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Deno VS machine-learning in Python

Compare Deno VS machine-learning in Python and see what are their differences

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Contents:
  1. ยป Base Details
  2. ยป Videos
  3. ยป Reviews
  4. ยป Alternatives

Deno logo Deno

A secure runtime for JavaScript and TypeScript built with V8, Rust, and Tokio.

machine-learning in Python logo machine-learning in Python

Do you want to do machine learning using Python, but youโ€™re having trouble getting started? In this post, you will complete your first machine learning project using Python.
  • Deno Landing page
    Landing page //
    2023-10-15
  • machine-learning in Python Landing page
    Landing page //
    2020-01-13

Deno features and specs

  • Security
    Deno has a secure-by-default approach, requiring explicit permission for file, network, and environment access, which reduces the risk of malicious code.
  • Built-in Tooling
    Deno includes built-in tools like a dependency inspector, a code formatter, and a test runner, reducing the need for additional setup.
  • Modern JavaScript/TypeScript
    Deno supports modern JavaScript and has built-in TypeScript support, making it easier to work with contemporary codebases without additional configuration.
  • Simplified Module Management
    Deno uses URLs for importing modules, eliminating the need for a package manager like npm and simplifying dependency management.
  • Compatibility with Web Standards
    Deno aims to be browser-compatible, adhering closely to web standards like the Fetch API, making it easier to share code between the server and the client.

Possible disadvantages of Deno

  • Ecosystem Maturity
    Deno's ecosystem is relatively new compared to Node.js, resulting in fewer libraries, tools, and community resources.
  • Breaking Changes
    Due to its rapid development, Deno can have breaking changes between versions, potentially requiring more frequent updates and code adjustments.
  • Performance
    Deno's performance may not match that of optimized Node.js applications, especially for certain workloads where Node.js has been highly tuned.
  • Learning Curve
    Even though Deno is designed to be familiar to JavaScript and TypeScript developers, it introduces new concepts (like secure-by-default) that may require a learning curve.
  • Limited Enterprise Adoption
    Being relatively new, Deno has limited enterprise adoption, which might make it less appealing for large-scale or long-term projects that rely on a robust support ecosystem.

machine-learning in Python features and specs

  • Ease of Use
    Python has a simple and clean syntax, which makes it accessible for beginners and efficient for experienced developers to implement fundamental concepts of machine learning quickly.
  • Rich Ecosystem
    Python boasts a vast collection of libraries and frameworks such as scikit-learn, TensorFlow, and PyTorch that provide extensive functionalities for machine learning tasks.
  • Community Support
    Python has a large and active community that contributes to continuous improvement, support, and readily available resources like tutorials, forums, and documentation for troubleshooting.
  • Integration Capabilities
    Python can easily integrate with other languages and technologies, enabling seamless deployment of machine learning models in diverse environments.
  • Visualization Tools
    Python supports various visualization libraries like Matplotlib and Seaborn which are crucial for data analysis and understanding the performance of machine learning models.

Possible disadvantages of machine-learning in Python

  • Performance Limitations
    Python is an interpreted language and can be slower compared to compiled languages like C++ or Java, which might be a consideration for performance-intensive tasks.
  • Global Interpreter Lock (GIL)
    The GIL in Python can be a bottleneck for multi-threaded applications, limiting parallel execution and performance in CPU-bound machine learning tasks.
  • Dependency Management
    Managing dependencies can be complex in Python projects, especially when handling different versions of libraries required for specific machine learning projects.
  • Memory Consumption
    Python can require more memory for large datasets when compared with more memory-efficient languages, which might affect scalability and the ability to process very large datasets.

Analysis of Deno

Overall verdict

  • Deno is a strong option for developers who prioritize security and modern JavaScript/TypeScript features. Its out-of-the-box toolchain can simplify development by reducing dependencies on external libraries and tools.

Why this product is good

  • Deno is designed to address some of the shortcomings of Node.js. It includes built-in TypeScript support, a secure-by-default runtime, module management using URLs instead of package managers like npm, and built-in utilities for tasks such as linting, formatting, and testing.

Recommended for

    Deno is recommended for developers who are starting new projects that can benefit from its modern approach, those who prioritize security, and developers who prefer using TypeScript. However, for large-scale projects that depend heavily on Node.js's extensive package ecosystem, the transition might require additional considerations.

Deno videos

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Why nobody is using Deno?

More videos:

  • Review - What is Deno & Will it replace Node.js?
  • Review - Will Deno replace Node.js: Which programming language is better? | TechLead

machine-learning in Python videos

No machine-learning in Python videos yet. You could help us improve this page by suggesting one.

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Category Popularity

0-100% (relative to Deno and machine-learning in Python)

Deno

machine-learning in Python

Typescript
100 100%
Typescript
0% 0
Data Science And Machine Learning
0 0%
Data Science And Machine Learning
100% 100
JavaScript
100 100%
JavaScript
0% 0
Data Dashboard
0 0%
Data Dashboard
100% 100

User comments

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Social recommendations and mentions

Based on our record, Deno seems to be a lot more popular than machine-learning in Python. While we know about 201 links to Deno, we've tracked only 7 mentions of machine-learning in Python. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

Deno mentions (201)

  • 100 Most Useful Open Source Projects
    Deno โ€” https://deno.land Technology: JavaScript/TypeScript runtime. Backed / sponsored by: Deno Company + open source community. How to generate revenue: Paid hosting, enterprise support, managed Deno services, training. Description / details: Secure by default runtime by Nodeโ€™s original author; integrates TypeScript natively. - Source: dev.to / 10 months ago
  • Benchmarking in Node.js vs Deno: A Comprehensive Comparison
    Deno.bench("URL parsing", () => { new URL("https://deno.land"); }); Deno.bench("Async method", async () => { await crypto.subtle.digest("SHA-256", new Uint8Array([1, 2, 3])); }); Deno.bench({ name: "Long form", fn: () => { new URL("https://deno.land"); }, }); Deno.bench({ name: "Date.now()", group: "timing", baseline: true, fn: () => { Date.now(); }, }); Deno.bench({ name:... - Source: dev.to / over 1 year ago
  • Deno 2.0 REST API Explained: Faster, Secure JavaScript Development
    // Importing the serve function from Deno's standard library Import { serve } from "https://deno.land/std@0.196.0/http/server.ts"; // Function to handle requests Async function handler(req: Request): Promise { const { pathname, searchParams } = new URL(req.url); // Handling different routes if (pathname === "/api/greet" && req.method === "GET") { const name = searchParams.get("name") ||... - Source: dev.to / almost 2 years ago
  • Building a Simple Todo App with Deno and Oak
    Import { Application, Router } from "https://deno.land/x/oak/mod.ts";. - Source: dev.to / almost 2 years ago
  • LogTape: Zero-Dependency Logging for JavaScript That Just Works
    LogTape is a shiny new logging library for JavaScript and TypeScript that's designed with one goal in mind: to make logging simple, flexible, and hassle-free across all your JavaScript environments. Whether you're building applications for Deno, Node.js, Bun, edge functions, or browsers, LogTape has got you covered. - Source: dev.to / almost 2 years ago
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machine-learning in Python mentions (7)

  • Data science and cybersecurity with python project
    After that you should probably look at some very basic ML tutorials. I just googled it, I have no idea if this is good https://machinelearningmastery.com/machine-learning-in-python-step-by-step/. Source: over 3 years ago
  • Ask HN: How can I learn ML in 6 months as a teenager?
    Few different approaches based on search engine 'ml with python': Work though use cases / examples : https://www.databricks.com/resources/ebook/big-book-of-machine-learning-use-cases On-line class(es) / step by step projects: * https://bootcamp-sl.discover.online.purdue.edu/ai-machine-learning-certification-course * https://www.w3schools.com/python/python_ml_getting_started.asp *... - Source: Hacker News / over 3 years ago
  • Are these CS courses enough CS knowledge for ML engineer?
    MLE: ALL OF THE ABOVE (this is important - pure machine learning skills generally wonโ€™t make you hireable unless youโ€™re doing a PhD and/or are a genius) Plus: 1. https://machinelearningmastery.com/machine-learning-in-python-step-by-step/ 2. https://www.coursera.org/learn/machine-learning 3. https://www.3blue1brown.com/topics/neural-networks. Source: over 4 years ago
  • how to do i train an AI
    Have you seen this? https://machinelearningmastery.com/machine-learning-in-python-step-by-step/. Source: over 4 years ago
  • Python Data Science Project Ideas (+References)
    Machine learning models Fine-tune existing machine learning models for improved accuracy, or create your own custom models. - Source: dev.to / over 4 years ago
View more

What are some alternatives?

When comparing Deno and machine-learning in Python, you can also consider the following products

Bun.sh - Bun is an all-in-one JavaScript runtime & toolkit designed for speed, complete with a bundler, test runner, and Node.js-compatible package manager.

Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.

Node.js - Node.js is a platform built on Chrome's JavaScript runtime for easily building fast, scalable network applications

BigML - BigML's goal is to create a machine learning service extremely easy to use and seamless to integrate.

Fresh Framework - Fresh is a next generation web framework, built for speed, reliability, and simplicity.

Google Cloud TPU - Custom-built for machine learning workloads, Cloud TPUs accelerate training and inference at scale.

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