Why Learn Elixir in 2020? — Elixir Tutorial for Beginners

At Monterail we have fancied the Elixir language for quite a long time. However, for some reasons, we failed to get it into production usage except for a few minor implementations. And I felt like this should change. So when Plataformatec shared the idea on how to learn the language together in teams, within a company, we didn’t hesitate long. In simplest terms—you go through the book for beginners called Learn Functional Programming with Elixir, dedicate each week to the next chapter and do exercises listed at the end of each chapter. Doesn’t it sound like an awesome initiative?

I want to share the reasons why I think you should learn Elixir in 2020. Let’s start with why we got interested in Elixir many years ago.

So we started learning Elixir Phoenix during regular meetings every Friday evening. And then Advent came, also to the coding life and we expanded Elixir education onto participating in the Advent of Code with the language creator, José Valim. AoC is a series of small programming puzzles for a variety of skill sets and skill levels in any programming language you like. It allowed us to learn A LOT in such a short time—it was challenging, exhausting but most above all, really fun!

And as a result of our effort, we managed to introduce Elixir to a key part of our client’s application.

We learned our lessons so I decided to share with you some of my findings, i.e. patterns, specific functions or quirks. A very experienced Elixir developer probably won’t catch much new here but if you are new in the alchemists’ community, you’re more than likely to benefit from it.

Scripting with Elixir — a short tutorial

When you come from the world similar to Ruby, you might have an issue at the beginning with organizing a workflow when you just want to script a small thing. How to test the script with Elixir? Do I need a full mix application? Do I have to recompile the code after every change? Luckily, there is a way:

defmodule Example do
  def fun(bar, baz) do
    ...
  end
end

case System.argv() do
  ["--test"] ->
    ExUnit.start()

    defmodule ExampleTest do
      use ExUnit.Case

      import Example

      test "my code" do
        assert fun() == 1
      end
    end

  [arg1, arg2] ->
    Example.fun(arg1, arg2) |> IO.puts()
end

Simplecase on System.argv() allows you to create a script with tests as simple to run as typing elixir script.exs --test.

And when talking about testing, I cannot keep myself from mentioning Doctest—it’s so cool that it should be banned.

#elixir #development #programming

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Why Learn Elixir in 2020? — Elixir Tutorial for Beginners
Mikel  Okuneva

Mikel Okuneva

1603735200

Top 10 Deep Learning Sessions To Look Forward To At DVDC 2020

The Deep Learning DevCon 2020, DLDC 2020, has exciting talks and sessions around the latest developments in the field of deep learning, that will not only be interesting for professionals of this field but also for the enthusiasts who are willing to make a career in the field of deep learning. The two-day conference scheduled for 29th and 30th October will host paper presentations, tech talks, workshops that will uncover some interesting developments as well as the latest research and advancement of this area. Further to this, with deep learning gaining massive traction, this conference will highlight some fascinating use cases across the world.

Here are ten interesting talks and sessions of DLDC 2020 that one should definitely attend:

Also Read: Why Deep Learning DevCon Comes At The Right Time


Adversarial Robustness in Deep Learning

By Dipanjan Sarkar

**About: **Adversarial Robustness in Deep Learning is a session presented by Dipanjan Sarkar, a Data Science Lead at Applied Materials, as well as a Google Developer Expert in Machine Learning. In this session, he will focus on the adversarial robustness in the field of deep learning, where he talks about its importance, different types of adversarial attacks, and will showcase some ways to train the neural networks with adversarial realisation. Considering abstract deep learning has brought us tremendous achievements in the fields of computer vision and natural language processing, this talk will be really interesting for people working in this area. With this session, the attendees will have a comprehensive understanding of adversarial perturbations in the field of deep learning and ways to deal with them with common recipes.

Read an interview with Dipanjan Sarkar.

Imbalance Handling with Combination of Deep Variational Autoencoder and NEATER

By Divye Singh

**About: **Imbalance Handling with Combination of Deep Variational Autoencoder and NEATER is a paper presentation by Divye Singh, who has a masters in technology degree in Mathematical Modeling and Simulation and has the interest to research in the field of artificial intelligence, learning-based systems, machine learning, etc. In this paper presentation, he will talk about the common problem of class imbalance in medical diagnosis and anomaly detection, and how the problem can be solved with a deep learning framework. The talk focuses on the paper, where he has proposed a synergistic over-sampling method generating informative synthetic minority class data by filtering the noise from the over-sampled examples. Further, he will also showcase the experimental results on several real-life imbalanced datasets to prove the effectiveness of the proposed method for binary classification problems.

Default Rate Prediction Models for Self-Employment in Korea using Ridge, Random Forest & Deep Neural Network

By Dongsuk Hong

About: This is a paper presentation given by Dongsuk Hong, who is a PhD in Computer Science, and works in the big data centre of Korea Credit Information Services. This talk will introduce the attendees with machine learning and deep learning models for predicting self-employment default rates using credit information. He will talk about the study, where the DNN model is implemented for two purposes — a sub-model for the selection of credit information variables; and works for cascading to the final model that predicts default rates. Hong’s main research area is data analysis of credit information, where she is particularly interested in evaluating the performance of prediction models based on machine learning and deep learning. This talk will be interesting for the deep learning practitioners who are willing to make a career in this field.


#opinions #attend dldc 2020 #deep learning #deep learning sessions #deep learning talks #dldc 2020 #top deep learning sessions at dldc 2020 #top deep learning talks at dldc 2020

Brain  Crist

Brain Crist

1594753020

Citrix Bugs Allow Unauthenticated Code Injection, Data Theft

Multiple vulnerabilities in the Citrix Application Delivery Controller (ADC) and Gateway would allow code injection, information disclosure and denial of service, the networking vendor announced Tuesday. Four of the bugs are exploitable by an unauthenticated, remote attacker.

The Citrix products (formerly known as NetScaler ADC and Gateway) are used for application-aware traffic management and secure remote access, respectively, and are installed in at least 80,000 companies in 158 countries, according to a December assessment from Positive Technologies.

Other flaws announced Tuesday also affect Citrix SD-WAN WANOP appliances, models 4000-WO, 4100-WO, 5000-WO and 5100-WO.

Attacks on the management interface of the products could result in system compromise by an unauthenticated user on the management network; or system compromise through cross-site scripting (XSS). Attackers could also create a download link for the device which, if downloaded and then executed by an unauthenticated user on the management network, could result in the compromise of a local computer.

“Customers who have configured their systems in accordance with Citrix recommendations [i.e., to have this interface separated from the network and protected by a firewall] have significantly reduced their risk from attacks to the management interface,” according to the vendor.

Threat actors could also mount attacks on Virtual IPs (VIPs). VIPs, among other things, are used to provide users with a unique IP address for communicating with network resources for applications that do not allow multiple connections or users from the same IP address.

The VIP attacks include denial of service against either the Gateway or Authentication virtual servers by an unauthenticated user; or remote port scanning of the internal network by an authenticated Citrix Gateway user.

“Attackers can only discern whether a TLS connection is possible with the port and cannot communicate further with the end devices,” according to the critical Citrix advisory. “Customers who have not enabled either the Gateway or Authentication virtual servers are not at risk from attacks that are applicable to those servers. Other virtual servers e.g. load balancing and content switching virtual servers are not affected by these issues.”

A final vulnerability has been found in Citrix Gateway Plug-in for Linux that would allow a local logged-on user of a Linux system with that plug-in installed to elevate their privileges to an administrator account on that computer, the company said.

#vulnerabilities #adc #citrix #code injection #critical advisory #cve-2020-8187 #cve-2020-8190 #cve-2020-8191 #cve-2020-8193 #cve-2020-8194 #cve-2020-8195 #cve-2020-8196 #cve-2020-8197 #cve-2020-8198 #cve-2020-8199 #denial of service #gateway #information disclosure #patches #security advisory #security bugs

Jeromy  Lowe

Jeromy Lowe

1599097440

Data Visualization in R with ggplot2: A Beginner Tutorial

A famous general is thought to have said, “A good sketch is better than a long speech.” That advice may have come from the battlefield, but it’s applicable in lots of other areas — including data science. “Sketching” out our data by visualizing it using ggplot2 in R is more impactful than simply describing the trends we find.

This is why we visualize data. We visualize data because it’s easier to learn from something that we can see rather than read. And thankfully for data analysts and data scientists who use R, there’s a tidyverse package called ggplot2 that makes data visualization a snap!

In this blog post, we’ll learn how to take some data and produce a visualization using R. To work through it, it’s best if you already have an understanding of R programming syntax, but you don’t need to be an expert or have any prior experience working with ggplot2

#data science tutorials #beginner #ggplot2 #r #r tutorial #r tutorials #rstats #tutorial #tutorials

Sival Alethea

Sival Alethea

1624305600

Learn Data Science Tutorial - Full Course for Beginners. DO NOT MISS!!!

Learn Data Science is this full tutorial course for absolute beginners. Data science is considered the “sexiest job of the 21st century.” You’ll learn the important elements of data science. You’ll be introduced to the principles, practices, and tools that make data science the powerful medium for critical insight in business and research. You’ll have a solid foundation for future learning and applications in your work. With data science, you can do what you want to do, and do it better. This course covers the foundations of data science, data sourcing, coding, mathematics, and statistics.
⭐️ Course Contents ⭐️
⌨️ Part 1: Data Science: An Introduction: Foundations of Data Science

  • Welcome (1.1)
  • Demand for Data Science (2.1)
  • The Data Science Venn Diagram (2.2)
  • The Data Science Pathway (2.3)
  • Roles in Data Science (2.4)
  • Teams in Data Science (2.5)
  • Big Data (3.1)
  • Coding (3.2)
  • Statistics (3.3)
  • Business Intelligence (3.4)
  • Do No Harm (4.1)
  • Methods Overview (5.1)
  • Sourcing Overview (5.2)
  • Coding Overview (5.3)
  • Math Overview (5.4)
  • Statistics Overview (5.5)
  • Machine Learning Overview (5.6)
  • Interpretability (6.1)
  • Actionable Insights (6.2)
  • Presentation Graphics (6.3)
  • Reproducible Research (6.4)
  • Next Steps (7.1)

⌨️ Part 2: Data Sourcing: Foundations of Data Science (1:39:46)

  • Welcome (1.1)
  • Metrics (2.1)
  • Accuracy (2.2)
  • Social Context of Measurement (2.3)
  • Existing Data (3.1)
  • APIs (3.2)
  • Scraping (3.3)
  • New Data (4.1)
  • Interviews (4.2)
  • Surveys (4.3)
  • Card Sorting (4.4)
  • Lab Experiments (4.5)
  • A/B Testing (4.6)
  • Next Steps (5.1)

⌨️ Part 3: Coding (2:32:42)

  • Welcome (1.1)
  • Spreadsheets (2.1)
  • Tableau Public (2.2)
  • SPSS (2.3)
  • JASP (2.4)
  • Other Software (2.5)
  • HTML (3.1)
  • XML (3.2)
  • JSON (3.3)
  • R (4.1)
  • Python (4.2)
  • SQL (4.3)
  • C, C++, & Java (4.4)
  • Bash (4.5)
  • Regex (5.1)
  • Next Steps (6.1)

⌨️ Part 4: Mathematics (4:01:09)

  • Welcome (1.1)
  • Elementary Algebra (2.1)
  • Linear Algebra (2.2)
  • Systems of Linear Equations (2.3)
  • Calculus (2.4)
  • Calculus & Optimization (2.5)
  • Big O (3.1)
  • Probability (3.2)

⌨️ Part 5: Statistics (4:44:03)

  • Welcome (1.1)
  • Exploration Overview (2.1)
  • Exploratory Graphics (2.2)
  • Exploratory Statistics (2.3)
  • Descriptive Statistics (2.4)
  • Inferential Statistics (3.1)
  • Hypothesis Testing (3.2)
  • Estimation (3.3)
  • Estimators (4.1)
  • Measures of Fit (4.2)
  • Feature Selection (4.3)
  • Problems in Modeling (4.4)
  • Model Validation (4.5)
  • DIY (4.6)
  • Next Step (5.1)

📺 The video in this post was made by freeCodeCamp.org
The origin of the article: https://www.youtube.com/watch?v=ua-CiDNNj30&list=PLWKjhJtqVAblfum5WiQblKPwIbqYXkDoC&index=7
🔺 DISCLAIMER: The article is for information sharing. The content of this video is solely the opinions of the speaker who is not a licensed financial advisor or registered investment advisor. Not investment advice or legal advice.
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#data science #learn data science #learn data science tutorial #beginners #learn data science tutorial - full course for beginners

Myriam  Rogahn

Myriam Rogahn

1598553300

How to Use Jupyter Notebook in 2020: A Beginner’s Tutorial

What is Jupyter Notebook?

The Jupyter Notebook is an incredibly powerful tool for interactively developing and presenting data science projects. This article will walk you through how to use Jupyter Notebooks for data science projects and how to set it up on your local machine.

First, though: what is a “notebook”?

A notebook integrates code and its output into a single document that combines visualizations, narrative text, mathematical equations, and other rich media. In other words: it’s a single document where you can run code, display the output, and also add explanations, formulas, charts, and make your work more transparent, understandable, repeatable, and shareable.

Using Notebooks is now a major part of the data science workflow at companies across the globe. If your goal is to work with data, using a Notebook will speed up your workflow and make it easier to communicate and share your results.

Best of all, as part of the open source Project Jupyter, Jupyter Notebooks are completely free. You can download the software on its own, or as part of the Anaconda data science toolkit.

Although it is possible to use many different programming languages in Jupyter Notebooks, this article will focus on Python, as it is the most common use case. (Among R users, R Studio tends to be a more popular choice).

How to Follow This Tutorial

To get the most out of this tutorial you should be familiar with programming — Python and pandas specifically. That said, if you have experience with another language, the Python in this article shouldn’t be too cryptic, and will still help you get Jupyter Notebooks set up locally.

Jupyter Notebooks can also act as a flexible platform for getting to grips with pandas and even Python, as will become apparent in this tutorial.

We will:

  • Cover the basics of installing Jupyter and creating your first notebook
  • Delve deeper and learn all the important terminology
  • Explore how easily notebooks can be shared and published online.

(In fact, this article was written as a Jupyter Notebook! It’s published here in read-only form, but this is a good example of how versatile notebooks can be. In fact, most of our programming tutorials and even our Python courses were created using Jupyter Notebooks).

#data science tutorials #beginner #jupyter #jupyter notebooks #learn python #pandas #python #tutorial #tutorials