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Smart speaker showdown: Google Home vs. Amazon Echo

Mashable

Smart speakers sit in our homes, quietly listening to everything we say and feeding what they learn back to the corporations that spawned them, like sinister Elves on Shelves. Still, some of us can't resist the shiny allure of being able to yell random questions into the void like a medieval despot and get an answer back. This raises the difficult question of exactly which servant/spy to employ in your smart home. The Google Home and the Amazon Echo are two of the most prolific smart speakers on the market, but at first glance there's little separating them. Both allow you to control your smart home, play music, and set timers by speaking.


13 Top Python Libraries You Should Know in 2020

#artificialintelligence

Python provides a lot of libraries to help developers with their work. Which of them will be the most popular in 2020? And which are worth your time? Here are our picks for the 13 top Python libraries. Python is one of the most popular programming languages.


Fine-Tuning ML Hyperparameters

#artificialintelligence

"Just as electricity transformed almost every industry 100 years ago, today I actually have hard time thinking of an industry that I don't think AI (Artificial Intelligence) will transform in the next several years" -- Andrew NG I have long been fascinated with these algorithms, capable of something that we can as humans barely begin to comprehend. However, even with all these resources one of the biggest setbacks any ML practitioner has ever faced would be tuning the model's hyperparameters. A hyperparameter is a parameter whose value is used to control the learning process. By contrast, the values of other parameters (typically node weights) are learned. The same kind of machine learning model can be trained on different constraints, learning rates or kernels and other such parameters to generalize to different datasets, and hence these instructions have to be tuned so that the model can optimally solve the machine learning problem.


13 Python Data Science and Machine Learning Libraries You Need to Know

#artificialintelligence

It contains lots of pre-trained machine learning models that data scientists use rather than creating their own models. Obviously, it depends on what ML model you need to use. If you are looking for something very specific for your intent, maybe it's better to create your own model. Theano uses NumPy's syntax to optimize and evaluate mathematical expressions. It uses the GPU to speed up its processes.


Udemy Coupon Applied Deep Learning with TensorFlow

#artificialintelligence

"Artificial Intelligence, deep learning, machine learning -- whatever you're doing if you don't understand it -- learn it. Because otherwise, you're going to be a dinosaur within 3 years." How will You benefit from this Free Course? This course has one goal:Teaching you how Artificial Neural Networks work at a low level and how to implement them from scratch using TensorFlow. How are We going to do that?


Automated Machine Learning: The Free eBook - KDnuggets

#artificialintelligence

It's a new week, and what better time to get your hands on another free eBook? We have been highlighting a new such installment weekly for the better part of the past few months, doing our best to single out and share top learning materials for those stuck at home right now, or really for anyone interested in learning a new concept or brushing up on what they already know. This week we turn our attention to the topic of automated machine learning (AutoML), a personal favorite of mine. What is automated machine learning? It is a wide (and widening) concept, but I've previously tried to capture its essence as such: If, as Sebastian Raschka has described it, computer programming is about automation, and machine learning is "all about automating automation," then automated machine learning is "the automation of automating automation."


An Introduction to Machine Learning Libraries for C

#artificialintelligence

I love working with C, even after I discovered the Python programming language for machine learning. C was the first programming language I ever learned and I'm delighted to use that in the machine learning space! I wrote about building machine learning models in my previous article and the community loved the idea. I received an overwhelming response and one query stood out for me (from multiple folks) – are there any C libraries for machine learning? Languages like Python and R have a plethora of packages and libraries that cater to different machine learning tasks.


Udemy Machine Learning: Decent course, excellent community

#artificialintelligence

This post is part of "AI education", a series of posts that review and explore educational content on data science and machine learning. When it comes to software development education, I'm a classical type: I prefer books over video tutorials, and I like to manually write every single line of code instead of copy-pasting from sample files and Stack Exchange. My early experience with online artificial intelligence and machine learning courses had mostly left me disappointed. So, when Udemy gave me access to their online course "Machine Learning A-Z: Hands-On Python & R In Data Science," I was a bit skeptical. But after going through the course, I must say that the instructors, Kirill Eremenko and Hadelin de Ponteves, have done a great job to make machine learning, a fairly complicated topic, accessible to a wide audience.


The Librarians of the Future Will Be AI Archivists

#artificialintelligence

In July 1848, L'illustration, a French weekly, printed the first photo to appear alongside a story. It depicted Parisian barricades set up during the city's June Days uprising. Nearly two centuries later, photojournalism has bestowed libraries with legions of archival pictures that tell stories of our past. But without a methodical approach to curate them, these historical images could get lost in endless mounds of data. That's why the Library of Congress in Washington, D.C. is undergoing an experiment.


Steam can now use machine-learning to tell you what game you should play next – IAM Network

#artificialintelligence

Valve is introducing an exciting new Steam Labs experiment to Steam. Experiment 008, officially titled "Play Next," is a simple feature that uses machine learning to tell you what game you should play next."Using " Users who have unplayed (or very low playtime) games in their library, will now have a Play Next shelf available in the library view." Credit: ValveMoreThough Valve has not told us anything in-depth about the feature, it seems to take information from your recently played games and gives you options based on the similarities in genre and modes. If you usually play FPS games, Steam might recommend different kinds of shooters like battle royales or survival games that are found in your library.To check out the Play Next feature, make sure you have the latest Steam update installed and the new feature should appear right under your recent games in the Library tab.READ MORE: This is what Tokido thinks about when he plays Street Fighter