Already 30 minutes on Stack Overflow, 1 hour on Quora and you still don’t understand WHY non-linearity is necessary in a Neural Network ? If you are also in this scenario, you’ve landed at the right spot !💡 In this…
Layers, the basic concept that structure Deep Learning. What are the main types and how to use them ? That what we’ll find out. In a Deep Learning algorithm the input data are called tensors, it is basically a matrix…
The mechanism of Attention is a fundamental process in Deep Learning to understand the most recent high-performance models. Today, most AI researchers integrate Attention in their neural networks. To apprehend Attention, you need to know the principle of Encoders-Decoders, a…
How does an Encoder-Decoder work and why use it in Deep Learning? The Encoder-Decoder is a neural network discovered in 2014 and it is still used today in many projects. It is a fundamental pillar of Deep Learning. It is…
Recall, Precision, F1 Score how to easily remember their usefulness and what these metrics imply ? To understand these metrics, you need to know the concepts of True Positive / False Negative (detailed in this article along with a method…
In this article we explain you THE method to never forget the difference between True Positives, False Negatives, True Negatives and False Positives. These terms define the type of result we can get after a prediction with two possibilities (in…