#image recognition api

[filestack]

Using the Image Recognition API with a picture taken with the device's camera or one from the gallery. Without some sort of picture recognition, handling a large number of images is no longer useful or even feasible.

[izicodes]

Hi! I’m a student currently learning computer science in college and would love it if you had any advice for a cool personal project to do? Thanks!

Personal Project Ideas

Hiya!! 💕

It's so cool that you're a computer science student, and with that, you have plenty of options for personal projects that can help with learning more from what they teach you at college. I don't have any experience being a university student however 😅

Someone asked me a very similar question before because I shared my projects list and they asked how I come up with project ideas - maybe this can inspire you too, here's the link to the post [LINK]

However, I'll be happy to share some ideas with you right now. Just a heads up: you can alter the projects to your own specific interests or goals in mind. Though it's a personal project meaning not an assignment from school, you can always personalise it to yourself as well! Also, I don't know the level you are, e.g. beginner or you're pretty confident in programming, if the project sounds hard, try to simplify it down - no need to go overboard!!

But here is the list I came up with (some are from my own list):

Personal Finance Tracker

A web app that tracks personal finances by integrating with bank APIs. You can use Python with Flask for the backend and React for the frontend. I think this would be great for learning how to work with APIs and how to build web applications 🏦

Online Food Ordering System

A web app that allows users to order food from a restaurant's menu. You can use PHP with Laravel for the backend and Vue.js for the frontend. This helps you learn how to work with databases (a key skill I believe) and how to build interactive user interfaces 🙌🏾

Movie Recommendation System

I see a lot of developers make this on Twitter and YouTube. It's a machine-learning project that recommends movies to users based on their past viewing habits. You can use Python with Pandas, Scikit-learn, and TensorFlow for the machine learning algorithms. Obviously, this helps you learn about how to build machine-learning models, and how to use libraries for data manipulation and analysis 📊

Image Recognition App

This is more geared towards app development if you're interested! It's an Android app that uses image recognition to identify objects in a photo. You can use Java or Kotlin for the Android development and TensorFlow for machine learning algorithms. Learning how to work with image recognition and how to build mobile applications - which is super cool 👀

Social Media Platform

(I really want to attempt this one soon) A web app that allows users to post, share, and interact with each other's content. Come up with a cool name for it! You can use Ruby on Rails for the backend and React for the frontend. This project would be great for learning how to build full-stack web applications (a plus cause that's a trend that companies are looking for in developers) and how to work with user authentication and authorization (another plus)! 🎭

Text-Based Adventure Game

If you're interested in game developments, you could make a simple game where users make choices and navigate through a story by typing text commands. You can use Python for the game logic and a library like Pygame for the graphics. This project would be great for learning how to build games and how to work with input/output. 🎮

Weather App

Pretty simple project - I did this for my apprenticeship and coding night classes! It's a web app that displays weather information for a user's location. You can use Node.js with Express for the backend and React for the frontend. Working with APIs again, how to handle asynchronous programming, and how to build responsive user interfaces! 🌈

Online Quiz Game

A web app that allows users to take quizzes and compete with other players. You could personalise it to a module you're studying right now - making a whole quiz application for it will definitely help you study! You can use PHP with Laravel for the backend and Vue.js for the frontend. You get to work with databases, build real-time applications, and maybe work with user authentication. 🧮

Chatbot

(My favourite, I'm currently planning for this one!) A chatbot that can answer user questions and provide information. You can use Python with Flask for the backend and a natural language processing library like NLTK for the chatbot logic. If you want to mauke it more beginner friendly, you could use HTML, CSS and JavaScript and have hard-coded answers set, maybe use a bunch of APIs for the answers etc! This project would be great because you get to learn how to build chatbots, and how to work with natural language processing - if you go that far! 🤖

Another place I get inspiration for more web frontend dev projects is on Behance and Pinterest - on Pinterest search for like "Web design" or "[Specific project] web design e.g. shopping web design" and I get inspiration from a bunch of pins I put together! Maybe try that out!

I hope this helps and good luck with your project!

[govindhtech]

Open Platform For Enterprise AI Avatar Chatbot Creation

How may an AI avatar chatbot be created using the Open Platform For Enterprise AI framework?

I. Flow Diagram

The graph displays the application’s overall flow. The Open Platform For Enterprise AI GenAIExamples repository’s “Avatar Chatbot” serves as the code sample. The “AvatarChatbot” megaservice, the application’s central component, is highlighted in the flowchart diagram. Four distinct microservices Automatic Speech Recognition (ASR), Large Language Model (LLM), Text-to-Speech (TTS), and Animation are coordinated by the megaservice and linked into a Directed Acyclic Graph (DAG).

Every microservice manages a specific avatar chatbot function. For instance:

Software for voice recognition that translates spoken words into text is called Automatic Speech Recognition (ASR).

By comprehending the user’s query, the Large Language Model (LLM) analyzes the transcribed text from ASR and produces the relevant text response.

The text response produced by the LLM is converted into audible speech by a text-to-speech (TTS) service.

The animation service makes sure that the lip movements of the avatar figure correspond with the synchronized speech by combining the audio response from TTS with the user-defined AI avatar picture or video. After then, a video of the avatar conversing with the user is produced.

An audio question and a visual input of an image or video are among the user inputs. A face-animated avatar video is the result. By hearing the audible response and observing the chatbot’s natural speech, users will be able to receive input from the avatar chatbot that is nearly real-time.

Create the “Animation” microservice in the GenAIComps repository

We would need to register a new microservice, such “Animation,” under comps/animation in order to add it:

Register the microservice

@register_microservice( name=”opea_service@animation”, service_type=ServiceType.ANIMATION, endpoint=”/v1/animation”, host=”0.0.0.0″, port=9066, input_datatype=Base64ByteStrDoc, output_datatype=VideoPath, ) @register_statistics(names=[“opea_service@animation”])

It specify the callback function that will be used when this microservice is run following the registration procedure. The “animate” function, which accepts a “Base64ByteStrDoc” object as input audio and creates a “VideoPath” object with the path to the generated avatar video, will be used in the “Animation” case. It send an API request to the “wav2lip” FastAPI’s endpoint from “animation.py” and retrieve the response in JSON format.

Remember to import it in comps/init.py and add the “Base64ByteStrDoc” and “VideoPath” classes in comps/cores/proto/docarray.py!

This link contains the code for the “wav2lip” server API. Incoming audio Base64Str and user-specified avatar picture or video are processed by the post function of this FastAPI, which then outputs an animated video and returns its path.

The functional block for its microservice is created with the aid of the aforementioned procedures. It must create a Dockerfile for the “wav2lip” server API and another for “Animation” to enable the user to launch the “Animation” microservice and build the required dependencies. For instance, the Dockerfile.intel_hpu begins with the PyTorch* installer Docker image for Intel Gaudi and concludes with the execution of a bash script called “entrypoint.”

Create the “AvatarChatbot” Megaservice in GenAIExamples

The megaservice class AvatarChatbotService will be defined initially in the Python file “AvatarChatbot/docker/avatarchatbot.py.” Add “asr,” “llm,” “tts,” and “animation” microservices as nodes in a Directed Acyclic Graph (DAG) using the megaservice orchestrator’s “add” function in the “add_remote_service” function. Then, use the flow_to function to join the edges.

Specify megaservice’s gateway

An interface through which users can access the Megaservice is called a gateway. The Python file GenAIComps/comps/cores/mega/gateway.py contains the definition of the AvatarChatbotGateway class. The host, port, endpoint, input and output datatypes, and megaservice orchestrator are all contained in the AvatarChatbotGateway. Additionally, it provides a handle_request function that plans to send the first microservice the initial input together with parameters and gathers the response from the last microservice.

In order for users to quickly build the AvatarChatbot backend Docker image and launch the “AvatarChatbot” examples, we must lastly create a Dockerfile. Scripts to install required GenAI dependencies and components are included in the Dockerfile.

II. Face Animation Models and Lip Synchronization

GFPGAN + Wav2Lip

A state-of-the-art lip-synchronization method that uses deep learning to precisely match audio and video is Wav2Lip. Included in Wav2Lip are:

A skilled lip-sync discriminator that has been trained and can accurately identify sync in actual videos

A modified LipGAN model to produce a frame-by-frame talking face video

An expert lip-sync discriminator is trained using the LRS2 dataset as part of the pretraining phase. To determine the likelihood that the input video-audio pair is in sync, the lip-sync expert is pre-trained.

A LipGAN-like architecture is employed during Wav2Lip training. A face decoder, a visual encoder, and a speech encoder are all included in the generator. Convolutional layer stacks make up all three. Convolutional blocks also serve as the discriminator. The modified LipGAN is taught similarly to previous GANs: the discriminator is trained to discriminate between frames produced by the generator and the ground-truth frames, and the generator is trained to minimize the adversarial loss depending on the discriminator’s score. In total, a weighted sum of the following loss components is minimized in order to train the generator:

A loss of L1 reconstruction between the ground-truth and produced frames

A breach of synchronization between the lip-sync expert’s input audio and the output video frames

Depending on the discriminator score, an adversarial loss between the generated and ground-truth frames

After inference, it provide the audio speech from the previous TTS block and the video frames with the avatar figure to the Wav2Lip model. The avatar speaks the speech in a lip-synced video that is produced by the trained Wav2Lip model.

Lip synchronization is present in the Wav2Lip-generated movie, although the resolution around the mouth region is reduced. To enhance the face quality in the produced video frames, it might optionally add a GFPGAN model after Wav2Lip. The GFPGAN model uses face restoration to predict a high-quality image from an input facial image that has unknown deterioration. A pretrained face GAN (like Style-GAN2) is used as a prior in this U-Net degradation removal module. A more vibrant and lifelike avatar representation results from prettraining the GFPGAN model to recover high-quality facial information in its output frames.

SadTalker

It provides another cutting-edge model option for facial animation in addition to Wav2Lip. The 3D motion coefficients (head, stance, and expression) of a 3D Morphable Model (3DMM) are produced from audio by SadTalker, a stylized audio-driven talking-head video creation tool. The input image is then sent through a 3D-aware face renderer using these coefficients, which are mapped to 3D key points. A lifelike talking head video is the result.

Intel made it possible to use the Wav2Lip model on Intel Gaudi Al accelerators and the SadTalker and Wav2Lip models on Intel Xeon Scalable processors.

Read more on Govindhtech.com

[murdotranscribes]

[Profile picture transcription: An eye shape with a rainbow flag covering the whites. The iris in the middle is red, with a white d20 for a pupil. End transcription.]

Hello! This is a blog specifically dedicated to image transcriptions. My main blog is @murdomaclachlan.

For those who don't know, I used to be part of r/TranscribersOfReddit, a Reddit community dedicated to transcribing posts to improve accessibility. That project sadly had to shut down, partially as a result of the whole fiasco with Reddit's API changes. But I miss transcribing and I often see posts on Tumblr with no alt text and no transcription.

So! Here I am, making a new blog. I'll be transcribing posts that need it when I see them and I have time; likely mainly ones I see on my dashboard. I also have asks open so anyone can request posts or images.

I have plenty of experience transcribing but that doesn't mean I'm perfect. We can always learn to be better and I'm not visually impaired myself, so if you have any feedback on how I can improve my transcriptions please don't hesitate to tell me. Just be friendly about it.

The rest of this post is an FAQ, adapted from one I posted on Reddit.

1. Why do you do transcriptions?

Transcriptions help improve the accessibility of posts. Tumblr has capabilities for adding alt-text to images, but not everyone uses it, and it has a character limit that can hamper descriptions for complex images. The following is a non-exhaustive list of the ways transcriptions improve accessibility:

They help visually-impaired people. Most visually-impaired people rely on screen readers, technology that reads out what's on the screen, but this technology can't read out images.

They help people who have trouble reading any small, blurry or oddly formatted text.

In some cases they're helpful for people with colour deficiencies, particularly if there is low contrast.

They help people with bad internet connections, who might as a result not be able to load images at high quality or at all.

They can provide context or note small details many people may otherwise miss when first viewing a post.

They are useful for search engine indexing and the preservation of images.

They can provide data for improving OCR (Optical Character Recognition) technology.

2. Why don't you just use OCR or AI?

OCR (Optical Character Recoginition) is technology that detects and transcribes text in an image. However, it is currently insufficient for accessibility purposes for three reasons:

It can and does get a lot wrong. It's most accurate on simple images of plain text (e.g. screenshots of social media posts) but even there produces errors from time to time. Accessibility services have to be as close to 100% accuracy as possible. OCR just isn't reliable enough for that.

Even were OCR able to 100%-accurately describe text, there are many portions of images that don't have text, or relevant context that should be placed in transcriptions to aid understanding. OCR can't do this.

"AI" in terms of what most people mean by it - generative AI - should never be used for anything where accuracy is a requirement. Generative AI doesn't answer questions, it doesn't describe images, and it doesn't read text. It takes a prompt and it generates a statistically-likely response. No matter how well-trained it is, there's always a chance that it makes up nonsense. That simply isn't acceptable for accessibility.

3. Why do you say "image transcription" and not "image ID"?

I'm from r/TranscribersOfReddit and we called them transcriptions there. It's ingrained in my mind.

For the same reason, I follow advice and standards from our old guidelines that might not exactly match how many Tumblr transcribers do things.

[tsreviews]

AvatoAI Review: Unleashing the Power of AI in One Dashboard

Here's what Avato Ai can do for you

Data Analysis:

Analyze CV, Excel, or JSON files using Python and libraries like pandas or matplotlib.

Clean data, calculate statistical information and visualize data through charts or plots.

Document Processing:

Extract and manipulate text from text files or PDFs.

​Perform tasks such as searching for specific strings, replacing content, and converting text to different formats.

Image Processing:

Upload image files for manipulation using libraries like OpenCV.

​Perform operations like converting images to grayscale, resizing, and detecting shapes or

Machine Learning:

Utilize Python's machine learning libraries for predictions, clustering, natural language processing, and image recognition by uploading

Versatile & Broad Use Cases:

An incredibly diverse range of applications. From creating inspirational art to modeling scientific scenarios, to designing novel game elements, and more.

User-Friendly API Interface:

Access and control the power of this advanced Al technology through a user-friendly API.

​Even if you're not a machine learning expert, using the API is easy and quick.

Customizable Outputs:

Lets you create custom visual content by inputting a simple text prompt.

​The Al will generate an image based on your provided description, enhancing the creativity and efficiency of your work.

Stable Diffusion API:

Enrich Your Image Generation to Unprecedented Heights.

Stable diffusion API provides a fine balance of quality and speed for the diffusion process, ensuring faster and more reliable results.

Multi-Lingual Support:

Generate captivating visuals based on prompts in multiple languages.

Set the panorama parameter to 'yes' and watch as our API stitches together images to create breathtaking wide-angle views.

Variation for Creative Freedom:

Embrace creative diversity with the Variation parameter. Introduce controlled randomness to your generated images, allowing for a spectrum of unique outputs.

Efficient Image Analysis:

Save time and resources with automated image analysis. The feature allows the Al to sift through bulk volumes of images and sort out vital details or tags that are valuable to your context.

Advance Recognition:

The Vision API integration recognizes prominent elements in images - objects, faces, text, and even emotions or actions.

Interactive "Image within Chat' Feature:

Say goodbye to going back and forth between screens and focus only on productive tasks.

​Here's what you can do with it:

Visualize Data:

Create colorful, informative, and accessible graphs and charts from your data right within the chat.

​Interpret complex data with visual aids, making data analysis a breeze!

Manipulate Images:

Want to demonstrate the raw power of image manipulation? Upload an image, and watch as our Al performs transformations, like resizing, filtering, rotating, and much more, live in the chat.

Generate Visual Content:

Creating and viewing visual content has never been easier. Generate images, simple or complex, right within your conversation

Preview Data Transformation:

If you're working with image data, you can demonstrate live how certain transformations or operations will change your images.

This can be particularly useful for fields like data augmentation in machine learning or image editing in digital graphics.

Effortless Communication:

Say goodbye to static text as our innovative technology crafts natural-sounding voices. Choose from a variety of male and female voice types to tailor the auditory experience, adding a dynamic layer to your content and making communication more effortless and enjoyable.

Enhanced Accessibility:

Break barriers and reach a wider audience. Our Text-to-Speech feature enhances accessibility by converting written content into audio, ensuring inclusivity and understanding for all users.

Customization Options:

Tailor the audio output to suit your brand or project needs.

​From tone and pitch to language preferences, our Text-to-Speech feature offers customizable options for the truest personalized experience.

>>>Get More Info<<<

[mindyourtopics44]

25 Python Projects to Supercharge Your Job Search in 2024

Introduction: In the competitive world of technology, a strong portfolio of practical projects can make all the difference in landing your dream job. As a Python enthusiast, building a diverse range of projects not only showcases your skills but also demonstrates your ability to tackle real-world challenges. In this blog post, we'll explore 25 Python projects that can help you stand out and secure that coveted position in 2024.

1. Personal Portfolio Website

Create a dynamic portfolio website that highlights your skills, projects, and resume. Showcase your creativity and design skills to make a lasting impression.

2. Blog with User Authentication

Build a fully functional blog with features like user authentication and comments. This project demonstrates your understanding of web development and security.

3. E-Commerce Site

Develop a simple online store with product listings, shopping cart functionality, and a secure checkout process. Showcase your skills in building robust web applications.

4. Predictive Modeling

Create a predictive model for a relevant field, such as stock prices, weather forecasts, or sales predictions. Showcase your data science and machine learning prowess.

5. Natural Language Processing (NLP)

Build a sentiment analysis tool or a text summarizer using NLP techniques. Highlight your skills in processing and understanding human language.

6. Image Recognition

Develop an image recognition system capable of classifying objects. Demonstrate your proficiency in computer vision and deep learning.

7. Automation Scripts

Write scripts to automate repetitive tasks, such as file organization, data cleaning, or downloading files from the internet. Showcase your ability to improve efficiency through automation.

8. Web Scraping

Create a web scraper to extract data from websites. This project highlights your skills in data extraction and manipulation.

9. Pygame-based Game

Develop a simple game using Pygame or any other Python game library. Showcase your creativity and game development skills.

10. Text-based Adventure Game

Build a text-based adventure game or a quiz application. This project demonstrates your ability to create engaging user experiences.

11. RESTful API

Create a RESTful API for a service or application using Flask or Django. Highlight your skills in API development and integration.

12. Integration with External APIs

Develop a project that interacts with external APIs, such as social media platforms or weather services. Showcase your ability to integrate diverse systems.

13. Home Automation System

Build a home automation system using IoT concepts. Demonstrate your understanding of connecting devices and creating smart environments.

14. Weather Station

Create a weather station that collects and displays data from various sensors. Showcase your skills in data acquisition and analysis.

15. Distributed Chat Application

Build a distributed chat application using a messaging protocol like MQTT. Highlight your skills in distributed systems.

16. Blockchain or Cryptocurrency Tracker

Develop a simple blockchain or a cryptocurrency tracker. Showcase your understanding of blockchain technology.

17. Open Source Contributions

Contribute to open source projects on platforms like GitHub. Demonstrate your collaboration and teamwork skills.

18. Network or Vulnerability Scanner

Build a network or vulnerability scanner to showcase your skills in cybersecurity.

19. Decentralized Application (DApp)

Create a decentralized application using a blockchain platform like Ethereum. Showcase your skills in developing applications on decentralized networks.

20. Machine Learning Model Deployment

Deploy a machine learning model as a web service using frameworks like Flask or FastAPI. Demonstrate your skills in model deployment and integration.

21. Financial Calculator

Build a financial calculator that incorporates relevant mathematical and financial concepts. Showcase your ability to create practical tools.

22. Command-Line Tools

Develop command-line tools for tasks like file manipulation, data processing, or system monitoring. Highlight your skills in creating efficient and user-friendly command-line applications.

23. IoT-Based Health Monitoring System

Create an IoT-based health monitoring system that collects and analyzes health-related data. Showcase your ability to work on projects with social impact.

24. Facial Recognition System

Build a facial recognition system using Python and computer vision libraries. Showcase your skills in biometric technology.

25. Social Media Dashboard

Develop a social media dashboard that aggregates and displays data from various platforms. Highlight your skills in data visualization and integration.

Conclusion: As you embark on your job search in 2024, remember that a well-rounded portfolio is key to showcasing your skills and standing out from the crowd. These 25 Python projects cover a diverse range of domains, allowing you to tailor your portfolio to match your interests and the specific requirements of your dream job.

If you want to know more, Click here:https://analyticsjobs.in/question/what-are-the-best-python-projects-to-land-a-great-job-in-2024/

[harinikhb30]

Navigating the Cloud Landscape: Unleashing Amazon Web Services (AWS) Potential

In the ever-evolving tech landscape, businesses are in a constant quest for innovation, scalability, and operational optimization. Enter Amazon Web Services (AWS), a robust cloud computing juggernaut offering a versatile suite of services tailored to diverse business requirements. This blog explores the myriad applications of AWS across various sectors, providing a transformative journey through the cloud.

Harnessing Computational Agility with Amazon EC2

Central to the AWS ecosystem is Amazon EC2 (Elastic Compute Cloud), a pivotal player reshaping the cloud computing paradigm. Offering scalable virtual servers, EC2 empowers users to seamlessly run applications and manage computing resources. This adaptability enables businesses to dynamically adjust computational capacity, ensuring optimal performance and cost-effectiveness.

Redefining Storage Solutions

AWS addresses the critical need for scalable and secure storage through services such as Amazon S3 (Simple Storage Service) and Amazon EBS (Elastic Block Store). S3 acts as a dependable object storage solution for data backup, archiving, and content distribution. Meanwhile, EBS provides persistent block-level storage designed for EC2 instances, guaranteeing data integrity and accessibility.

Streamlined Database Management: Amazon RDS and DynamoDB

Database management undergoes a transformation with Amazon RDS, simplifying the setup, operation, and scaling of relational databases. Be it MySQL, PostgreSQL, or SQL Server, RDS provides a frictionless environment for managing diverse database workloads. For enthusiasts of NoSQL, Amazon DynamoDB steps in as a swift and flexible solution for document and key-value data storage.

Networking Mastery: Amazon VPC and Route 53

AWS empowers users to construct a virtual sanctuary for their resources through Amazon VPC (Virtual Private Cloud). This virtual network facilitates the launch of AWS resources within a user-defined space, enhancing security and control. Simultaneously, Amazon Route 53, a scalable DNS web service, ensures seamless routing of end-user requests to globally distributed endpoints.

Global Content Delivery Excellence with Amazon CloudFront

Amazon CloudFront emerges as a dynamic content delivery network (CDN) service, securely delivering data, videos, applications, and APIs on a global scale. This ensures low latency and high transfer speeds, elevating user experiences across diverse geographical locations.

AI and ML Prowess Unleashed

AWS propels businesses into the future with advanced machine learning and artificial intelligence services. Amazon SageMaker, a fully managed service, enables developers to rapidly build, train, and deploy machine learning models. Additionally, Amazon Rekognition provides sophisticated image and video analysis, supporting applications in facial recognition, object detection, and content moderation.

Big Data Mastery: Amazon Redshift and Athena

For organizations grappling with massive datasets, AWS offers Amazon Redshift, a fully managed data warehouse service. It facilitates the execution of complex queries on large datasets, empowering informed decision-making. Simultaneously, Amazon Athena allows users to analyze data in Amazon S3 using standard SQL queries, unlocking invaluable insights.

In conclusion, Amazon Web Services (AWS) stands as an all-encompassing cloud computing platform, empowering businesses to innovate, scale, and optimize operations. From adaptable compute power and secure storage solutions to cutting-edge AI and ML capabilities, AWS serves as a robust foundation for organizations navigating the digital frontier. Embrace the limitless potential of cloud computing with AWS – where innovation knows no bounds.

[siddaling]

Advanced Techniques in Full-Stack Development

Certainly, let's delve deeper into more advanced techniques and concepts in full-stack development:

1. Server-Side Rendering (SSR) and Static Site Generation (SSG):

SSR: Rendering web pages on the server side to improve performance and SEO by delivering fully rendered pages to the client.

SSG: Generating static HTML files at build time, enhancing speed, and reducing the server load.

2. WebAssembly:

WebAssembly (Wasm): A binary instruction format for a stack-based virtual machine. It allows high-performance execution of code on web browsers, enabling languages like C, C++, and Rust to run in web applications.

3. Progressive Web Apps (PWAs) Enhancements:

Background Sync: Allowing PWAs to sync data in the background even when the app is closed.

Web Push Notifications: Implementing push notifications to engage users even when they are not actively using the application.

4. State Management:

Redux and MobX: Advanced state management libraries in React applications for managing complex application states efficiently.

Reactive Programming: Utilizing RxJS or other reactive programming libraries to handle asynchronous data streams and events in real-time applications.

5. WebSockets and WebRTC:

WebSockets: Enabling real-time, bidirectional communication between clients and servers for applications requiring constant data updates.

WebRTC: Facilitating real-time communication, such as video chat, directly between web browsers without the need for plugins or additional software.

6. Caching Strategies:

Content Delivery Networks (CDN): Leveraging CDNs to cache and distribute content globally, improving website loading speeds for users worldwide.

Service Workers: Using service workers to cache assets and data, providing offline access and improving performance for returning visitors.

7. GraphQL Subscriptions:

GraphQL Subscriptions: Enabling real-time updates in GraphQL APIs by allowing clients to subscribe to specific events and receive push notifications when data changes.

8. Authentication and Authorization:

OAuth 2.0 and OpenID Connect: Implementing secure authentication and authorization protocols for user login and access control.

JSON Web Tokens (JWT): Utilizing JWTs to securely transmit information between parties, ensuring data integrity and authenticity.

9. Content Management Systems (CMS) Integration:

Headless CMS: Integrating headless CMS like Contentful or Strapi, allowing content creators to manage content independently from the application's front end.

10. Automated Performance Optimization:

Lighthouse and Web Vitals: Utilizing tools like Lighthouse and Google's Web Vitals to measure and optimize web performance, focusing on key user-centric metrics like loading speed and interactivity.

11. Machine Learning and AI Integration:

TensorFlow.js and ONNX.js: Integrating machine learning models directly into web applications for tasks like image recognition, language processing, and recommendation systems.

12. Cross-Platform Development with Electron:

Electron: Building cross-platform desktop applications using web technologies (HTML, CSS, JavaScript), allowing developers to create desktop apps for Windows, macOS, and Linux.

13. Advanced Database Techniques:

Database Sharding: Implementing database sharding techniques to distribute large databases across multiple servers, improving scalability and performance.

Full-Text Search and Indexing: Implementing full-text search capabilities and optimized indexing for efficient searching and data retrieval.

14. Chaos Engineering:

Chaos Engineering: Introducing controlled experiments to identify weaknesses and potential failures in the system, ensuring the application's resilience and reliability.

15. Serverless Architectures with AWS Lambda or Azure Functions:

Serverless Architectures: Building applications as a collection of small, single-purpose functions that run in a serverless environment, providing automatic scaling and cost efficiency.

16. Data Pipelines and ETL (Extract, Transform, Load) Processes:

Data Pipelines: Creating automated data pipelines for processing and transforming large volumes of data, integrating various data sources and ensuring data consistency.

17. Responsive Design and Accessibility:

Responsive Design: Implementing advanced responsive design techniques for seamless user experiences across a variety of devices and screen sizes.

Accessibility: Ensuring web applications are accessible to all users, including those with disabilities, by following WCAG guidelines and ARIA practices.

full stack development training in Pune

[nostalgebraist-autoresponder]

Can you generate a picture of a face?

I guess you'd have to ask scikit-learn? I've never used it myself (I'm used to doing everything with TensorFlow and my own code). There might be a simple answer. I think that scikit-learn and TensorFlow can do about the same things.

EDIT: OK, I looked it up, and here is a bit from their website:

Let's take the example of face recognition. We would like to measure the effectiveness of a particular method of face recognition. Here is the TF Python API for loading images: tf.load("MNIST_images.txt"). The code above loads a text file called "MNIST_images.txt" that contains 60,000 low-resolution images of pencils, houses, faces, etc. The purpose of the "tf.load" function in TensorFlow is simply to load files into memory for us to access as tensors.

We can use a function called save_image to save an image as a file. Here is a Python call to save_image saving a MNIST image with label 2 to an image called "2.png."

I can't really understand any of this stuff, I'm just going by what it says on their website

I think what they are trying to say is "if you have a file of images, you can put it into tensorflow, load it up and work with it. If you want to work with the data as a single tensor, or do some matrix operations on the data, you should be able to do it with tensorflow."

TensorFlow and scikit-learn use different methods to do different stuff, so it's hard to say how they might compare to each other. (At least, to me.)

[dskcode]

Exploring the Possibilities of React Native Vision Camera

React Native Vision Camera is a powerful tool for developing mobile applications. With its easy-to-use API, developers can quickly and easily create powerful features such as face recognition, object detection, and image recognition. In this article, we explore the possibilities of React Native Vision Camera and discuss its potential for creating innovative mobile apps.

[toollistai]

What is Imagga?

Automatic Image Tagging: Imagga's AI-driven tagging system automatically labels images with relevant keywords, making it easy to search and categorize content without manual tagging.

Color Extraction: Identify and analyze dominant colors within images, allowing for brand alignment, design consistency, and data-driven visual insights.

Content Moderation: Ensure that images meet content standards with Imagga’s content moderation tools, helping you maintain a safe and compliant platform.

Custom Categorization: Create tailored image categories that match your specific business needs, streamlining the organization and accessibility of images.

API Integration: Seamlessly integrate Imagga’s capabilities into your application or website with a flexible API, enabling real-time image analysis and tagging.

[seositetool]

AI Vision Market worth $43.02 Billion in 2029, at a CAGR of 23.7%

AI Vision Market The global AI vision market size in terms of revenue is estimated to be worth $14.85 billion in 2024 and is poised to reach $43.02 billion in 2029, growing at a CAGR of 23.7% during the forecast period. The report “AI Vision Market by Vision Software (API, SDK), Vision Platform, Behavioral Analysis, Optical Character Recognition, Spatial Analysis, Image Recognition, Heatmap…

[masongrizchel]

Coding Diaries: TensorFlow (part 1)

This week's highlight is about my ongoing journey with TensorFlow. If you’ve ever dived into this deep-learning library, you know it’s a mix of excitement and “hold on, what does this error mean?” moments. TensorFlow’s incredible power is the backbone behind some of the most complex neural networks, from image recognition to natural language processing. But let’s be honest: it also has a knack for reminding you that no line of code is ever bug-free!

My favorite part so far has been experimenting with TensorFlow’s Keras API. It lets you stack layers and design neural networks like building with Lego blocks. Want to add a hidden layer? Just plug it in! Need an activation function? Pick one and go! It’s easy to start simple and then scale up, which is perfect for anyone (like me) balancing exploration with the thrill of just getting things to run.

Of course, TensorFlow has its fair share of challenges—debugging is always an adventure. But there’s an absolute satisfaction when you finally train a model, watch the accuracy rise, and realize that your network is learning. That’s when the long hours, countless Stack Overflow searches, and minor code crises feel entirely worth it.

[mancaimalaga]

SDEC 2024: Delegasi Kaohsiung Mempersembahkan Tata Kelola "City AI" yang Baru

 Rabu, 23 Oktober 2024 12:59 WIB

Kaohsiung, (ANTARA/PRNewswire)- Wakil Wali Kota Kaohsiung Charles Lin memimpin delegasi usaha rintisan dalam bidang smart transportation, smart healthcare, dan Asia New Bay Area untuk menghadiri Selangor Smart City and Digital Economy Convention (SDEC) 2024 di Malaysia. Wakil Wali Kota Lin juga menyampaikan sambutan di SDEC, serta membahas perkembangan dan pencapaian industri semikonduktor di Kaohsiung Smart City.

Delegasi tersebut berasal dari berbagai jenis perusahaan:

Chunghwa Telecom memamerkan dua teknologi penting: sistem Cellular Vehicle Probe (CVP) Big Data untuk analisis lalu lintas dan teknologi 5G Vehicle-to-Everything (V2X) untuk keamanan perlintasan kereta api perkotaan. Sistem CVP memantau dan menganalisis arus lalu lintas secara langsung, sedangkan teknologi V2X meningkatkan keselamatan berkendara dengan mengirim peringatan secara cepat dan pemantauan dinamis.

Advmeds memperkenalkan "Kaohsiung Health 4.0", serta mempresentasikan teknologi generative care engine untuk berbagai fasilitas medis, komunitas, dan klub kebugaran bagi warga senior. Sistem ini menawarkan manajemen kesehatan digital lewat chatbot, serta mengembangkan avatar digital AI yang memperbarui fungsi layanan pelanggan.

iAMBITION memamerkan solusi komprehensif untuk lembaga medis dan kesehatan dengan "iSAFE system platform". Platform ini menggunakan sensor 3D nirkontak (contactless), smart IoT, dan AI image recognition untuk mendeteksi anomali, memantau lingkungan hidup, serta asesmen risiko kesehatan di fasilitas kesehatan.

LTPA melansir its "Smart Cognitive Training Program," memadukan teknologi AIoT guna mendigitalisasi layanan nonfarmasi. Program ini menawarkan alat latihan jarak jauh dengan AI bagi warga senior untuk pencegahan demensia dan peningkatan kekuatan otot.

Hitspectra Intelligent Technology menggelar demo teknologi Hyperspectral Imaging in Early Medical Diagnosis. Teknologi ini mempercepat dokter mengidentifikasi bagian tubuh yang terkena penyakit tanpa gejala yang jelas, khususnya penyakit kulit. Fokus teknologi ini antara lain biomedical optical detection dan semiconductor thin film optical inspection.

Meta Intelligence memamerkan solusi inovasi digital terpadu yang mencakup AI, IoT, dan Digital Twin. Keahlian Meta Intelligence meliputi manajemen gedung yang didukung AI, foto model fesyen yang dihasilkan AI, serta sarana latihan olahraga pintar dengan VR dan AI, serta latihan belajar bahasa asing VR dengan karakter AI. Perusahaan-perusahaan ini menunjukkan beragam kapabilitas Kaohsiung dalam aplikasi AI dan 5G, mulai dari solusi-solusi smart transportation, smart healthcare, dan metaverse untuk kota pintar.

Dalam kunjungan tersebut, perusahaan-perusahaan Kaohsiung juga mengikuti sesi penjajakan kerja sama bisnis (business matchmaking) bersama sejumlah perusahaan Malaysia. Tujuannya, memperluas jangkauan di pasar Asia Tenggara. EXCO, Negara Bagian Selangor, YB Ng Sze Han, mengharapkan kolaborasi yang lebih erat setelah MoU "Smart City Strategic Partnership Alliance" ditandatangani pada Maret tahun lalu. Kunjungan tersebut merupakan perkembangan penting dalam kerja sama Taiwan-Malaysia di bidang teknologi pintar, serta melambangkan komitmen kedua pihak terhadap inovasi dan pengembangan perkotaan yang berkelanjutan.

SOURCE Kaohsiung City Government

[quantumitinnovation]

A Beginner’s Guide to AI App Development: Everything You Need to Know

Artificial intelligence (AI) has quickly become an essential component in app development, offering a range of tools and techniques that enable developers to create highly responsive, intuitive, and intelligent applications. From personalization and predictive analytics to enhanced security and automation, AI app development brings unprecedented capabilities to mobile applications, transforming the user experience.

In this guide, we’ll cover the basics of AI app development—what it is, the benefits it brings, the technologies involved, and how partnering with an experienced iOS app development agency can make a difference.

What is AI App Development?

AI app development involves integrating artificial intelligence technologies into mobile or web applications. AI enables apps to mimic human intelligence, learn from data, and adapt over time. AI-powered apps can perform tasks like speech recognition, image processing, predictive analysis, and automation, all of which enhance the user experience and create smarter, more efficient applications.

Key AI technologies commonly used in app development include:

Machine Learning (ML): Enables apps to learn from data and make predictions.

Natural Language Processing (NLP): Allows apps to understand and process human language.

Computer Vision: Enables image and video analysis within applications.

Deep Learning: Simulates the human brain’s neural networks, offering powerful pattern recognition capabilities.

Benefits of AI in App Development

Personalized User Experience AI enables developers to create highly personalized experiences by analyzing user preferences and behavior. For example, e-commerce apps can offer product recommendations based on a user’s past purchases, while social media apps can tailor content feeds to align with a user’s interests.

Predictive Analytics AI-powered predictive analytics help businesses anticipate user needs, making apps more responsive and valuable. This can include everything from suggesting content to forecasting user behavior, helping to increase engagement and drive retention.

Automated Customer Support AI chatbots are widely used in mobile applications to handle customer inquiries, providing instant support and answering common questions. These chatbots use Natural Language Processing (NLP) to understand and respond to user requests, creating a smoother customer service experience.

Enhanced Security AI’s ability to analyze vast amounts of data allows it to detect anomalies, making it ideal for identifying fraudulent activity or security threats. With AI-driven security features like biometric authentication and behavior analysis, apps can safeguard user data effectively.

Efficiency and Productivity Automation is one of the biggest advantages of AI. By automating repetitive tasks or offering intelligent insights, AI can significantly increase productivity within an app, allowing users to accomplish more in less time.

Key Technologies in AI App Development

Machine Learning (ML) Frameworks ML frameworks like TensorFlow and PyTorch allow developers to build, train, and deploy machine learning models in mobile applications. These frameworks are crucial for incorporating functionalities like image recognition, language translation, and data-driven predictions.

Natural Language Processing (NLP) Tools NLP tools, such as Google Dialogflow and IBM Watson, make it easier for apps to understand and process human language. This is especially useful for creating chatbots, voice assistants, and other conversational interfaces.

Computer Vision APIs Computer vision is often used for image recognition, object detection, and facial recognition within applications. Frameworks like OpenCV and Core ML for iOS apps allow developers to integrate computer vision functionalities easily.

Cloud-Based AI Services Platforms like Google Cloud AI and AWS AI provide pre-built models and APIs for a range of AI capabilities, such as speech recognition, translation, and image analysis. These services allow developers to quickly add AI features without building complex algorithms from scratch.

Steps to Getting Started with AI App Development

Define Your AI Goals Before diving into development, outline the specific AI functionalities you want in your app. Are you looking to improve personalization, add chatbot functionality, or enhance security? Identifying your goals will help you choose the right AI technologies and frameworks.

Choose an AI Development Framework The choice of AI framework depends on your goals and platform. Core ML is a powerful choice for iOS applications, while TensorFlow and Keras are widely used across both iOS and Android. Working with an iOS app development agency can help you make the best choice based on your project requirements.

Prepare and Analyze Data Data is the backbone of AI. For your app’s AI features to perform accurately, you need quality data. If you're building a recommendation system, for instance, gather data on user preferences, purchase history, and browsing behavior to train your algorithms effectively.

Develop and Train Your Model Once you have your data, you’ll need to train your AI model. This involves feeding your data into the model, testing its performance, and fine-tuning it to improve accuracy. Many developers use cloud-based solutions for training, as they offer high computing power and storage for large datasets.

Integrate AI with Your App After training, integrate your model with your app’s backend or directly with the frontend if using a mobile AI framework. iOS developers often use Core ML to implement ML models directly within an iOS app, ensuring fast performance and a smooth user experience.

Testing and Optimization Thorough testing is essential to ensure your AI features work as expected. AI models can behave unpredictably in real-world scenarios, so testing with a diverse set of data is crucial. Additionally, performance optimization ensures that AI features don’t drain battery life or slow down the app.

Continuous Learning and Updates AI models benefit from continuous learning. By gathering new data from user interactions, you can retrain and improve your model over time. This helps keep your AI features accurate and responsive to evolving user needs.

Partnering with an iOS App Development Agency for AI Integration

For businesses venturing into AI app development for the first time, partnering with an experienced iOS app development agency can make the process smoother and more efficient. An agency provides expertise in AI technology, iOS development, and app testing, ensuring that your AI-powered app delivers optimal performance.

An experienced team can also guide you in selecting the right AI tools, frameworks, and strategies to meet your business goals. By collaborating with experts, you can reduce development time, avoid common pitfalls, and ensure that your app’s AI features are implemented effectively.

Conclusion

AI app development is a game-changer, empowering businesses to build intelligent, engaging, and responsive applications. With the power of AI, you can create apps that deliver personalized user experiences, enhance customer support, improve security, and provide valuable insights.

Getting started with AI app development may seem complex, but understanding the basics and working with the right team can make the process rewarding. Ready to create an AI-powered app? Talk to our expert today to learn how our team can help you integrate cutting-edge AI solutions and bring your app idea to life.

[govindhtech]

AWS Amplify Features For Building Scalable Full-Stack Apps

AWS Amplify features

Build

Summary

Create an app backend using Amplify Studio or Amplify CLI, then connect your app to your backend using Amplify libraries and UI elements.

Verification

With a fully-managed user directory and pre-built sign-up, sign-in, forgot password, and multi-factor auth workflows, you can create smooth onboarding processes. Additionally, Amplify offers fine-grained access management for web and mobile applications and enables login with social providers like Facebook, Google Sign-In, or Login With Amazon. Amazon Cognito is used.

Data Storage

Make use of an on-device persistent storage engine that is multi-platform (iOS, Android, React Native, and Web) and driven by GraphQL to automatically synchronize data between desktop, web, and mobile apps and the cloud. Working with distributed, cross-user data is as easy as working with local-only data thanks to DataStore’s programming style, which leverages shared and distributed data without requiring extra code for offline and online scenarios. Utilizing AWS AppSync.

Analysis

Recognize how your iOS, Android, or online consumers behave. Create unique user traits and in-app analytics, or utilize auto tracking to monitor user sessions and web page data. To increase customer uptake, engagement, and retention, gain access to a real-time data stream, analyze it for customer insights, and develop data-driven marketing plans. Amazon Kinesis and Amazon Pinpoint are the driving forces.

API

To access, modify, and aggregate data from one or more data sources, including Amazon DynamoDB, Amazon Aurora Serverless, and your own custom data sources with AWS Lambda, send secure HTTP queries to GraphQL and REST APIs. Building scalable apps that need local data access for offline situations, real-time updates, and data synchronization with configurable conflict resolution when devices are back online is made simple with Amplify. powered by Amazon API Gateway and AWS AppSync.

Functions

Using the @function directive in the Amplify CLI, you can add a Lambda function to your project that you can use as a datasource in your GraphQL API or in conjunction with a REST API. Using the CLI, you can modify the Lambda execution role policies for your function to gain access to additional resources created and managed by the CLI. You may develop, test, and deploy Lambda functions using the Amplify CLI in a variety of runtimes. After choosing a runtime, you can choose a function template for the runtime to aid in bootstrapping your Lambda function.

GEO

In just a few minutes, incorporate location-aware functionalities like maps and location search into your JavaScript online application. In addition to updating the Amplify Command Line Interface (CLI) tool with support for establishing all necessary cloud location services, Amplify Geo comes with pre-integrated map user interface (UI) components that are based on the well-known MapLibre open-source library. For greater flexibility and sophisticated visualization possibilities, you can select from a variety of community-developed MapLibre plugins or alter embedded maps to fit the theme of your app. Amazon Location Service is the driving force.

Interactions

With only one line of code, create conversational bots that are both interactive and captivating using the same deep learning capabilities that underpin Amazon Alexa. When it comes to duties like automated customer chat support, product information and recommendations, or simplifying routine job chores, chatbots can be used to create fantastic user experiences. Amazon Lex is the engine.

Forecasts

Add AI/ML features to your app to make it better. Use cases such as text translation, speech creation from text, entity recognition in images, text interpretation, and text transcription are all simply accomplished. Amplify makes it easier to orchestrate complex use cases, such as leveraging GraphQL directives to chain numerous AI/ML activities and uploading photos for automatic training. powered by Amazon Sagemaker and other Amazon Machine Learning services.

PubSub

Transmit messages between your app’s backend and instances to create dynamic, real-time experiences. Connectivity to cloud-based message-oriented middleware is made possible by Amplify. Generic MQTT Over WebSocket Providers and AWS IoT services provide the power.

Push alerts

Increase consumer interaction by utilizing analytics and marketing tools. Use consumer analytics to better categorize and target your clientele. You have the ability to customize your content and interact via a variety of channels, such as push alerts, emails, and texts. Pinpoint from Amazon powers this.

Keeping

User-generated content, including images and movies, can be safely stored on a device or in the cloud. A straightforward method for managing user material for your app in public, protected, or private storage buckets is offered by the AWS Amplify Storage module. Utilize cloud-scale storage to make the transition from prototype to production of your application simple. Amazon S3 is the power source.

Ship

Summary

Static web apps can be hosted using the Amplify GUI or CLI.

Amplify Hosting

Fullstack web apps may be deployed and hosted with AWS Amplify’s fully managed service, which includes integrated CI/CD workflows that speed up your application release cycle. A frontend developed with single page application frameworks like React, Angular, Vue, or Gatsby and a backend built with cloud resources like GraphQL or REST APIs, file and data storage, make up a fullstack serverless application. Changes to your frontend and backend are deployed in a single workflow with each code commit when you simply connect your application’s code repository in the Amplify console.

Manage and scale

Summary

To manage app users and content, use Amplify Studio.

Management of users

Authenticated users can be managed with Amplify Studio. Without going through verification procedures, create and modify users and groups, alter user properties, automatically verify signups, and more.

Management of content

Through Amplify Studio, developers may grant testers and content editors access to alter the app data. Admins can render rich text by saving material as markdown.

Override the resources that are created

Change the fine-grained backend resource settings and use CDK to override them. The heavy lifting is done for you by Amplify. Amplify, for instance, can be used to add additional Cognito resources to your backend with default settings. Use amplified override auth to override only the settings you desire.

Personalized AWS resources

In order to add custom AWS resources using CDK or CloudFormation, the Amplify CLI offers escape hatches. By using the “amplify add custom” command in your Amplify project, you can access additional Amplify-generated resources and obtain CDK or CloudFormation placeholders.

Get access to AWS resources

Infrastructure-as-Code, the foundation upon which Amplify is based, distributes resources inside your account. Use Amplify’s Function and Container support to incorporate business logic into your backend. Give your container access to an existing database or give functions access to an SNS topic so they can send an SMS.

Bring in AWS resources

With Amplify Studio, you can incorporate your current resources like your Amazon Cognito user pool and federated identities (identity pool) or storage resources like DynamoDB + S3 into an Amplify project. This will allow your storage (S3), API (GraphQL), and other resources to take advantage of your current authentication system.

Hooks for commands

Custom scripts can be executed using Command Hooks prior to, during, and following Amplify CLI actions (“amplify push,” “amplify api gql-compile,” and more). During deployment, customers can perform credential scans, initiate validation tests, and clear up build artifacts. This enables you to modify Amplify’s best-practice defaults to satisfy the operational and security requirements of your company.

Infrastructure-as-Code Export

Amplify may be integrated into your internal deployment systems or used in conjunction with your current DevOps processes and tools to enforce deployment policies. You may use CDK to export your Amplify project to your favorite toolchain by using Amplify’s export capability. The Amplify CLI build artifacts, such as CloudFormation templates, API resolver code, and client-side code generation, are exported using the “amplify export” command.

Tools

Amplify Libraries

Flutter >> JavaScript >> Swift >> Android >>

To create cloud-powered mobile and web applications, AWS Amplify provides use case-centric open source libraries. Powered by AWS services, Amplify libraries can be used with your current AWS backend or new backends made with Amplify Studio and the Amplify CLI.

Amplify UI components

An open-source UI toolkit called Amplify UI Components has cross-framework UI components that contain cloud-connected workflows. In addition to a style guide for your apps that seamlessly integrate with the cloud services you have configured, AWS Amplify offers drop-in user interface components for authentication, storage, and interactions.

The Amplify Studio

Managing app content and creating app backends are made simple with Amplify Studio. A visual interface for data modeling, authorization, authentication, and user and group management is offered by Amplify Studio. Amplify Studio produces automation templates as you develop backend resources, allowing for smooth integration with the Amplify CLI. This allows you to add more functionality to your app’s backend and establish multiple testing and team collaboration settings. You can give team members without an AWS account access to Amplify Studio so that both developers and non-developers can access the data they require to create and manage apps more effectively.

Amplify CLI toolchain

A toolset for configuring and maintaining your app’s backend from your local desktop is the Amplify Command Line Interface (CLI). Use the CLI’s interactive workflow and user-friendly use cases, such storage, API, and auth, to configure cloud capabilities. Locally test features and set up several environments. Customers can access all specified resources as infrastructure-as-code templates, which facilitates improved teamwork and simple integration with Amplify’s continuous integration and delivery process.

Amplify Hosting

Set up CI/CD on the front end and back end, host your front-end web application, build and delete backend environments, and utilize Amplify Studio to manage users and app content.

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