Note: these are affiliate links. If you buy a similar setup, it will generate a few bucks used to keep my website live!
My setup totalled to about €1100 or $1200, but it may depend on the vendors you pick. Nonetheless, the CPU and the GPU are definitely the most expensive (and important).
I did not buy any additional fans, as the Be Quiet base already had some pre-installed. However, I think it might be better to install extra’s.
Actually, it’s very easy to upgrade (or downgrade) your system. You can easily switch out modules to decrease or increase the performance (and cost). For instance, you can install another two memory cards on your motherboard, or simply spend more on a GPU.
After everything was delivered to my house, I thought the hard part started: building the desktop and putting everything together. But actually, this only took me about an hour or two, with the help of some great tutorials on Youtube:
I hope this convinces and helps you to build your own system at home!
Cohen’s d (wiki) is a statistic used to indicate the standardised difference between two means. Resarchers often use it to compare the averages between groups, for instance to determine that there are higher outcomes values in a experimental group than in a control group.
Researchers often use general guidelines to determine the size of an effect. Looking at Cohen’s d, psychologists often consider effects to be small when Cohen’s d is between 0.2 or 0.3, medium effects (whatever that may mean) are assumed for values around 0.5, and values of Cohen’s d larger than 0.8 would depict large effects (e.g., University of Bath).
In this great tutorial for PyCon 2020, Eric Ma proposes a very simple framework for machine learning, consisting of only three elements:
By adjusting the three elements in this simple framework, you can build any type of machine learning program.
In the tutorial, Eric shows you how to implement this same framework in Python (using jax) and implement linear regression, logistic regression, and artificial neural networks all in the same way (using gradient descent).
I can’t even begin to explain it as well as Eric does himself, so I highly recommend you watch and code along with the Youtube tutorial (~1 hour):
Have you ever wondered what goes on behind the scenes of a deep learning framework? Or what is going on behind that pre-trained model that you took from Kaggle? Then this tutorial is for you! In this tutorial, we will demystify the internals of deep learning frameworks – in the process equipping us with foundational knowledge that lets us understand what is going on when we train and fit a deep learning model. By learning the foundations without a deep learning framework as a pedagogical crutch, you will walk away with foundational knowledge that will give you the confidence to implement any model you want in any framework you choose.