Thalles Silva

Image by Ahmed Gad from Pixabay In the last two decades, the field of Artificial Intelligence (AI) has seen substantial development in research and industrial applications. Machine learning algorithms such as Logistic Regression and Naive Bayes can recognize patterns from a set of features and solve problems that seemed otherwise impossible to be solved by hard-coding knowledge into expert systems.

Notes on the current state of deep learning and how self-supervision may be the answer to more robust models Image by free stock photos from www.picjumbo.com from Pixabay There is one single thing that every Deep Learning practitioner agrees. Deep learning models are data inefficient. Let’s start by considering the popular task of classification in Computer Vision. Take the ImageNet database as an example. It contains 1.3 million images from 1000 different classes.

Introduction Putting Machine Learning (ML) models to production has become a popular, recurrent topic. Many companies and frameworks offer different solutions that aim to tackle this issue. To address this concern, Google released TensorFlow (TF) Serving in the hope of solving the problem of deploying ML models to production. This piece offers a hands-on tutorial on serving a pre-trained Convolutional Semantic Segmentation Network.