This scientific article presents the implementation of an automated control system for detecting and classifying faults in tuna metal cans using artificial vision. The system utilizes a conveyor belt and a camera for visual recognition triggered by a photoelectric sensor. A robotic arm classifies the metal cans according to their condition. Industry 4.0 integration is achieved through an IoT system using Mosquitto, Node-RED, InfluxDB, and Grafana. The YOLOv5 model is employed to detect faults in the metal can lids and the positioning of the easy-open ring. Training with GPU on Google Colab enables OCR text detection on the labels. The results indicate efficient real-time problem identification, optimization of resources, and delivery of quality products. At the same time, the vision system contributes to autonomy in quality control tasks, freeing operators to perform other functions within the company.

We present an open-source Physics Informed Neural Network environment for simulations of transient phenomena on two-dimensional rectangular domains, with the following features: (1) it is compatible with Google Colab which allows automatic execution on cloud environment; (2) it supports two dimensional time-dependent PDEs; (3) it provides simple interface for definition of the residual loss, boundary condition and initial loss, together with their weights; (4) it support Neumann and Dirichlet boundary conditions; (5) it allows for customizing the number of layers and neurons per layer, as well as for arbitrary activation function; (6) the learning rate and number of epochs are available as parameters; (7) it automatically differentiates PINN with respect to spatial and temporal variables; (8) it provides routines for plotting the convergence (with running average), initial conditions learnt, 2D and 3D snapshots from the simulation and movies (9) it includes a library of problems: (a) non-stationary heat transfer; (b) wave equation modeling a tsunami; (c) atmospheric simulations including thermal inversion; (d) tumor growth simulations.

This is my first post and this post is for an absolute beginner. If you are stuck somewhere in this tutorial then don't worry about that. This post is just for you to make you familiar with the machine learning process, In the upcoming series of posts, we will discuss in-depth about the concepts. In this post, you will make your first machine learning project (step-by-step) in Python. This post is 1 day of the "10 days of machine learning project" post series.

There are a lot of platforms available for coding, but in studies regarding deep learning, we need to pay extra attention to the platform's capabilities of training the model, with that being said, coders need to obtain a full knowledge about monitoring the resources and devices. Follow ups I will go over ten reasons why you should use Google Colab for Deep Learning projects. Are you still struggling with finding your files on the local drive? If so, why don't you try Google Colab? With everythings being stored on the cloud, you can easily find your files by one click.

Colaboratory, sometimes called "Colab," is a Google Research product. It enables anyone to create and execute arbitrary Python code through the browser. Technically speaking, Colab is a hosted Jupyter notebook service that offers free access to computer resources, including GPUs, and requires no setup. As a better iteration of Jupyter Notebook, Google Colab can be characterized. Data analysis, teaching, and machine learning are three areas where Colab excels.

Wonder what your old black-and-white photos look like in real? It is based on GANs. Now take out your Grandparents' old photo album and let's get Started. We will be using google colab to run the Deoldify model. Google Colab is a product from Google Research.

Machine Learning and Deep learning plays a vital role in our day to day life. Today's machine can automatically detect or classify images. Here, I want to show the step by step process of image classification and improve the accuracy of the model and you can understand how to experiment with data with a huge ways. Here, you can see the image classification by Machine learning Algorithms like Logistic Regression, KNN, Random Forest Classifier, Adaboost, Neural Networks (Convolution Neural Networks-CNN), transfer learning algorithms( Resnet50, VGG-16, VGG-19 and Others). I will implement all the algorithms step by step so that you can realize and implement any classification problem.

Generative Adversarial Networks (GANs) are one of the hottest topics in computer science in recent times. They are a clever way of training a generative model (unsupervised learning) by framing the problem as a supervised learning problem. The main idea is that two different models are trained simultaneously by an adversarial process. Generative adversarial networks are based on a game theoretic scenario in which the generator network must compete against an adversary. The generator network directly produces samples.

Welcome to SuperGradients, a free, open-source training library for PyTorch-based deep learning models. SuperGradients allows you to train or fine-tune SOTA pre-trained models for all the most commonly applied computer vision tasks with just one training library. We currently support object detection, image classification and semantic segmentation for videos and images. Easily load and fine-tune production-ready, pre-trained SOTA models that incorporate best practices and validated hyper-parameters for achieving best-in-class accuracy. Why do all the grind work, if we already did it for you?

If you are a student or professional interested in the latest trends in the computing world, you would have heard of terms like artificial intelligence, data science, machine learning, deep learning, etc. The first article in this series on artificial intelligence explains these terms, and sets the platform for a simple tutorial that will help beginners get started with AI. Today it is absolutely necessary for any student or professional in the field of computer science to learn at least the basics of AI, data science, machine learning and deep learning. However, where does one begin to do so? To answer this question, I have gone through a number of textbooks and tutorials that teach AI. Some start at a theoretical level (a lot of maths), some teach you AI in a language-agnostic way (they don't care whether you know C, C, Java, Python, or some other programming language), and yet others assume you are an expert in linear algebra, probability, statistics, etc. In my opinion, all of them are useful to a great extent. But the important question remains -- where should an absolute beginner interested in AI begin his or her journey? Frankly, there are many fine ways to begin your AI journey.