The objective of AI is to predict a behavior based on past statistics. Algorithms are fundamentally influenced by the data they are trained with, thus the prediction is never perfect. We are studying, for each situation, the best way to secure our clients' business against the probabilism of algorithms. This threshold allows us to warn humans when the model is more likely to make a false prediction.
Accessing a large quantity of data can be difficult to achieve. New techniques are available to enable algorithms to perform well with little information. At the cutting-edge of technology, Sicara works on fundamental research linked to Few-Shot learning problems. Meta-learning, data-augmentation or transfer learning, our researchers work to connect the world of academics to the business problems we are facing. You can read our research on Meta-learning algorithms for Few-Shot Computer Vision [here](https://arxiv.org/pdf/1909.13579.pdf).
Our purpose it to help our clients in succeeding on their projects. Our researchers teach the outcomes of their work to our team of Data Scientists to help them deliver faster. Our R&D is the first brick of our expertise in Image Recognition. When we reach a certain level of maturity on a technology, we transform this knowledge into tools so that our clients can see the benefits on their projects.
Edge Detection in Opencv 4.0, A 15 Minutes Tutorial
This tutorial will teach you, with examples, two OpenCV techniques in python to deal with edge detection.
Keras Tutorial: Content Based Image Retrieval Using a Denoising Autoencoder
How to find similar images thanks to Convolutional Denoising Autoencoder.
Set up TensorFlow with Docker + GPU in Minutes
Why Docker is the best platform to use Tensorflow with a GPU.
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