a blog about what's cool to me
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 »