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Cost Matters! The Serverless Edition


Tags serverless lambda aws cloud

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!

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TensorFlow 2 Tutorial: Get started in Deep Learning with tf.keras


Tags big-data data-science python how-to learning

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!

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Visualizing Keras neural networks with Net2Vis and Docker


Tags big-data data-science python how-to

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!

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faasd: lightweight Serverless for your Raspberry Pi


Tags devops software-architecture containers kubernetes

Kick the tires with faasd today, for a lightweight serverless experience that doesn’t require Kubernetes. Instead it uses containerd and the existing OpenFaaS ecosystem. By Alex Ellis.

You can run faasd anywhere, even on a Raspberry Pi, but why would you want to do that? faasd offers many of the benefits of containers and OpenFaaS, but without the complexity and operational costs of Kubernetes. containerd is a low-level tool for automating containers, and a CNCF project.

Compute is compute, and OpenFaaS with containerd makes it easy to both consume and provide.

The article briefly covers:

  • Before we start and installation
  • Post-install
  • How to build a new container image
  • Deploy your new container

faasd brings a lightweight experience to your Raspberry Pi, cloud infrastructure and to bare metal, all without the need for Kubernetes. Nice read!

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8 console API functions other than console.log()


Tags javascript devops functional-programming

An in-depth guide to some important console API functions other than console.log. By Deepak Gupta.

The Console API’s most commonly used method is undoubtedly console.log. However, it also comes with several other useful methods that can improve our debugging efficiency.

The article describes some other interesting methods:

  • console.table(data[, properties])
  • console.group(label) & console.groupEnd(label)
  • console.dir(object) & console.dirxml(object)
  • console.profile([, label]) & console.profileEnd([, label])
  • console.time([, label]) & console.timeEnd([, label])

… and more. All described here methods are available in NodeJS as of version 10.0.0 and in almost every major browser. For the rest the link to original article. Nicely put together!

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Clean code applied to JavaScript - Part III. Functions


Tags javascript programming functional-programming

Carlos Caballero published this story about fundamental tips and advice to generate clean code focusing on the element that allows us to reuse our code: the functions.

Some tips to generate good code applied specifically to the variables:

  • Use default arguments instead of short circuiting or conditionals
  • Function arguments (2 or fewer ideally)
  • Avoid side effects – Global Variables
  • Avoid side effects – Objects Mutables
  • Functions should do one thing
  • Favor functional programming over imperative programming
  • Use method chaining

The design of functions applying clean code is essential because the functions are the basic element to decouple the code. Good read!

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Computer vision applications: The power and limits of deep learning


Tags big-data machine-learning miscellaneous

This article is part of Demystifying AI, a series of posts that (try to) disambiguate the jargon and myths surrounding AI. By Ben Dickson.

Before becoming too excited about advances in computer vision, it’s important to understand the limits of current AI technologies. While improvements are significant, we are still very far from having computer vision algorithms that can make sense of photos and videos in the same way as humans do.

The article focuses on:

  • Commercial applications of computer vision
    • Image search
    • Image editing and enhancement
    • Facial recognition applications
    • Data efficient home security
    • Interacting with the real world
  • Advanced applications of computer vision
    • Medical image processing
    • Playing games
    • Cashier-less stores
  • Creepy applications of computer vision
    • Surveillance
    • Autonomous weapons

… and much more. Worth to notice that renowned computer scientist and AI researcher Stuart Russell has founded an organization dedicated to stopping the development of autonomous weapons. Good read!

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Predicting contract length with probabilistic programming


Tags big-data data-science miscellaneous how-to

An article by Antoine Hachez about Jobandtalent experience with applying Artificial Intelligence (AI) to real business problems.

One of the key metrics in this industry is obviously the length of the contracts as it is proportional to the revenue per contract, which is compared to the fixed and variable costs of managing it.

The article describe how their data team tackled a challenge:

  • The modeling process
  • Modeling over time
  • Probabilistic programming – the simple model
  • Probabilistic programming – the time-dependent approach
  • Project evolution and conclusion

They describe how their approach is flexible and how they quickly iterated to create the KPIs that would fit our needs (actionable and unbiased by outliers). The Bayesian approach offers many advantages in this regard. Great read!

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How to apply Lean Principles to your startup's productivity and time management


Tags agile frameworks miscellaneous software how-to

John Rampton wrote this article about how focusing on one thing at a time is a very good start to your productivity.

If you’ve recently launched a startup, author is sure that you’ve heard a lot about being “lean.” This article is not about the methodology popularized by the likes of Eric Ries. It is actually about the term and concept of “lean” that was originally developed by Toyota executive Taiichi Ohno during the reconstruction period in Japan following World War II.

The article captures:

  • The lean principles from a Toyota exec
  • Applying “lean” to productivity in startups
  • Improve your workplace using the five principles of lean
    • Value, Value stream, Flow, Pull, Perfection
  • Use the concept of 5S to get yourself organized
  • Standardize your work to become more efficient

… and much more in this eyes opening article. We liked:

Start by keeping a time log to see when you’re most productive and how you’re spending your time. You may notice that you’re most productive in the mornings. If so, that’s when you should work on your most important task.

One way you can improve flow in your startup is by focusing on one thing at a time. Great read!

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Introducing Mocking Hans - An open source tool for creating fake APIs


Tags apis nodejs restful web-development

An article by Kevin about Hans. Hans is a small Node application for faking APIs - but not just a single REST API using HTTP. It allows for creating multiple APIs using different ports and protocols (like native WebSockets or even GraphQL). It supports multi-port/app API mocking, Express, Socket.io and native WebSocket.

On top of that it is written entirely in TypeScript which allows for some neat abstractions and sharing models between your real application and your fake API. And, of course, everything is licensed under MIT.

The article then quickly dives into details:

  • Basic example
  • How to use Adapters
  • Requests, Responses
  • Middleware
  • State

Small, nice library. Hans integrates Faker by default, providing useful methods for generating fake data. Link to repository provided.

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