K-means clustering is a powerful algorithm for similarity searches, and Facebook AI Research's faiss library is turning out to be a speed champion. With only a handful of lines of code shared in this demonstration, faiss outperforms the implementation in scikit-learn in speed and accuracy.
Definitely, a new one to try and see if you like the results better.
Performing repetitive tasks can bore even the most resilient of us out of our minds. Lucky for us, the digital age we live in offers us a bevvy of tools to relieve ourselves of that sort of tedious labour.
I loved this and it looks incredibly useful - worth the time to investigate so that you can use it going foward.
Tuples are very handy to use in Python programming. Every Python programmer should know its usage really well.
Some really useful code snippets that really help you to understand all of this.
Here’s a project with real demand, real use and a lot of potentials.
An interesting quick read which is worth a quick read if you are able.
How to tell exciting stories in presentation slides with elegantly re-organized charts.
A chart can make or break a presentation and so the time you invest in it now will pay dividends later.
Google has a massive impact on the tools, applications and research that help steer the data science community. As with prior years, this retrospective by Jeff Dean is amazing in its scope. Includes useful summaries, screenshots, videos and linked references throughout.
I found this really interesting and showed a number of areas that I was not aware of the scale of Google involvement. The look forward was a great place to learn of areas that are going to come at some point and worth planning for as it will be there and possible soon.
One of the easiest algorithms you’ll ever learn. You might find the Wikipedia page on Insertion Sort too.
A good reminder of this algorithm and how to use it. Please note that this is only good for small datasets as it can be slow to build the final result one at a time. Consider using Quicksort instead.
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Developing machine learning algorithms requires implementing countless libraries and integrating many supporting tools and software packages. All this magic must be written by you in yet another tool -- the IDE - that is fundamental to all your code work and can drive your productivity. These top Python IDEs and code editors are among the best tools available for you to consider and are reviewed with their noteworthy features.
This is really useful and very clear. You might find a new favourite by going through this list.
Automation has been the buzzword for a long time and will continue to be one in the times to come. It can take many forms and this great article by Sandeep explains how you can automate Excel using Python XIwings.
A very clear and very useful thing to learn. Useful code snippets to help you get your own code right.
Over the past 20+ years, it has been amazing to see how IT has been evolving to handle the ever-growing amount of data, via technologies including relational OLTP (Online Transactional Processing) database, data warehouse, ETL (Extraction, Transformation and Loading) and OLAP (Online Analytical Processing) reporting, big data and now AI, Cloud and IoT.
This was very clear and insightful. Worth a read as I think it could clear up a few misunderstandings.
Many machine learning algorithms exist that range from simple to complex in their approach, and together provide a powerful library of tools for analyzing and predicting patterns from data. If you are learning for the first time or reviewing techniques, then these intuitive explanations of the most popular machine learning models will help you kick off the new year with confidence.
This will help you get your machine learning right by using the correct algorithm.
People assume that NoSQL is a counterpart to SQL. Instead, it’s a different type of database designed for use-cases where SQL is not ideal. The differences between the two are many, although some are so crucial that they define both databases at their cores.
I enjoyed reading this thoughtful article. I think it helps to clear up some potential confusion and ensures that you really understand via his careful use of diagrams.
Demonstrating the efficiency of pmdarima’s auto_arima() function compared to implementing a traditional ARIMA model.
I think this would be worth playing with as I believe it would be worth using to improve your results of time series analysis.
A brief introduction to Python’s Peephole optimization technique.
I think this could save you time if you can learn how to use and understand it properly. Definitely worth reading.
This comprehensive guide covers the use of services from multiple cloud vendors, including the benefits businesses gain and the challenges IT teams face when using multi-cloud.
There are a lot of links in this article which acts as an index for cloud information.
A guide to the elegant and helpful uses of built-in Python sets.
A great tutorial on a very important but basic functionality in Python code.
Programming principles, rules, and some fun facts.
I could guess at some of these but didn't know them for sure.
You might be an expert in TensorFlow or PyTorch, but you must take advantage of these open-source Python libraries to succeed.
A very useful list of some of the most relevant libraries to use if you want to do a Deep Learning project. This could save you the time you would have spent searching for the right libraries.
Explaining the basics of Python objects and classes using examples.
Great explanations and code segments and is really helpful in understanding them.
And why you should take it.
A great way to really understand Julia.
Automate the Web with Python.
I like this as it sounds great and it looks goof form my initial playing around with it. I think we can do a lot of interesting things with Selenium.
