MIT Deep Learning and Artificial Intelligence Lectures | Lex Fridman Paper.li 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.
BigData Hebdo - BigData Hebdo 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.
towardsdatascience There are thousands of different tutorials out there that tell you how to explore your data. Most of them, however, focus on continuous data. Therefore, I won't waste any of your time (or mine) and I will stick to highlighting methods and tools that are specifically useful in survey data. Describe (Numpy version) There are a few inbuilt functions that can help you understand your a lot more, really fast. Describe is a really common tool that is used often by data scientists but this only accounts for the numeric and continuous variables. Some survey software's will output the questions already in one hot format. Groupby Crosstabs and Heatmaps Looking at subgroups of the data can be extremely important, especially in survey data. Groupby I won't dig too deep into how groupby works, but if you want to know more there’s a detailed explanation here. So with our data, we could produce something like this. Crosstabs We can do this with both raw counts or percentages as the code shows. Heat-maps
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.
twitter 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?
Which Data Science Skills are core and which are hot/emerging ones? - KDnuggets The latest KDnuggets Poll asked 1. Which skills / knowledge areas do you currently have (at the level you can use in work or research)? We selected a list of 30 skills based on a number of previous KDnuggets articles and polls - see useful links at the end of this post, as well as external sources. Altogether(*), this poll received over 1,500 votes - a large enough sample to make meaningful inferences. Fig. 1 below shows key findings, with X-axis showing % Have Skill - answers to the first poll question, and Y-axis showing % Want Skill - answers to the 2nd poll question. Fig. 1: Data Science-related Skills, Have skill vs Want to add or improve skill We note two main clusters in this chart. Cluster 1, in blue dashed rectangle on the right side of the chart, includes skills that over 40% of all voters have, and where the ratio of Want/Have is less than 1. Table 1: Core Data Science Skills, in decreasing order of %Have Table 3: Other Data Science Skills, in decreasing order of %Have Related:
Lex Fridman | MIT | Human-Centered AI & Autonomous Vehicles