Datanami Essential Cheat Sheets for Machine Learning and Deep Learning Engineers | by Kailash Ahirwar | Startups & Venture Capital 12. Dask I am a Co-Founder of MateLabs, where we have built Mateverse, an ML Platform which enables everyone to easily build and train Machine Learning Models, without writing a single line of code. Note: Recently, I published a book on GANs titled “Generative Adversarial Networks Projects”, in which I covered most of the widely popular GAN architectures and their implementations. MIT Deep Learning and Artificial Intelligence Lectures | Lex Fridman
How to install NGINX + PHP7.1 + PHP-FPM in Amazon AMI EC2 for LARAVEL 5.x No blah blah blah, let’s go to the point… 1. Install and Update 1.1. $ sudo yum update 1.2. $ sudo yum install nginx -y 1.3. $ sudo yum install php71 php71-fpm php71-mcrypt php71-xml php71-mcrypt php71-zip php71-xmlrpc php71-gd php71-curl php71-pdo php71-mysqlnd php71-mbstring php71-gmp 1.4. $ sudo chkconfig nginx on$ sudo chkconfig php-fpm on 1.5. $ sudo service nginx start$ sudo service php-fpm start 2. 2.1. $ sudo vi /etc/php-fpm.d/www.conf (Add or uncomment by removing ; in the start) [global]emergency_restart_threshold = 10emergency_restart_interval = 1mprocess_control_timeout = 10s [www]listen = /var/run/php-fpm/www.socklisten.owner = nginxlisten.group = nginxlisten.mode = 0664user = nginxgroup = nginx pm.max_children = 20pm.start_servers = 5pm.min_spare_servers = 5pm.max_spare_servers = 20pm.max_requests = 200 php_admin_value[memory_limit] = 128M Restart service $ sudo service php-fpm restart 2.2. virtual.conf $ sudo vi /etc/nginx/conf.d/virtual.conf Add config in file: server { listen 80; 3. 3.1.
Wonder How To Machine learning in Python with scikit-learn Ce que vous allez apprendre À la fin de ce cours, vous serez capable de : Grasp the fundamental concepts of machine learningBuild a predictive modeling pipeline with scikit-learnDevelop intuitions behind machine learning models from linear models to gradient-boosted decision treesEvaluate the statistical performance of your models Please note that this course is available in english only. Description Predictive modeling brings value to a vast variety of data, in business intelligence, health, industrial processes… It is a pillar of modern data science. This course is an in-depth introduction to predictive modeling with scikit-learn. The course covers the software tools to build and evaluate predictive pipelines, as well as the related concepts and statistical intuitions. The training will be essentially practical, focusing on examples of applications with code executed by the participants. Prérequis The course aims to be accessible without a strong technical background.
Deep Learning ou Apprentissage Profond : qu'est-ce que c'est ? Le Machine Learning est un ensemble de techniques donnant la capacité aux machines d’apprendre, contrairement à la programmation qui consiste en l’exécution de règles prédéterminées. Il existe deux principaux types d’apprentissages en Machine Learning. L’apprentissage supervisé et non supervisé. En apprentissage supervisé, l’algorithme est guidé avec des connaissances préalables de ce que devraient être les valeurs de sortie du modèle. Facebook’s A.I. Whiz Now Faces the Task of Cleaning It Up. Sometimes That Brings Him to Tears. “We can now catch this sort of thing — proactively,” Mr. Schroepfer said. The problem was that the marijuana-versus-broccoli exercise was not just a sign of progress, but also of the limits that Facebook was hitting. Identifying rogue images is also one of the easier tasks for A.I. Delip Rao, head of research at A.I. “Sometimes you are ahead of the people causing harm,” Mr. On that afternoon, Mr. Mr. In designing systems that identify graphic violence, Facebook typically works backward from existing images — images of people kicking cats, dogs attacking people, cars hitting pedestrians, one person swinging a baseball bat at another.
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Scikit-Learn Cheat Sheet (2021), Python for Data Science | by Christopher Zita Scikit-learn is a free software machine learning library for the Python programming language. It features various classification, regression, clustering algorithms, and efficient tools for data mining and data analysis. It’s built on NumPy, SciPy, and Matplotlib. Sections:1. The code below demonstrates the basic steps of using scikit-learn to create and run a model on a set of data. The steps in the code include: loading the data, splitting into train and test sets, scaling the sets, creating the model, fitting the model on the data, using the trained model to make predictions on the test set, and finally evaluating the performance of the model. Maximizing the Impact of Data Augmentation: Effective Techniques and Best Practices | by Youssef Hosni | Mar, 2023 | Towards AI Data augmentation is a popular technique in machine learning that involves creating new data from existing data by making various modifications to it. These modifications could include adding noise, flipping images horizontally, or changing colors, among others. Data augmentation is an effective way to increase the size of a dataset and improve the performance of machine learning models. However, data augmentation is not a one-size-fits-all solution. To maximize its impact, it is important to use effective techniques and best practices. Table of Contents: What is Data Augmentation? If you want to study Data Science and Machine Learning for free, check out these resources: Free interactive roadmaps to learn Data Science and Machine Learning by yourself. If you want to start a career in data science & AI and do not know how. Join the Medium membership program for only 5$ to continue learning without limits. We need data augmentation for several reasons: What are the parameters?