a blog about what's cool to me
It’s hard to decide on a baby name, even more so when multiple people are invested in the decision-making process. My friends recently had a baby and couldn’t decide between Alex and Niklas. I naturally took it upon myself to solve their problem by creating a Machine Learning algorithm that finds the names laying perfectly […]
Using Machine Learning to Compromise between names Read More »
We often use recurrent neural network architectures for use cases such as streaming, where we make a new prediction each time new information arrives. It’s common to give it a long context each time, which repeats work and is therefore computationally wasteful. In this blog post, I will illustrate multiple ways to perform inference step
Step by step recurrent neural network inference with Keras Read More »
In the past, I’ve covered how to implement k-means and how to cluster MNIST directly. Then I showed a bad way to reduce dimensionality for clustering. Finally, we used an autoencoder on MNIST. In this post, we’ll join everything together by performing clustering on the encoding and see how it performs. In the previous post,
Clustering on encoding Read More »
In my previous post, I followed up on my k-means tutorial by applying it to cluster MNIST. We’re used to reading digits, so the centroids made perfect sense to us since plotting them just looked like a smudged number. The dataset even clustered pretty well since all digits are centred and look similar enough that
How not to reduce dimensionality for clustering Read More »
This is the beginning of a series in which we’ll explore ways to perform clustering. In particular, we’ll perform clustering on the MNIST dataset, since it’s a dataset of high dimensionality which is easy to understand. In my previous post, ‘How to implement the K-means algorithm in Tensorflow‘, I explain the code of Sergey Kovalev,
Clustering MNIST – naïve Read More »
The K-means algorithm is a simple clustering algorithm. It is an unsupervised learning algorithm, therefore it automatically finds structure in unordered data. Since TensorFlow does not have a native implementation, it’s useful to know the tips and tricks to implement K-means. I’m hoping to use K-means in an upcoming post, so I want to explain
How to implement the K-means algorithm in Tensorflow Read More »
Sticking to the sequential model limits the freedom you have with regards to creating models in Tensorflow. In this post, I’ll start by showing a simple sequential model. I’ll then convert it into a functional model. Finally, I’ll use GradientTape to calculate all gradients and control the full train loop, all to improve the possibilities
Creating a custom training loop in tensorflow Read More »
Keras offers a large amount of flexibility to its dense layers. In this more advanced post, I’ll illustrate this and rationalise why it happens. If you are unfamiliar with shapes or the way Keras handles them, my posts Shapes – an introduction and A closer look at shapes might help. Let’s dive right in with
on the flexibility of dense layers in Keras Read More »
Understanding shapes is an integral part of demystifying how data gets passed around in your model. If you are unfamiliar with shapes, reading my previous post, Shapes – an introduction, might help. In this post, I’ll explore how Tensorflow expects to receive data, and hopefully provide a better intuition for how to conceptualise your data
A closer look at shapes Read More »
We’ve all had Tensorflow complain about some shapes at some point when starting out. In this first post, I’ll make shapes more intuitive by going into what shapes are, which data structures have a shape, and put everything into words. The shape of an n-dimensional array shows its dimensions. A scalar has shape () since
Shapes – an introduction Read More »