Atomic Mail Sender VS NumPy

Compare Atomic Mail Sender VS NumPy and see what are their differences

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Atomic Mail Sender

A desktop tool designed for efficient bulk email sending that aids businesses in identifying prospective clients and ensures successful message delivery.

NumPy

NumPy is the fundamental package for scientific computing with Python

Landing page //
2023-05-04

This program is well-suited for small businesses, agencies, and coaches seeking to connect with potential clients through mass mailing. Atomic Mail Sender offers the ability to connect to any SMTP server without abuse limitations and send email campaigns without any limits. The Spin text function and built-in spam checker enable easy passing of spam filters. Additionally, the program allows for the uploading of an unlimited number of email addresses and ensures secure communication through SSL/TLS. It also offers a variety of free email templates for different cases. You can use a coupon Saashub20 for 20% OFF on any software from AtomPark.

Landing page //
2023-05-13

Atomic Mail Sender

Website: atompark.com
Pricing URL: Official Atomic Mail Sender Pricing
$ Details: Free Trial $89.9 / One-off
Release Date: 2001 May
Categories: #Email Marketing #Newsletter Marketing #Email Marketing Platforms #Cold Outreach #Cold Emails #Cold Emailing #Email #Bulk Mass Mailing #Marketing

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NumPy

Website: numpy.org
Pricing URL: -
$ Details
Release Date: -
Categories: #Data Science And Machine Learning #Data Science Tools #Python Tools #Software Libraries

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Atomic Mail Sender features and specs

Unlimited mailing lists: The automated email system allows for unlimited recipients per mailing, providing a significant advantage for businesses with a large pool of prospects and customers. With this feature, you can effortlessly compose your newsletter and send free bulk emails.
Personalization: By utilizing various functions like personalization, spin-text, unsubscribe links, and social networking widgets, your emails have a higher chance of landing in the recipient's inbox rather than being filtered into spam folders.
Flexible SMTP settings: You have the freedom to utilize: the built-in server, which sends emails directly and maximizes speed; an unlimited number of 3rd-party SMTP servers; your own personal SMTP server.
Free trial version: Experience a free trial of our mass emailing service for 7 days.
“Unsubscribe Link” insertion: Recipients have the option to unsubscribe from your emails using the link you have provided within the message.
Text spin: By automatically inserting a variety of synonyms into the message, spin text creates a more personalized newsletter that is less likely to trigger spam filters, improving the chances of successful delivery.
Spam check: Prior to sending your email, you can assess its likelihood of being marked as spam by utilizing our built-in spam filter, "Spam Assassin," which provides a grade for your email. The lower the grade, the higher the likelihood of successful delivery to the inbox, and the filter also highlights areas for improvement to achieve a better score.
HTML and plain-text format : Atomic Mail Sender supports both plain-text and HTML formats, although users may encounter some limitations when editing in text mode. However, users can work with an alternative text part that automatically synchronizes with the HTML part.
Email tracking: You have the option to review the statistics of your sent campaigns, including the email open rate, most clicked links, CTR, and more. This feature can be utilized with Atomic Email Tracker, a web-based service for tracking sent email messages. Additionally, Atomic Mail Sender retrieves data from Google Analytics, which requires the insertion of a script onto your website pages.
Detailed reports: Following the delivery of emails, users can access generated reports that come in two forms: individual and grouped according to specific criteria. Users can also save all reports to their personal computer.

NumPy features and specs

No features have been listed yet.

Atomic Mail Sender videos

+ Add

Atomic Mail Sender — The BEST program FOR BULK EMAIL

NumPy videos

+ Add

Learn NUMPY in 5 minutes - BEST Python Library!

Category Popularity

0-100% (relative to Atomic Mail Sender and NumPy)

Atomic Mail Sender

NumPy

Email Marketing

100 100%

Email Marketing

0% 0

Data Science And Machine Learning

0 0%

Data Science And Machine Learning

100% 100

Productivity

100 100%

Productivity

0% 0

Data Science Tools

0 0%

Data Science Tools

100% 100

User comments

Share your experience with using Atomic Mail Sender and NumPy. For example, how are they different and which one is better?

Reviews

These are some of the external sources and on-site user reviews we've used to compare Atomic Mail Sender and NumPy

Atomic Mail Sender Reviews

Eniko Biro

Working at Dolphio Technologies | 9 months ago

The customer support was excellent
Every time, during the demo I had questions, they always helped me promptly.
Mick Fastrer

11 months ago

Amazing
I've been using this email extractor for a while now, and it has become an invaluable asset. It's efficient, user-friendly, and saves me time.

👍 Pros: Good price|Fast support|Knowledge base

👎 Cons: Nothing
Hannah Madna

12 months ago

High quality
As an email marketer, the Atomic Mail Sender Program fulfilled my business requirements. It enabled me to send mass emails to both my subscribers and potential new clients. Thank you.

👍 Pros: Best customer service

👎 Cons: None

NumPy Reviews

25 Python Frameworks to Master

SciPy provides a collection of algorithms and functions built on top of the NumPy. It helps to perform common scientific and engineering tasks such as optimization, signal processing, integration, linear algebra, and more.

Source: kinsta.com

Top 8 Image-Processing Python Libraries Used in Machine Learning

Scipy is used for mathematical and scientific computations but can also perform multi-dimensional image processing using the submodule scipy.ndimage. It provides functions to operate on n-dimensional Numpy arrays and at the end of the day images are just that.

Source: neptune.ai

Top Python Libraries For Image Processing In 2021

Numpy It is an open-source python library that is used for numerical analysis. It contains a matrix and multi-dimensional arrays as data structures. But NumPy can also use for image processing tasks such as image cropping, manipulating pixels, and masking of pixel values.

Source: www.analyticsvidhya.com

4 open source alternatives to MATLAB

NumPy is the main package for scientific computing with Python (as its name suggests). It can process N-dimensional arrays, complex matrix transforms, linear algebra, Fourier transforms, and can act as a gateway for C and C++ integration. It's been used in the world of game and film visual effect development, and is the fundamental data-array structure for the SciPy Stack,...

Source: opensource.com

Social recommendations and mentions

Based on our record, NumPy seems to be more popular. It has been mentiond 107 times since March 2021. 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.

Atomic Mail Sender mentions (0)

We have not tracked any mentions of Atomic Mail Sender yet. Tracking of Atomic Mail Sender recommendations started around Mar 2021.

NumPy mentions (107)

Element-wise vs Matrix vs Dot multiplication
In NumPy with * or multiply(). ` or multiply()` can multiply 0D or more D arrays by element-wise multiplication. - Source: dev.to / about 2 months ago
JSON in data science projects: tips & tricks
Data science projects often use numpy. However, numpy objects are not JSON-serializable and therefore require conversion to standard python objects in order to be saved:. - Source: dev.to / about 2 months ago
Introducing Flama for Robust Machine Learning APIs
Numpy: A library for scientific computing in Python. - Source: dev.to / 5 months ago
A Comprehensive Guide to NumPy Arrays
Python has become a preferred language for data analysis due to its simplicity and robust library ecosystem. Among these, NumPy stands out with its efficient handling of numerical data. Let’s say you’re working with numbers for large data sets—something Python’s native data structures may find challenging. That’s where NumPy arrays come into play, making numerical computations seamless and speedy. - Source: dev.to / 6 months ago
Beginning Python: Project Management With PDM
A majority of software in the modern world is built upon various third party packages. These packages help offload work that would otherwise be rather tedious. This includes interacting with cloud APIs, developing scientific applications, or even creating web applications. As you gain experience in python you'll be using more and more of these packages developed by others to power your own code. In this example... - Source: dev.to / 7 months ago