Enhance your data science experience on M1 Apple Silicon In this brief tutorial, I’ll show you how to set up your Macbook Air / Pro / Mini with Apple Silicon M1 architecture for Tensorflow and Datascience. As you might know, many python libraries haven’t been optimized yet for the…

A quick guide to visualizing the structure of a neural network in TensorFlow — Why read this article? This article will show you how to use Google Colab to implement a Convolutional Neural Network model with the eager execution of Tensorflow and the Keras Functional API. …

Creation of unique images using machine learning algorithms — 3 Minutes Machine Learning 3 minutes machine learning is a series of tutorials and informative videos related to the world of machine learning. You can find the complete videos on YouTube. The repository contains all the files shown in the videos. This series is in an experimental phase and is developed totally free…

Create and put into production, quickly and efficiently — This article presents two main topics: the desire to experiment with a tool for deploying python projects as web applications, sharing one’s work with friends, clients and colleagues quickly, with a responsive and pleasant interface implement a customised system for converting CAD geometries (step and iges) to STL format…

An overview of the Spotify library — As reported by the developers in the official documentation, Pythonflow is a Python library of dataflow programming, with a syntax very similar to that of Tensorflow, in its graph type. This library, simple but extremely powerful in my opinion, is designed for pre-processing flow (pipelines) for artificial intelligence models. …

Artificial intelligence is becoming more and more important in our lives. AI can change the way we live and work for the better. For industrial engineers, AI can play a huge role in empowering their workflow. In this blog post, we will discuss the importance of AI for industrial engineers…

Make your training loop smart and accessible —

Practical and customised operations in Tensorflow Neural Networks — When developing artificial intelligence algorithms of varying complexity, it is sometimes necessary to perform specific operations on the available data. In this context, we will work with Tensorflow 2. X and in particular with the Keras API. This working environment provides a wide range of mathematical operations, data processing…

Customise your algorithm by creating the function to be optimised — In our journey into the world of machine learning and deep learning, it will soon become necessary to approach the customisation of models, optimisers, loss functions, layers and other fundamental components of the algorithm as a whole. Tensorflow and Keras have a large number of pre-implemented and optimised loss…