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Alternatives à GitHub pour les projets d'apprentissage machine.

Alternatives à GitHub pour les projets d’apprentissage machine.

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GitHub est un outil très populaire pour le développement de projets, mais il existe d’autres alternatives pour les projets d’apprentissage machine. Découvrez-les ici!

Alternatives populaires à GitHub pour les projets d’apprentissage automatique

2. GitLab (gitlab.com)

GitLab is an all-in-one platform that offers a wide range of features, including code review, issue tracking, and project management. It is an ideal choice for those who are looking for a comprehensive solution for their machine learning projects. It also provides an integrated CI/CD pipeline to automate the process of building, testing, and deploying ML models. Moreover, it offers a robust security system to ensure that your data remains safe and secure.

3. Bitbucket (bitbucket.org)

Bitbucket is another popular platform for managing machine learning projects. It is a great choice for teams that are looking for a powerful yet simple solution to manage their projects. It provides a comprehensive set of features, including code review, issue tracking, and project management. Additionally, it offers an integrated CI/CD pipeline to automate the process of building, testing, and deploying ML models.

Dans le monde technologique en constante évolution, la recherche continue de plateformes efficaces pour rationaliser les projets d’apprentissage automatique est toujours persistante. Il est indéniable que GitHub a ouvert un chemin facile pour les développeurs du monde entier. Cependant, nous comprenons la nécessité de la diversité et de l’innovation dans ce domaine. C’est pourquoi nous vous présentons les meilleures alternatives à GitHub qui peuvent révolutionner votre approche des projets d’apprentissage automatique. Plongeons-nous dans certaines de ces plateformes qui offrent des fonctionnalités et des fonctionnalités robustes qui peuvent facilement donner à GitHub un combat.

Alternatives populaires à GitHub pour les projets d’apprentissage automatique

1. DVC (dvc.org)

Le contrôle de version des données (DVC) est un puissant outil permettant une gestion et une collaboration rationalisées des projets. Fondamentalement, il simplifie la gestion des données en s’intégrant étroitement à Git, ce qui permet de suivre les modifications des données et des modèles de manière méticuleuse, similaire à la façon dont Git suit les variations du code. Cela favorise une approche plus organisée pour gérer de grands jeux de données et apporte un plus grand degré de reproductibilité, car les membres d’équipe peuvent facilement revenir aux versions précédentes si nécessaire.

2. GitLab (gitlab.com

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Optimisation des lignes d'objet d'email et mobile avec AI et ML

Optimisation des lignes d’objet d’email et mobile avec AI et ML

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Les entreprises peuvent désormais optimiser leurs lignes d’objet d’email et mobile grâce à l’intelligence artificielle et au machine learning. Une nouvelle ère de marketing commence !

Méthodologie

Architecture

La ligne d’objet et les titres des e-mails et des notifications push jouent un rôle important dans la détermination des taux d’engagement. La communication numérique nécessite la compétence de la conception de lignes d’objet convaincantes et de titres de notifications push concis qui captent l’attention de l’utilisateur. Les marketeurs conçoivent des lignes d’objet en fonction du ton du message à transmettre et du public cible visé. En «enseignant» efficacement cette compétence et en l’optimisant pour la communication numérique, les modèles d’IA générative offrent une avenue passionnante pour automatiser ce processus. L’article examine quelques approches pour créer des lignes d’objet et des titres de notifications push efficaces tout en les combinant avec des modèles classiques d’apprentissage automatique pour prédire les taux d’ouverture avec l’IA générative (Large Language Models).

Il ne s’agit pas seulement de créer des lignes d’objet accrocheuses que les LLM peuvent facilement générer avec le bon déclencheur. L’objectif est de générer un candidat idéal pour le contexte et le contenu qui incitera le destinataire à cliquer et à afficher le message. Les modèles d’apprentissage machine (ML), en particulier les algorithmes de forêt aléatoire, peuvent prédire avec une grande confiance la probabilité qu’un destinataire clique sur un message s’ils sont correctement formés. En combinant les LLM avec des modèles ML prédictifs, il est possible de générer des lignes d’objet et des titres de notifications push de haute qualité. Voici quelques moyens possibles.

La première approche consiste à entraîner un modèle ML prédictif sur un jeu de données historiques. Le modèle apprend à prédire le taux d’ouverture en fonction des caractéristiques telles que le sujet, le contenu et le public cible. Une fois le modèle formé, il peut être utilisé pour générer des lignes d’objet et des titres de notifications push optimaux pour chaque message. La seconde approche consiste à entraîner un modèle ML prédictif sur un jeu de données historiques tout en utilisant un modèle LLM pour générer des lignes d’objet et des titres de notifications push. Le modèle ML apprend à prédire le taux d’ouverture en fonction des caractéristiques telles que le sujet, le contenu et le public cible, tandis que le modèle LLM génère des lignes d’objet et des titres de notifications push optimaux pour chaque message. Enfin, la troisième approche consiste à entraîner un modèle ML prédictif sur un jeu de données historiques tout en utilisant un modèle LLM pour générer des lignes d’objet et des titres de notifications push optimaux pour chaque message. Le modèle ML apprend à prédire le taux d’ouverture en fonction des caractéristiques telles que le sujet, le contenu et le public cible, tandis que le modèle LLM génère des lignes d’objet et des titres de notifications

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The Importance of Machine Learning Security for Chatbots

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Artificial Intelligence is a growing industry powered by advancements from large tech companies, new startups, and university research teams alike. While AI technology is advancing at a good pace, the regulations and failsafes around machine learning security are an entirely different story.

Failure to protect your ML models from cyber attacks such as data poisoning can be extremely costly. Chatbot vulnerabilities can even result in the theft of private user data. In this article, we’ll look at the importance of machine learning cyber security. Furthermore, we’ll explain how Scanta, an ML security company, protects Chatbots through their Virtual Assistant Shield. 

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Exciting New Tools for Designers, August 2020

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The common theme in this month’s collection of new tools and resources is “things that help you show off your work.” Many of these tools are made to help you better web products or apps or showcase designs with others.

Here’s what new for designers this month.

Naturaltts

Naturaltts is an online text to speech converter, that allows you to download an mp3 recording. The tool has more than 60 voices to choose from in six languages. There’s a free plan for personal use (based on characters converted) and affordable paid plans for higher volumes and commercial users. One application of this tool is voiceover for videos or tutorials.

Handz

Handz is a library of hands with different gestures in three-dimensional shapes. The collection includes 12 gestures with nine skin colors, and three different sleeve types. Put all that together and you have 320 potential combinations that you can use for projects. The library is completely free and works in a variety of formats with different tools.

Isoflow

Isoflow allows you to create isometric diagrams for presentations and illustrations with ease. You can edit and then export diagrams for print or website use, thanks to vector rendering.

Device Shots

Device Shots is a small web app that helps you generate a high-resolution device mockup using a screenshot of your website or mobile application. It supports almost every device type you can think of and resizes for social media platforms.

Barchartrace

Barchartrace is a simple MIT open source bar chart generator. Use it to create some of the animated charts you see on social media. Just insert your information (upload via CSV file), choose animation settings, and go.

Zettlr Markdown Editor

Zettlr is a free and open source markdown editor for Mac OS. Zettlr supports simple notations, references, includes a dark mode, and tagging. It’s made for note takers who need a tool to amp up their projects, and is used primarily in higher education.

CSS Leaning Card Effect

The CSS Leaning Card Effect replicates the bookshelf feel you get when rectangles lean with a shadow against planes. Lynn Fisher does it in the pen with code that you can see and work on with your own images.

Lemon.io

Lemon.io is a tool that matches you with freelance developers to get projects moving more quickly. You are guaranteed a match in 24 hours and there is no risk if the match doesn’t work out. Just tell Lemon.io what you need and the algorithm will match you with a dev from the database. Prices for development through the platform start at $35 per hour.

Papercups

Papercups is a customer messaging tool that lets you chat in real-time. The customizable widget works with your favorite tools, such as Slack and Gmail, and is free to use. Chat apps are one of the most in-demand website features right now.

CSS Click to Animate Gif

Christian Heilmann has created a great guide/experiment in pure CSS that adds a play button on top of animated GIFs so that users can control the motion. He developed the concept because GIFs can get overwhelming and annoying. Learn how he did it and see it in action.

3D Book Image Generator

Here’s another little bit of CSS magic with a 3D Book Image Generator. Just input your image and set some specifications and get a 3D book cover image that you can use in projects. (There’s also an accompanying tutorial if you want to learn how to generate the CSS on your own.)

Luckysheet

Luckysheet is an online spreadsheet – it’s a lot like Microsoft Excel – with powerful data functions and tools. It’s user-friendly and open source. It even has quite a few built-in mathematical formulas and supports various table types.

RevKit

RevKit is a design system UI kit that works with Sketch, Figma, and Adobe XD. It includes plenty of organized components that you can pop right into designs to help get them started faster. It also includes a style guide, elements, and form controls. The download is free.

Card

Card allows you to store social media profiles, websites, and files in a customized profile. Share it in one click. Replace awkward contact exchange and multiple usernames with a simple QR code or link.

Scale Nucleus

Scale Nucleus helps visualize data, curate interesting slices within your dataset, review and manage annotations, and measure and debug model performance. This tool claims to be “the right way” to develop ML models.

Previewed

Previewed is a mockup generator to create beautiful promotional graphics for your app. Browse a variety of templates, pick one, customize, and download your design to show off.

NSFW Filter

NSFW Filter is a browser extension that blocks images that aren’t safe for work. The best part is that it runs locally in-browser and doesn’t access any of your data. Plus, it saves you from on-the-job embarrassment.

ColorFlick for Dribbble

ColorFlick for Dribbble is another browser extension that makes it easy to copy hex codes from the tool to your clipboard with ease. You can also create palettes you can share from your favorite shots using Coolors.

Tabler Icons

Tabler Icons is a collection of more than 550 SVG icons that you can customize. Change the color, size, or stroke width with on-screen controls and then click to copy the icons you want to use. It’s that simple!

Teenyicons

Teenyicons might be some of the cutest icons out there. This collection includes minimal 1px icons in outline or solid fills. And there are plenty of icons to choose from. Adjust the size and grab the ones that you need for projects.

Basicons

Basicons is a set of simple icons for product design and development. Plus, they are updated weekly.

Chozy Mermaid

Chozy Mermaid is a super funky novelty typeface to close out summer. The characters feature beach themes within slab characters. It might be hard to find an application for this one, but it is too fun not to share.

Dotuku

Dotuku is a dingbats font with a back to school theme. The limited character set features filled and outline styles that are perfect for classrooms.

Margin

Margin is a fun retro style typeface with a 1970s vibe. It’s a “chubby serif” with 60 characters and 58 glyphs.

Rollanda

Rollanda is a signature-style script with a thicker weight and rough stroke. The character set is pretty robust.

Source


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Top 10 Most Popular AI Models

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While Artificial Intelligence and Machine Learning provide ample possibilities for businesses to improve their operations and maximize their revenues, there is no such thing as a “free lunch.”

The “no free lunch” problem is the AI/ML industry adaptation of the age-old “no one-size-fits-all” problem. The array of problems the businesses face is huge, and the variety of ML models used to solve these problems is quite wide, as some algorithms are better at dealing with certain types of problems than the others. Thus said, one needs a clear understanding of what every type of ML models is good for, and today we list 10 most popular AI algorithms:


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Testing ML Models on Dual Coding Principles

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This post intends to propose a technique termed as Dual Coding for testing or performing quality control checks on Machine Learning models from quality assurance (QA) perspective. This could be useful in performing black box testing of ML models.

The proposed technique is based on the principles of Dual Coding Theory (DCT) hypothesized by Allan Paivio of the University of Western Ontario in 1971. According to Dual Coding Theory, our brain uses two different systems including verbal and non-verbal/visual to the gather, process, store, and retrieve (recall) the information related to a particular subject. One of the key assumptions of dual coding theory is the connections (also termed as referential connections) that link verbal and nonverbal representations into a complex associative network. For example, let’s say we are shown flower images and also told about the name of these flowers (such as rose, lotus etc). At a later point in time, when told about one of these flowers by name, or shown one of the images, we end up classifying them as flowers. Pay attention to the fact of one of the two systems (verbal or non-verbal/visual) get activated appropriately to classify the subject (word or images) in the correct manner. The following diagram represents different representations of a dual-coding theory.


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Assessing Quality of AI Models from QA Standpoint

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In this post, you will learn about the definition of quality of AI/Machine Learning (ML) models. Getting a good understanding of what is the high and low quality of AI models would help you design quality control checks for testing Machine Learning models and related quality assurance (QA) practices. This post would be a good read for QA professionals in general. However, it would also help set perspectives for data scientists and Machine Learning experts.

The following are some of the key quality traits that are described in detail for assessing the quality of AI models:


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Testing Features of ML Models

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In this post, you will learn about different types of test cases that you could come up for testing features of the Data Science/Machine Learning models. Testing features are one of the key sets of which needs to be performed for ensuring the high performance of Machine Learning models in a consistent and sustained manner.

Features make the most important part of a Machine Learning model. Features are nothing but the predictor variable, which is used to predict the outcome or response variable. Simply speaking, the following function represents y as the outcome variable and x1, x2, and x1x2 as predictor variables.


Source de l’article sur DZONE (AI)

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