In Part 1 on GANs, we started to build intuition regarding what GANs are, why we need them, and how the entire point behind training GANs is to create a generator model that knows how to convert a random noise vector into a (beautiful) almost real image. Since we have already discussed the pseudocode in great depth in Part 1, be sure to check that out as … [Read more...] about Step-By-Step Implementation of GANs on Custom Image Data in PyTorch: Part 2
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How I Would Explain GANs From Scratch to a 5-Year Old: Part 1
Note: Quite frankly, there are already a zillion articles out there explaining the intuition behind GANs. While I will briefly touch upon it, the rest of the article will be an absolute deep dive into the GAN architecture and mainly coding — but with a very very detailed explanation of the pseudocode (open-sourced as an example by PyTorch on Github). Why do I need … [Read more...] about How I Would Explain GANs From Scratch to a 5-Year Old: Part 1
Fine-tune Transformers in PyTorch Using Hugging Face Transformers
This notebook is designed to use a pretrained transformers model and fine-tune it on a classification task. The focus of this tutorial will be on the code itself and how to adjust it to your needs. This notebook is using the AutoClasses from transformer by Hugging Face functionality. This functionality can guess a model’s configuration, tokenizer and … [Read more...] about Fine-tune Transformers in PyTorch Using Hugging Face Transformers
Pretrain Transformers Models in PyTorch Using Hugging Face Transformers
This notebook is used to pretrain transformers models using Hugging Face on your own custom dataset. What do I mean by pretrain transformers? The definition of pretraining is to train in advance. That is exactly what I mean! Train a transformer model to use it as a pretrained transformers model which can be used to fine-tune it on a specific … [Read more...] about Pretrain Transformers Models in PyTorch Using Hugging Face Transformers
From Text to Knowledge: The Information Extraction Pipeline
I am thrilled to present my latest project I have been working on. In this blog post, I will present my implementation of an information extraction data pipeline, following my passion for combining natural language processing and knowledge graphs. Later on, I will also explain why I see the combination of NLP and graphs as one of the paths to explainable AI. If this in-depth … [Read more...] about From Text to Knowledge: The Information Extraction Pipeline
Why Is Object Detection So Messy?
Those working with Neural Networks know how complicated Object Detection techniques can be. It is no wonder there is no straight forward resource for training them. You are always required to convert your data to a COCO-like JSON or some other unwanted format. It is never a plug and play experience. Moreover, no diagram thoroughly explains Faster R-CNN or YOLO as there is for … [Read more...] about Why Is Object Detection So Messy?
Top Applications of Graph Neural Networks 2021
At the beginning of the year, I have a feeling that Graph Neural Nets (GNNs) became a buzzword. As a researcher in this field, I feel a little bit proud (at least not ashamed) to say that I work on this. It was not always the case: three years ago when I was talking to my peers, who got busy working on GANs and Transformers, the general impression that they got on me was that I … [Read more...] about Top Applications of Graph Neural Networks 2021
Time Series Forecasting with Deep Learning and Attention Mechanism
This is an overview of the architecture and the implementation details of the most important Deep Learning algorithms for Time Series Forecasting. This article was originally published on Towards Data Science and re-published to TOPBOTS with permission from the author. Motivation Time Series Forecasting has always been a very important area of research in many domains … [Read more...] about Time Series Forecasting with Deep Learning and Attention Mechanism
Time Series Classification with Deep Learning
This is an overview of the architecture and the implementation details of the most important Deep Learning algorithms for Time Series Classification. This article was originally published on Towards Data Science and re-published to TOPBOTS with permission from the author. Why Time Series Classification? First of all it’s important to underline why this … [Read more...] about Time Series Classification with Deep Learning
Transformers in Computer Vision
Transformer architecture has achieved state-of-the-art results in many NLP (Natural Language Processing) tasks. One of the main breakthroughs with the Transformer model could be the powerful GPT-3 released in the middle of the year, which has been awarded Best Paper at NeurIPS2020. In Computer Vision, CNNs have become the dominant models for vision tasks … [Read more...] about Transformers in Computer Vision