A Comprehensive Guide with 175 Keywords to Master Midjourney Photorealism (along with a Primer on Photography) — Given below are two images (A) & (B). Which of them was generated by AI & which was clicked by a human?

Learn how to use Midjourney, an AI image generator from text, to create eye-catching graphics, logos, avatars, and more for your marketing content. Plus, discover some ethical and legal considerations, as well as some best practices to optimize your images for SEO. — ⚡Are you looking for a way to spice up your marketing content with eye-catching visuals? 🤩 ⚡Do you want to save time and money on hiring designers or buying stock images? ⌚💵 ⚡Do you want to unleash your creativity and generate unique graphics that match your brand and vision? 🧠

Autosuggestion, also known as query autocompletion (QAC), predictive search, and type-ahead, is a key search-assist feature that has become indispensable in modern information retrieval for enhancing search experience. …

A step-by-step tutorial on building a Microsoft Edge extension for text paraphrasing using PEGASUS, KeyBERT, and WordNet — A Microsoft Edge extension is a small program that allows developers to modify or “extend” certain functionality in Microsoft Edge. It enhances the browsing experience of a specific target audience by usually performing a niche function. …

Efficient tracking and monitoring of Transformer models for Financial Sentiment Analysis using MLFoundry, by TrueFoundry — Natural language processing (NLP) has become increasingly popular in financial applications in recent years. Stock/forex market forecasting, volatility modeling, asset allocation, business taxonomy construction, credit scoring, initial public offering (IPO) valuation, and other applications are among them. One common task (or rather subtask, which can provide features for some tasks…

PyTorch implementation of VQA models using text and image transformers from Hugging Face — Recent years have seen significant advancements not only in the respective domains of Natural Language Processing (NLP) and Computer Vision (CV) but also in tasks involving multiple modalities (text + image features) such as image captioning, visual question answering (VQA), cross-modal retrieval, visual common-sense reasoning, and more. …

Over the last 3 posts, we developed a firm understanding of how to version ML artifacts & build ML Pipelines with great ease & sophistication. …

In Part 2 of this 4-blog series, we gained some experience with DVC, a tool to implement efficient versioning of ML artifacts. With our data in place, the next steps in an ML workflow are to first perform EDA, followed by feature engineering & model training. …

In Part 1 of this 4-blog series, we understood the motivation behind adopting MLOps practices & also looked at some of the basic principles of MLOps. We also had a glance at the arsenal of tools & frameworks used to implement MLOps in various stages of an ML workflow lifecycle…