Data Science & Machine Learning Rinat Abdullin Data Science & Machine Learning Rinat Abdullin

So You are Building an AI Assistant?

So you are building an AI assistant for the business?

This is a popular topic in the companies these days. Everybody seems to be doing that. While running AI Research in the last months, I have discovered that many companies in the USA and Europe are building some sort of AI assistant these days, mostly around enterprise workflow automation and knowledge bases.

There are common patterns in how such projects work most of the time. So let me tell you a story...

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Data Science & Machine Learning Aqeel Alazree Data Science & Machine Learning Aqeel Alazree

Unlocking Potential with Natural Readers: The Intersection of AI and Voice Manipulation

The advent of Artificial Intelligence (AI) in text-to-speech (TTS) technologies has revolutionized the way we interact with written content. Natural Readers, standing at the forefront of this innovation, offers a comprehensive suite of features designed to cater to a broad spectrum of needs, from personal leisure to educational support and commercial use. As we delve into the capabilities of Natural Readers, it's crucial to explore both the advantages it brings to the table and the ethical considerations surrounding voice manipulation in TTS technologies.

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Data Science & Machine Learning, LLM Aqeel Alazree Data Science & Machine Learning, LLM Aqeel Alazree

Part 2: Data Analysis with powerful Python – How to get the best results from your data

Analyzing and visualizing data from a SQLite database in Python can be a powerful way to gain insights and present your findings. In Part 2 of this blog series, we will walk you through the steps to retrieve data from a SQLite database file named gold.db and display it in the form of a chart using Python. We'll use some essential tools and libraries for this task.

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Data Science & Machine Learning, LLM Aqeel Alazree Data Science & Machine Learning, LLM Aqeel Alazree

Part 1: Harnessing the Potential of Data Analysis with ChatGPT

In this new blog series we will give you an overview of how to analyze and visualize data, create code manually and how to make ChatGPT work effectively.
Part 1 deals with the following: In the data-driven era, businesses and organizations are constantly seeking ways to extract meaningful insights from their data. One powerful tool that can facilitate this process is ChatGPT, a state-of-the-art natural language processing model developed by OpenAI.
In Part 1 pf this blog, we'll explore the proper usage of data analysis with ChatGPT and how it can help you make the most of your data.

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Data Science & Machine Learning, LLM Rinat Abdullin Data Science & Machine Learning, LLM Rinat Abdullin

5 Inconvenient Questions when hiring an AI company

This article discusses five questions you should ask when hiring an AI. These questions are inconvenient for providers of AI products, but they are necessary to ensure that you are getting the best product for your needs. The article also discusses the importance of testing the AI system on your own data to see how it performs.

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Data Science & Machine Learning, LLM Matus Zilinsky Data Science & Machine Learning, LLM Matus Zilinsky

How ChatGPT (GPT-4) Created a Social Media Posts Generator Website

Using the GPT-3-turbo and DALL-E models in Node.js to create a social post generator for a fictional product can be really helpful. The author uses ChatGPT to create an API that utilizes the openai library for Node.js., a Vue component with an input for the title and message of the post. This article provides step-by-step instructions for setting up the project and includes links to the code repository.

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Data Science & Machine Learning Felix Krause Data Science & Machine Learning Felix Krause

Creating a Cross-Domain Capable ML Pipeline for Image Classification

As classifying images into categories is a ubiquitous task occurring in various domains, a need for a machine learning pipeline which can accommodate for new categories is easy to justify. In particular, common general requirements are to filter out low-quality (blurred, low contrast etc.) images, and to speed up the learning of new categories if image quality is sufficient. In this blog post, resulting from a joint work with Aigiz Kunafin (Trustbit Data Science), we compare several image classification models from the transfer learning perspective.

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