Hello everyone, today I’m going to take you to explore a super interesting field – TinyML! Are you still worried about insufficient computing power of devices? Or are you concerned that AI models are too large to run? TinyML was born to solve these problems. It can make AI models lightweight, make smart devices smarter, … Read more
↑↑↑ Click on the blue text above, reply with materials, and get a surprise of 10GB Author: Mu Yang Source: Huazhang Computer (hzbook_jsj) Introduction: Today, we introduce a brand new version of machine learning that you may not have tried before, called TinyML. ML is something we are all familiar with, while TinyML is the … Read more
Are you curious about Artificial Intelligence (AI) and Machine Learning (ML)? Do you want to know how to use it on the MCU you have already worked with? In this article, we will introduce you to Machine Learning on MCUs. This topic is also known as Tiny Machine Learning (TinyML). Get ready to lose to … Read more
Machine Learning (ML) is a vibrant and powerful field of computer science that permeates almost all digital devices we interact with, whether it’s social media, mobile phones, cars, or even household appliances. Artificial Intelligence (AI) is rapidly moving from the “cloud” to the “edge,” entering increasingly smaller IoT devices. The machine learning processes implemented on … Read more
Learn how to deploy complex machine learning models on single-board computers, mobile phones, and microcontrollers. Main Features ● Comprehensive understanding of the core concepts of TinyML. ● Learn how to design your own TinyML applications from scratch. ● Explore cutting-edge models, hardware, and software platforms for developing TinyML. Description TinyML is an innovative technology that … Read more
1. Introduction Weakly Supervised Object Localization is used to discover the location of target objects within images. Traditional object detection methods typically require precise bounding box annotations for each training sample, which can be time-consuming and labor-intensive for large-scale datasets. To address this issue, weakly supervised object localization solves the problem by using simpler annotation … Read more
1. Introduction Voice recognition, as an important method of human-computer interaction, is gradually becoming one of the core functions of smart devices. However, traditional voice recognition systems often rely on cloud servers for audio data processing and analysis, which brings issues such as latency and privacy. TensorFlow Lite provides an efficient and fast solution for … Read more
Click the blue text to follow us Introduction In today’s Internet of Things era, the application of voice recognition technology is becoming increasingly widespread. This article will introduce how to deploy a TinyML voice recognition system and detail the process from data collection to model deployment. Getting Started with TinyML: Implementing TinyML Voice Recognition Using … Read more
According to reports from Electronic Enthusiasts (Written by Li Wanwan), in the era of artificial intelligence, more and more AI applications need to extend from the cloud to the edge, such as smart headphones, smart cameras, smart bracelets, logistics robots, etc. Deploying AI at the edge has become a trend. With the rapid development of … Read more
Introduction This page will guide you on installing TensorFlow on a Raspberry Pi 4 running the 64-bit Bullseye operating system. TensorFlow is a large software library developed specifically for deep learning, which consumes a lot of resources. You can run TensorFlow on a Raspberry Pi 4, but do not expect miraculous performance. If the model … Read more