In this post, we are going to understand the underlying concepts of semantic image inpainting and various techniques developed for the same.
However, we are going to focus on an effective and elegant state-of-the-art context encoder and generative model based approach for image inpainting.
Continue reading “Semantic Image Inpainting with Context Encoders”
Regular attendees of tech conferences might have noticed that pushing toward AI and machine learning is one thing that’s consistent across all tech giants, including Apple , Google , Amazon, and Microsoft.
For example, this year’s Google I / O was largely focused on machine learning, artificial intelligence, and how it’s going to revolutionize the world.
And me, as an Android developer and someone who has a bad case of FOMO, I started looking into any and all resources that could help me get a kickstart with using machine learning and AI in my development projects.
Continue reading “Embracing Machine Learning as a Mobile Developer”
Tween Manager is used in making custom animations within Lens Studio. This helps you make your lenses more engaging and captivating. You also don’t need to write any code. In this article, you will learn how the different types of tweens works.
Selfies are an art form. If you’re going to put your face out there, it’s got to look good. I’ve seen plenty of apps claim to offer the best filters, stickers, and retouching options, but they most of them just fall flat. The Portrait app by Img.ly is one of the few that offers something truly unique.
They use machine learning to automatically extract our face from the background in order to create awesome, poster-like styles that look different than anything else out there.
In our post from about a month ago, we compared two of the major on-device text recognition SDKs on iOS: Firebase’s ML Kit & Tesseract OCR. The results were pretty one-sided, as ML Kit outclassed Tesseract on many of the predictions and showed much better accuracy even when it failed.
Continue reading “Comparing Firebase ML Kit’s Text Recognition on Android & iOS”
If you’re looking for the best ChatGPT alternatives, you’re in the right place. Since launching in 2022, ChatGPT took the world by storm.
The AI chatbot, ideal for creating content, answering complex questions, and boosting productivity, introduced millions of users to a new type of AI.
Powered by large language model technology (GPT3.5 for the free version and GPT-4 for the paid version), ChatGPT is one of the world’s leading AI tools.
However, it’s far from the only generative AI solution on the market. In fact, dozens of similar solutions have emerged to help summarize content, streamline tasks, and sharpen the competitive edge of users.
When it comes to retail and e-commerce, the AI revolution is already here. From AI-enhanced search and at-home product demos to clothing/accessory try-on applications, machine learning is increasingly baked into shopping experiences, both in-store and online.
Continue reading “Mobile Machine Learning in E-commerce and Retail: Visual Search [Ebook Sample]”
TensorFlow was the new kid on the block when it was introduced in 2015, and since then, it’s become one of the most used deep learning framework.
I jumped on the train a few months after the first release and began my journey into deep learning during my master’s thesis. It took a while to get used to the computation graph and session model, but since then I’ve got my head around most of the quirks and twists.
Sentiment analysis has become a hot topic in the fields of Natural Language Processing and machine learning. Sentiment analysis is the process of examining a piece of text for opinions and feelings.
There are innumerable real-life use cases for sentiment analysis that include understanding how consumers feel about a product or service, looking for signs of depression, or to see how people respond to certain ad and political campaigns.
Continue reading “Training a Core ML Model for Sentiment Analysis”
In this post, I’ll be covering how CNNs are implemented efficiently in deep learning frameworks like TensorFlow and PyTorch.
When I was writing my own framework for Binarized CNNs in C++, I expected it to work as fast as PyTorch. The result — my Conv implementation was 100x slower than PyTorch. I even compared the number of FLOPs of my code with PyTorch. But the FLOP count was same.
![Mobile Machine Learning in E-commerce and Retail: Visual Search [Ebook Sample]](https://fritz.ai/wp-content/uploads/2023/12/1MTQN0qTASnLSEy2E8cMGMQ.webp)
