This blog post was originally published at Digica’s website. It is reprinted here with the permission of Digica. Here goes a second edition of our special Christmas treat for you! (The first one was about Santa Classification using classical Computer Vision techniques. It not only was accurate – catching even the Grinch – but also […]
Radar Santa Classifier Read More +
This blog post was originally published at Digica’s website. It is reprinted here with the permission of Digica. Increasing yields has been a key goal for farmers since the dawn of agriculture. People have continually looked for ways to maximise food production from the land available to them. Until recently, land management techniques such as
Feeding the World with AI: A Study In the Use of Synthetic Data Read More +
This blog post was originally published at Digica’s website. It is reprinted here with the permission of Digica. When you want to take the best possible care of your brain, certain things are recommended, such as fatty fish, vegetables, doing some brain exercises, and learning new things. But what about artificial neural networks? Of course,
How to Find the Best “Food” for Your AI Model Read More +
This blog post was originally published at Digica’s website. It is reprinted here with the permission of Digica. A Generative Adversarial Network (GAN) is a concept that was developed in 2014 by a team of distinguished researchers led by Ian J. Goodfellow. In short, we train two deep neural networks in parallel so that the
A Handful of Thoughts On the Practical Use of Generative Adversarial Networks Read More +
This blog post was originally published at Digica’s website. It is reprinted here with the permission of Digica. As humans, we have a visual system that allows us to see (extract and understand) shapes, colours and contours. So why do we see every image as a different image? How do we know, for example, that
This blog post was originally published at Digica’s website. It is reprinted here with the permission of Digica. Autoencoders have existed in Data Science for a long time. This type of model has three sequential parts: the input layer (encoder); the hidden layer; and the output layer (decoder). It seems like a simple concept, but
What I Like About Autoencoders Read More +
This blog post was originally published at Digica’s website. It is reprinted here with the permission of Digica. Nowadays, no one needs to be convinced of the power and usefulness of deep neural networks. AI solutions based on neural networks have revolutionised almost every area of ​​technology, business, medicine, science and military applications. After the
How to Fool a Neural Network Read More +
This blog post was originally published at Digica’s website. It is reprinted here with the permission of Digica. In the previous article, I described attention mechanisms by using an example of natural language processing. This method was first used in language processing, but this is not its only usage. We can also use attention mechanisms
This blog post was originally published at Digica’s website. It is reprinted here with the permission of Digica. The attention mechanism made big changes in deep learning. Thanks to this, models can achieve better results. This mechanism was also the inspiration for perceivers and also transformer neural networks . And transformers led to the development
Deep Learning Models Which Pay Attention (Part I) Read More +
This blog post was originally published at Digica’s website. It is reprinted here with the permission of Digica. Why is explaining machine learning models important? The main focus in machine learning projects is to optimize metrics  like accuracy, precision, recall, etc. We put effort into hyper-parameter tuning or designing good data pre-processing. What if these
Why is Explaining Machine Learning Models Important? Read More +
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