In this post/Jupyter Notebook we'll forecast Cryptocurrency prices using Deep Learning (PyTorch, TF/Keras & darts) and we'll use both both simpler networks and more complex ones like NBEATs.
In this post/Jupyter Notebook we'll look at stocks, forecasting and predictions using classical machine learning (sklearn) approaches.
In this post/Jupyter Notebook we'll look at Time Series and theory surrounding them.
Just a week ago I did a presentation on Knowledge Distillation and how it can help improve efficiency of models.
Not interested in reading the whole article and just wanna play around with the model(s)? Head over to londogard.com/ner.
P.S. The Flair model is available for simple installation through huggingface.co's model hub
In this post I walk through Self-Attention Transformers from scratch with demos at the end for Text Classification & Generation, where the PyTorch-code is wrapped by fast.ai to simplify end-2-end.
I did a presentation/workshop on Object Detection using Transformers. It's a little bit confusing as the experience of the people that joined was very different, some even not knowing Python.
This is a presentation I did on Transfer Learning.
In this post I improve the previous FAQ search engine by some low hanging fruits. The requirements stay the same thus SotA is not achieved but rather it's simply generic & easy on hardware (Raspberry Pi capable).
I decided to scratch a small itch I've had for a while now - creating a search engine using an unsupervised approach. The final product, or the first iteration rather, ended up pretty good and I wanted to share what I've done so far.
I've set a goal to create one blog post per Competence Meeting I've held at AFRY to spread the knowledge further. This goal will also grab all the older meetings, my hope is that I'll be finished before summer 2020, but we'll see.