who is responsible for end-to-end delivery, is one of the most effective solutions, bringing with it a number of benefits.

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https://deepsense.ai/wp-content/uploads/2022/08/Five-solid-reasons-to-outsource-your-AI-software-development.jpeg 337 1140 deepsense.ai https://deepsense.ai/wp-content/uploads/2019/04/DS_logo_color.svg deepsense.ai2022-08-21 10:43:502022-08-21 10:46:51Five solid reasons to outsource your AI software development
Data Science with Graphs - using knowledge graphs on the data before it reaches the ML phase

Data Science with Graphs – using knowledge graphs on the data before it reaches the ML phase

June 11, 2022/in Machine learning /by Grzegorz Rybak

Graph usage in AI recently became quite evident with an increased number of research papers and some impressive examples among the industry . This article aims to answer the question: Are there ways to improve a project’s delivery by using graphs even before reaching GraphML?

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https://deepsense.ai/wp-content/uploads/2022/06/Data-Science-with-Graphs-using-knowledge-graphs-on-the-data-before-it-reaches-the-ML-phase.jpeg 337 1140 Grzegorz Rybak https://deepsense.ai/wp-content/uploads/2019/04/DS_logo_color.svg Grzegorz Rybak2022-06-11 10:19:332023-02-05 20:23:47Data Science with Graphs – using knowledge graphs on the data before it reaches the ML phase
Paramount factors in successful machine learning projects

Paramount factors in successful machine learning projects. Part 1/2.

May 21, 2022/in Machine learning /by Robert Bogucki

Much has been said about the effective running of machine learning projects. However, the topic keeps coming up. It is vital to remember that the purpose of ML projects is not modeling itself, but achieving defined business goals.

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https://deepsense.ai/wp-content/uploads/2021/05/Paramount-factors-in-successful-machine-learning-projects.jpeg 337 1140 Robert Bogucki https://deepsense.ai/wp-content/uploads/2019/04/DS_logo_color.svg Robert Bogucki2022-05-21 16:26:312022-12-19 15:35:49Paramount factors in successful machine learning projects. Part 1/2.
6 steps to successfully implement AI project

6 steps to successfully implement AI project

May 18, 2022/in Artificial Intelligence /by deepsense.ai

While enterprises recognize the measurable business benefits of AI adoption, they don’t necessarily see the path to get there. As the Everest Group survey indicates, 3 out of 5 of enterprises fail to adopt AI and don’t achieve meaningful business outcomes. Let’s look for the missing key to harness the full potential of AI implementation.

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https://deepsense.ai/wp-content/uploads/2022/05/6-steps-to-successfully-implement-AI-project.jpeg 337 1140 deepsense.ai https://deepsense.ai/wp-content/uploads/2019/04/DS_logo_color.svg deepsense.ai2022-05-18 18:54:332022-05-18 19:11:296 steps to successfully implement AI project
Overview of explainable AI methods in NLP

Overview of explainable AI methods in NLP

March 29, 2022/in Artificial Intelligence /by Kamil Pluciński

In recent years, we have seen rapid development in the field of artificial intelligence, which has led to increased interest in areas that have not often been previously addressed. As AI becomes more and more advanced, beyond model effectiveness, experts are being challenged to understand and retrace how the algorithms came up with their results, and how the models are reasoning and why [Samek and Muller, 2019].

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https://deepsense.ai/wp-content/uploads/2022/02/Overview-of-explainable-AI-methods-in-NLP.jpeg 337 1140 Kamil Pluciński https://deepsense.ai/wp-content/uploads/2019/04/DS_logo_color.svg Kamil Pluciński2022-03-29 19:57:392022-03-30 16:41:39Overview of explainable AI methods in NLP
AI challenges in retail and manufacturing

AI challenges in retail and manufacturing

February 2, 2022/in Artificial Intelligence /by deepsense.ai

We discuss the main challenges of AI implementations in retail and manufacturing with Ireneusz Prus, Director of Data Monetization at Maspex Group.

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https://deepsense.ai/wp-content/uploads/2022/01/AI-challenges-in-retail-and-manufacturing.jpeg 337 1140 deepsense.ai https://deepsense.ai/wp-content/uploads/2019/04/DS_logo_color.svg deepsense.ai2022-02-02 18:58:112022-03-01 14:31:18AI challenges in retail and manufacturing
Five questions to answer before hiring an AI vendor

Five questions to answer before hiring an AI vendor

January 17, 2022/in Artificial Intelligence /by deepsense.ai

AI projects requires building interdisciplinary teams – both from business and technology. Executing the process successfully is quite a challenge and hiring and AI vendor may be an effective solution.

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https://deepsense.ai/wp-content/uploads/2022/01/Five-questions-to-answer-before-hiring-an-AI-vendor.jpeg 568 1920 deepsense.ai https://deepsense.ai/wp-content/uploads/2019/04/DS_logo_color.svg deepsense.ai2022-01-17 09:06:082022-01-17 09:56:11Five questions to answer before hiring an AI vendor
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