Python is known as the king of data science. Its usage has exploded in the last decade and with ML libraries such as TensorFlow, PyTorch, Caffe and more, it has made code extraordinarily easy to write. The underlying C/C++ software provides the ultimate behind-the-scenes numerical computations, so that a data scientist can focus on the real work. Which is great, but doesn’t change the fact that python is slow.
I’m here to tell you why Go is the new preferred ML programming language and that my keras equivalent neural network architecture will lead the way.
I started my programming journey…
You may not recognize them, but graphs are an essential part of every day. Their applications make the dream work. Let’s work through them. If you feel you’ve mastered this part already, feel free to skip ahead :)
Here’s a proper introduction by example. Road network! Assuming you have a map of the UK in front of you, what you’d like is to represent the cities and the roads between. At this point, I can introduce two out of three key components of graphs: Edges and vertices(or nodes).
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Being a data scientist or an AI researcher, there’s a pretty high chance you’ll be needing lots of data. Listen, when I mean lots, I mean a whole LOT. (the number is usually up in GBs).
When your data can’t fit in memory, use generators. Trust me!
Generator functions allow you to declare a function that behaves like an iterator. They allow programmers to make an iterator in a fast, easy, and clean way.
Iterators don’t compute the value of each item when instantiated. They only compute it when you ask for it. This is known as lazy evaluation.
Containers are the centerpiece of running modern applications effectively with easy deployment and scalability. Hopefully, after reading the article, you'll understand why it's not an either-or-question and how to go about running and orchestrating your containers.
It is not a matter of question whether you should use either Docker or Kubernetes. They are not the same. Let me give you an example and you can find your answers amongst the highlighted memos.
Let-s assume you have a backend application running on React or Node.js Also, you require database access running dynamically. You built a Java script that enables exactly that…
The demand for computational power has never been greater than right now. Due to big data, companies and organizations are not only looking for the right people to handle the right part of the development process, they’re shifting their priorities towards bringing their products closer to their clients at a secure, manageable, but most importantly efficient, high throughput, low latency way.
Moving to the Cloud is happening right now!
Companies such as Netflix, Instagram, Pinterest, and Apple have already felt the pressure of scalability and have turned to the Cloud to handle their overwhelming customer activity.
(Not to mention Google…
Streamlit has proved out to be an outstanding opportunity for developers to share their machine learning instantly without having to worry too much about the underlying infrastructure.
In the following paragraphs, I’ll demonstrate the code written from the basis of a TensorFlow model, what changes you have to make and how I deployed my neural style transfer application in the most elegant manner with Streamlit Sharing.
And yes, this is Angelina Jolie as if painted by Gustav Klimt.
I know you’ve been dying to see the end result, and without further ado:
Go ahead and try it out for…
Binary Cross-Entropy loss or BCE loss is traditionally known as a metric for training GANs, but it’s far from being the best. Hopefully, after reading this article it’ll be quite clear why as well as what you can do about it.
If you’re trying to build a binary classifier, chances are you were using the binary cross-entropy loss. The BCE has in itself a rather simple idea.
For the sake of the argument, picture a scenario where you are trying predict two classes ( it could be either cats versus dogs, Tesla versus Ferrari etc. ). The BCE loss is…
Deep Convolutional Generative Adversarial Networks are the most exquisite type of neural network architecture in 2020. Generative models go way back to the 60s, largely created by Ian Goodfellow in 2014, and have unprecedented value regarding the future of deep learning.
For more on GANs or more specifically DCGANs, I encourage you to take a peek at the following articles:
KNN stands for k-nearest neighbor and is an algorithm based on a simple idea and can be used for both classification and regression. The boundary becomes smoother with increasing value of k. With k increasing to infinity, it finally becomes all blue or all red depending on the total majority.
KNN works by iterating through the data and calculating the Euclidean distance and for the k nearest neighbor of the given point, the point falls into one of several categories.
Okay, now that you’re familiar with how the KNN algorithm works in theory, let’s dig into the code.