Telegram Reviews

1. Flawlessflower

   10 months ago
   the best messanger sp far for me

   Very secure, straightforward,and i must say,more relevant nowadays, at least in my opinion.

   👍 Pros:    Intuitive|Easy user interface

Scikit-learn Reviews

Telegram mentions (129)

• Semi-Scripted Conversational Applications

  You can serve it in any way, either as a standalone application, a Telegram bot or a web application. We will focus on the core of the conversational application and skip the delivery method for now. - Source: dev.to / 2 months ago

• Sending Telegram Messages using Node.js and the Telegram API

  Telegram is a popular messaging app that allows users to send messages, photos, videos, and other types of media to other Telegram users. Me personally use it almost everyday as a way to communicate with family and friends, in short words I really prefer it to some more popular ones as Viber and Whatsapp. One of the great features of Telegram is that it also has an API that allows developers to interact with... - Source: dev.to / 4 months ago

• A list of SaaS, PaaS and IaaS offerings that have free tiers of interest to devops and infradev

  Telegram — Telegram is for everyone who wants fast, reliable messaging and calls. Business users and small teams may like the large groups, usernames, desktop apps, and powerful file-sharing options. - Source: dev.to / 5 months ago

• Top Growing Social Media Platforms in 2024 🚀

  (https://telegram.org/) Secure messaging app with over 500 million active users. Provides encrypted chats, group chats up to 200,000 people, file sharing and more. - Source: dev.to / 5 months ago

• How a data analyst built a smart habit tracker using Baserow 😊

  📢 Check out the new #MadeWithBaserow project for building habits! Baptiste Thivend has automated the process using Baserow, n8n, and Telegram. Source: 9 months ago

Scikit-learn mentions (29)

• Essential Deep Learning Checklist: Best Practices Unveiled

  How to Accomplish: Utilize data splitting tools in libraries like Scikit-learn to partition your dataset. Make sure the split mirrors the real-world distribution of your data to avoid biased evaluations. - Source: dev.to / 18 days ago

• How to Build a Logistic Regression Model: A Spam-filter Tutorial

  Online Courses: Coursera: "Machine Learning" by Andrew Ng EdX: "Introduction to Machine Learning" by MIT Tutorials: Scikit-learn documentation: https://scikit-learn.org/ Kaggle Learn: https://www.kaggle.com/learn Books: "Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow" by Aurélien Géron "The Elements of Statistical Learning" by Trevor Hastie, Robert Tibshirani, and Jerome Friedman By... - Source: dev.to / 4 months ago

• Link Prediction With node2vec in Physics Collaboration Network

  Firstly, we need a connection to Memgraph so we can get edges, split them into two parts (train set and test set). For edge splitting, we will use scikit-learn. In order to make a connection towards Memgraph, we will use gqlalchemy. - Source: dev.to / about 1 year ago

• WiFilter is a RaspAP install extended with a squidGuard proxy to filter adult content. Great solution for a family, schools and/or public access point

  The ML component is based on scikit-learn which differentiates it from purely list-based filters. It couples this with a full-featured wireless router (RaspAP) in a single device, so it fulfills the needs of a use case not entirely addressed by Pi-hole. Source: about 1 year ago

• PSA: You don't need fancy stuff to do good work.

  Finally, when it comes to building models and making predictions, Python and R have a plethora of options available. Libraries like scikit-learn, statsmodels, and TensorFlowin Python, or caret, randomForest, and xgboostin R, provide powerful machine learning algorithms and statistical models that can be applied to a wide range of problems. What's more, these libraries are open-source and have extensive... Source: about 1 year ago