Telegram Reviews

1. Flawlessflower

   11 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

NumPy 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

NumPy mentions (112)

• Essential Deep Learning Checklist: Best Practices Unveiled

  How to Accomplish: Develop a script that iterates over the image database, preprocesses each image according to the model's requirements (e.g., resizing, normalization), and feeds them into the model for prediction. Ensure the script can handle large datasets efficiently by implementing batch processing. Use libraries like NumPy or Pandas for data management and TensorFlow or PyTorch for model inference. Include... - Source: dev.to / 20 days ago

• Documenting my pin collection with Segment Anything: Part 3

  NumPy: This library is fundamental for handling arrays and matrices, such as for operations that involve image data. NumPy is used to manipulate image data and perform calculations for image transformations and mask operations. - Source: dev.to / 21 days ago

• Awesome List

  NumPy - The fundamental package for scientific computing with Python. NumPy Documentation - Official documentation. - Source: dev.to / 26 days ago

• NumPy for Beginners: A Basic Guide to Get You Started

  This guide covers the basics of NumPy, and there's much more to explore. Visit numpy.org for more information and examples. - Source: dev.to / 28 days ago

• 2 Minutes to JupyterLab Notebook on Docker Desktop

  Below is an example of a code cell. We'll visualize some simple data using two popular packages in Python. We'll use NumPy to create some random data, and Matplotlib to visualize it. - Source: dev.to / 10 months ago