Now I’m going to show you a comprehensive example how you can make raw web data useful and interesting using Scrapy, SQL and Matplotlib. It’s really supposed to be just an example because there are so many types of data out there and there are so many ways to analyze them and it really comes down to what is the best for you and your business.
Scraping And Analyzing Soccer Data
Briefly, this is the process I’m going to be using now to create this example project:
Task Zero: Requirements Of Reports
Figuring out what is really needed to be done. What are our (business) goals and what reports should we create? What would a proper analysis look like?
Task One: Data Fields And Source Of Data
Planning ahead what data fields and attributes we’ll need to satisfy the requirements. Also, looking for websites where I can get data from.
Task Two: Scrapy Spiders
Creating scrapers for the website(s) that we’ve chosen in the previous task.
Task Three: Process Data
Cleaning, standardizing, normalizing, structuring and storing data into a database.
Task Four: Analyze Data
Creating reports that help you make decisions or help you understand data more.
Task Five: Conclusions
Draw conclusions based on analysis. Understand data.
Learn to Automate and Scrape the web with Headless Chrome
What Will We Learn?
“Browserify lets you require(‘modules’) in the browser by bundling up all of your dependencies.”
I pretty much didn’t understand any word in this sentence, and struggled to make sense of how this would be helpful for me as a developer.
If you have been following Data Science / Machine Learning, you just can’t miss the buzz around Deep Learning and Neural Networks. Organizations are looking for people with Deep Learning skills wherever they can. From running competitions to open sourcing projects and paying big bonuses, people are trying every possible thing to tap into this limited pool of talent. Self driving engineers are being hunted by the big guns in automobile industry, as the industry stands on the brink of biggest disruption it faced in last few decades!
If you are excited by the prospects deep learning has to offer, but have not started your journey yet – I am here to enable it. Starting with this article, I will write a series of articles on deep learning covering the popular Deep Learning libraries and their hands-on implementation.
In this article, I will introduce TensorFlow to you. After reading this article you will be able to understand application of neural networks and use TensorFlow to solve a real life problem. This article will require you to know the basics of neural networks and have familiarity with programming. Although the code in this article is in python, I have focused on the concepts and stayed as language-agnostic as possible.
Summary: To learn Go, I ported the backend of a small site I run from Python to Go, and had a fun, pain-free experience doing so.
I’ve been wanting to learn Go for a while now: I like the philosophy of a language that’s small, has a gentle learning curve, and compiles very fast (for a statically-typed language). What pushed me over the line to actually go and do it was seeing more and more fast, robust tools that are written in Go – Docker and ngrok are two I’ve used recently.
The philosophy of Go is not to everyone’s taste (no exceptions, no user-defined generics, etc), but it fit my mental model well. Simple, speedy, does things the obvious way. During the port, I was especially impressed with how robust the standard library and tooling was.
Javalin is a very lightweight web framework for Kotlin and Java, inspired by Sparkjava and koa.js. Javalin is written in Kotlin with a few functional interfaces written in Java. This was necessary to provide an enjoyable and near identical experience for both Kotlin and Java developers.
Javalin is really more library than framework; you don’t need to extend or implement anything and there are very few “Javalin-concepts” you have to learn.
REST API Simplicity
Javalin started as a fork of the Spark Java and Kotlin web framework but quickly turned into a ground-up rewrite influenced by koa.js. All of these web frameworks are inspired by the modern micro web framework grandfather: Sinatra, so if you’re coming from Ruby then Javalin shouldn’t feel too unfamiliar.
Javalin is not aiming to be a full web framework, but rather just a lightweight REST API library. There is no concept of MVC, but there is support for template engines, websockets, and static file serving for convenience. This allows you to use Javalin for both creating your RESTful API backend, as well as serving an index.html with static resources (in case you’re creating an SPA). This is practical if you don’t want to deploy to apache or nginx in addition to your Javalin service. If you wish to use Javalin to create web-pages instead of just REST APIs, there are some simple template engine wrappers available for a quick and easy setup.
If you want a more established Java web framework, you could try Spark. If you want a more established Kotlin web framework, you can try the Spark Kotlin Wrapper.
This post summarizes and links to a great multi-part tutorial series on learning the TensorFlow API for building a variety of neural networks, as well as a bonus tutorial on backpropagation from the beginning.
By Erik Hallström, Deep Learning Research Engineer.
Editor’s note: The TensorFlow API has undergone changes since this series was first published. However, the general ideas are the same, and an otherwise well-structured tutorial such as this provides a great jumping off point and opportunity to consult the API documentation to identify and implement said changes.
Schematic of a RNN processing sequential data over time.