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Machne Learning vs. Deep Learning
Actualités, Développement IoT, InnovationObjective
In this article, we will study a comparison between Deep Learning and Machine Learning. We will also learn about them individually. We will also cover their differences on various points. Along with a Deep Learning and Machine Learning comparison, we will also study their future trends.
Introduction to Deep Learning vs. Machine Learning
a. What is Machine Learning?
Generally, to implement Artificial Intelligence, we use Machine Learning. We have several algorithms that are used for Machine Learning. For example:
Source de l’article sur DZONE (AI)
Understanding the Confusion Matrix
Actualités, Développement IoT, InnovationThe confusion matrix is one of the most popular and widely used performance measurement techniques for classification models. While it is super easy to understand, its terminology can be a bit confusing.
Therefore, keeping the above premise under consideration, this article aims to clear the "fog" around this amazing model evaluation system.
Source de l’article sur DZONE (AI)
Testing Features of ML Models
Actualités, Développement IoT, InnovationIn this post, you will learn about different types of test cases that you could come up for testing features of the Data Science/Machine Learning models. Testing features are one of the key sets of which needs to be performed for ensuring the high performance of Machine Learning models in a consistent and sustained manner.
Features make the most important part of a Machine Learning model. Features are nothing but the predictor variable, which is used to predict the outcome or response variable. Simply speaking, the following function represents y as the outcome variable and x1, x2, and x1x2 as predictor variables.
Source de l’article sur DZONE (AI)
19 AI Technologies That Are Currently Dominating
Actualités, Développement IoT, Innovation1. Natural Language Generation
Natural Language Generation is an AI sub-discipline that converts data into text, enabling computers to communicate ideas with perfect accuracy.
It is used in customer service to generate reports and market summaries and is offered by companies like Attivio, Automated Insights, Cambridge Semantics, Digital Reasoning, Lucidworks, Narrative Science, SAS, and Yseop.
Source de l’article sur DZONE (AI)
When Machine Learning Meets the Business World
Actualités, Business, Développement IoT, InnovationBusinesses have always been at the forefront as early adopters of new technologies. Advancements in computing like Machine Learning have already made a notable impact on the business world. With business operations and processes spread across varying levels, the inclusion of a Machine Learning framework can prove worthwhile in increasing efficiency, productivity, and speed.
Machine Learning has found widespread acceptance among enterprises. MIT Technology Review and Google Cloud recently published a report based on their studies in Machine Learning and its adoption. The reports state that about 60 percent of the respondents have already implemented Machine Learning into their business.
Source de l’article sur DZONE (AI)
Image Processing With Deep Learning
Actualités, Développement IoT, InnovationNeural Networks were first used in 2009 for speech recognition and were only implemented by Google in 2012. Deep Learning, also called Neural Networks, is a subset of Machine Learning that uses a model of computing that’s very much inspired by the structure of the brain.
Source de l’article sur DZONE (AI)
How to Create a Great User Experience: Fresh App Personalization Ideas
Développement applicatif, Méthodes et organisation des process ITHave you ever made a purchase online following someone’s advice? What about the recommendations that were prompted to you by the website itself? Being quite a common element of an eCommerce app, tailored product recommendations have proven to be an extremely effective tool for revenue growth.
Namely, Amazon’s recommendation engine is said to generate 35% of the platform’s total revenue. Taking into account its actual sales volume ($178 billion in 2017), this makes an additional $62 billion per year. Quite impressive, right?
Source de l’article sur DZone (Agile)
Machine Learning in the World of Nuclear Physics [Video]
Actualités, Développement IoT, InnovationHow does TensorFlow apply to nuclear physics? In this video, I chat with Ian Langmore to learn about power generated from nuclear fusion, new plasma generator machines, and how TensorFlow is helping with plasma measurement.
To learn more about what we talked about:
Source de l’article sur DZONE (AI)
My Favorite Command-Line Shortcuts
Actualités, Méthodes et organisation des process ITI use a shell every day. Almost always, I want to repeat a previous command, or repeat it after a slight modification. A very convenient way is to use arrow-up to get the most recent command back. Another common trick is to type ctrl-R and incrementally search for a previously used command. However, there are two other tricks for repeating previous commands that I use all the time, which are not as well known.
Escape-Dot (or
!$)Often, you want to repeat only the last argument of the previous command. For example, suppose you want to run git diff path/to/tests, and then git add path/to/tests. For the second command, you can type git add escape-dot (escape followed by a period), and it gets expanded to path/to/tests (the last argument of the previous command).
Source de l’article sur DZONE
Accelerate: Building and Scaling High-Performing Technology Organizations [Review]
Développement applicatif, Méthodes et organisation des process ITNicole Forsgren’s, Jez Humble’s, and Gene Kim’s latest book, Accelerate: Building and Scaling High Performing Technology Organizations, describes the factors that drive high-performing tech organizations, derived from the data that has been aggregated with the State of DevOps Report since 2014.
Accelerateis a must-read book for anyone involved in building Agile organizations and teams. It lays out a path to success based on a statistical analysis of data. It also puts an end to the popular narrative that "becoming Agile" is somehow a fuzzy process. The data shows that there are patterns at all levels that successful Agile organizations share.
Source de l’article sur DZone (Agile)