CodeIsGo.com

The Angular CLI provides all the built tools out of the box to create, build, and test Angular applications. In this post, we will build a continuous integration (CI) build system using Github Actions. By Cory Rylan.

Described CI process will run our build and unit tests anytime we push to our repository. Using the new Github Actions feature, we can easily integrate a CI system into our Github repositories. Author can build and test our entire project with just a few commands.

To run these commands in Github Actions environment, we need to add a new dependency called Puppeteer. Puppeteer is a headless version of Chrome browser.

The fairly detailed explanation then focuses on:

• Running Angular unit tests with Puppeteer
• Integrating the Angular CLI with Github Actions

Code is well explained. And remember demo code is available in this GitHub repository. Well done!

Aman Mittal created this tutorial which focuses on utilizing Google Cloud Vision API for you React Native App. Google Cloud Vision API is a machine learning tool that can classify details from an image provided as an input into thousands of different categories with pre-trained API models.

In this tutorial, you are going to learn how to integrate Google Cloud Vision API in a React Native application and make use of real-time APIs.

The author uses Expo. Expo provides a set of tools to create and publish React Native applications with minimal effort.

The content of the tutorial:

• Installing Expo
• Setting Up Firebase
• Building The App

The code is well explained in the article. You will find the complete code inside this GitHub repo.

SQL injection, or SQLi, is an attack on a web application by compromising its database through malicious SQL statements. As it’s a common attack, let’s try to learn more about what it is, how it happens, and how to defend yourself from it. By Shaunik Daityari.

SQL injection, or SQLi, is a type of attack on a web application that enables an attacker to insert malicious SQL statements into the web application, potentially gaining access to sensitive data in the database or destroying this data.SQL injection was first discovered by Jeff Forristal in 1998.

The article deals with:

• What is SQL Injection?
• How does the SQL Injection vulnerability work?
• Types of SQL Injection
• How to prevent SQL Injections
• SQL Injection in WordPress
• Is SQL Injection illegal?

An SQL injection is prevalent even today and the severity of injection attacks in a web application is recognized widely. It is one of the top ten most critical web application security risks by OWASP. Nice one!

Alexis King wrote this piece about his concise, simple way to explain what it means to practice type-driven design. He has now a single, snappy slogan that encapsulates what type-driven design means to him, and better yet, it’s only three words long: Parse, don’t validate.

The article then explains:

• The essence of type-driven design
• Turning partial functions total
• The power of parsing
• The danger of validation
• Parsing, not validating, in practice

Author’s advice is to focus on the datatypes. And follow 2 simple ideas: Use a data structure that makes illegal states unrepresentable and push the burden of proof upward as far as possible, but no further. That said, it is not always easy. Excellent read!

By optimizing reinforcement-learning algorithms, DeepMind uncovered new details about how dopamine helps the brain learn. By Karen Hao.

In 1951, Marvin Minsky, then a student at Harvard, borrowed observations from animal behavior to try to design an intelligent machine. At the time, neuroscientists had yet to figure out the mechanisms within the brain that allow animals to learn in this way.

In a paper published in Nature, DeepMind, Alphabet’s AI subsidiary, has once again used lessons from reinforcement learning to propose a new theory about the reward mechanisms within our brains.

At a high level, reinforcement learning follows the insight derived from Pavlov’s dogs: it’s possible to teach an agent to master complex, novel tasks through only positive and negative feedback.

It turns out the brain’s reward system works in much the same way—a discovery made in the 1990s, inspired by reinforcement-learning algorithms. Read more to learn the study outcomes!

Yohan Lasorsa published this guide about how to approach your open source project development. If you plan to maintain an open-source project in the long run, what you must do is automate.

The worst way of losing time is when your automated builds are not working as intended: flakiness, unreliable builds, and cross-platforms issues can (will) drive you crazy. Pragmatism should be your main driver here, seek to get the job done for the least time invested.

Azure Pipelines gives you quite a lot for free compared to other solutions. It’s fast (offers 10 parallels workers per organization for open source tier).

The article then describes:

• How to setup Azure Pipelines for an open-source project hosted on GitHub
• Requirements, how to prepare your repository
• Create the Azure DevOps project
• Setup the pipeline, yaml config
• Adding a build matrix

CI is one of the first things you should put in place in any open source project to simplify your life as a maintainer. Good read!

Mark Noonan posted this article about what pseudo-elements are and how to use them, as well as look at some common use cases where these pseudo-elements can make life easier for us.

::before and ::after are great for decorations like custom bullet points, arrows, or other features that should go with an HTML element, but are not part of the main content of the element. Often you can achieve the same result by adding extra HTML elements to your markup, and pseudo-elements are a way to avoid clutter.

The article content is split into:

• First, why are they called pseudo-elements?
• Always set the content property
• What are these pseudo-elements used for and why?

Unlike most other elements, ::before and ::after don’t appear in the HTML code for a page.

Just remember, there are many possible uses of pseudo-elements available, and you can learn more in Kevin Powell’s awesome video series about them on YouTube. Nice one!

An article by by Leon Stigter about serverless development model and how that model works on AWS Fargate, which allows you to run containers without having to manage servers or clusters.

Serverless is a development model where developers focus on a single unit of work and can deploy to a platform that automatically scales, without developer intervention.

The article has this sections:

• Containers, like really?
• Where do your containers come from?
• What does that cost me?
• Comparing the results

Deployment scripts for AWS Faragate available in GitHub repository.

Note: there is a tradeoff between control and abstraction and as a developer you have to decide if and how you want to make that tradeoff. Nice read!

Jason Brownlee wrote this tutorial to get you started with Deep Learning in TensorFlow. Predictive modeling with deep learning is a skill that modern developers need to know.

TensorFlow is the premier open-source deep learning framework developed and maintained by Google. Although using TensorFlow directly can be challenging, the modern tf.keras API beings the simplicity and ease of use of Keras to the TensorFlow project.

The article deals with:

• The difference between Keras and tf.keras and how to install and confirm TensorFlow is working
• The 5-step life-cycle of tf.keras models and how to use the sequential and functional APIs
• How to develop MLP, CNN, and RNN models with tf.keras for regression, classification, and time series forecasting
• How to use the advanced features of the tf.keras API to inspect and diagnose your model
• How to improve the performance of your tf.keras model by reducing overfitting and accelerating training

In this tutorial, you discovered a step-by-step guide to developing deep learning models in TensorFlow using the tf.keras API. Code and links to further resources, including some data sets also included. Well done!

Visualizing the structure of your neural network is quite useful for publications, such as papers and blogs. Written by @MachineCurve (Christian).

Today, various tools exist for generating these visualizations – allowing engineers and researchers to generate them either by hand, or even (partially) automated.

In this blog post, we’ll take a look at Net2Vis. Firstly, we’ll inspect the challenges of current tools in more detail, followed by the introduction of Net2Vis. We then suggest a different way of installing it, by using our Docker-based installation process, saving you quite some time on installing dependencies. Subsequently, we’ll talk you through our experience with Net2Vis – and show you what it’s capable of.

The article then walks over these topics:

• What is Net2Vis?
• Using Net2Vis online
• How does Net2Vis work?

Often, print media requires horizontal visualizations, maintaining the natural flow of reading, while still conveying all important information. Very useful for anybody interested into machine learning. Nice one!