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Google’s Nest announces new smart thermostat with simpler design, lower price



Google’s Nest smart home division has a new smart thermostat available to order starting today. The new Nest Thermostat is a simpler model than the Nest Learning Thermostat or Nest Thermostat E and comes with a lower price, just $129.99. That’s $40 less than the Nest E and $120 less than the top-of-the-line third-generation Nest Learning Thermostat. It is available to pre-order starting today, and Google says it will be shipping in a few weeks.

Simpler is the theme with the new Nest Thermostat, and that starts with its design. Gone is the traditional rotating dial that’s been on every Nest thermostat for the past nine years. In its place is a touch sensitive strip on the right side that is used to navigate the interface and make adjustments. Instead of turning a dial to adjust the temperature, you swipe up and down and tap on this touch strip. This design eliminates all of the moving parts and allowed Google to bring the price down.

The front of the thermostat is a completely mirrored finish with a display that shines through the mirror when the thermostat is being used. Google is using the same Soli technology that was in the Pixel 4 smartphone underneath the mirrored finish to automatically detect when you are standing in front of the thermostat and wake it up. The company says that the Soli tech allows the mirrored finish to be uninterrupted, without an obvious window or cutout for a traditional motion sensor, as used on the other models. But that is the extent of the Soli use in the Nest Thermostat — there are no gesture-based controls outside of the touch strip.

The new Nest Thermostat has a mirrored front and comes in four different colors
Image: Google

Similar to the recently released Nest Audio smart speaker, the Nest Thermostat comes in a variety of colors: white, dark grey, light pink, and light green. A color-matched trim kit is available to cover up the holes left from your old thermostat for $14.99.

Nest has also simplified the software experience. The new Nest Thermostat does not feature Nest’s signature learning function, which attempts to automatically learn your living circumstances and adjust the thermostat for the best balance of comfort and efficiency. Instead, it runs off of a traditional schedule system, where you tell it when you’re home, when you’re away, and what temperature it should maintain for each scenario. In this way, it’s similar to how a traditional programmable thermostat works and should be very familiar to most people upgrading to a smart thermostat for the first time.

Instead of the automatic learning system of other Nest models, the new Nest Thermostat relies on a more traditional programmed schedule.
Photo: Google

The Nest Thermostat does have some smarts built in. It can be controlled via the Google Home smartphone app just like any other Nest thermostat, and it supports voice control via the Google Assistant or Amazon’s Alexa. It can also prompt you to adjust temperature levels for more efficiency and can alert you to potential issues with your HVAC system or when it’s time to change your air filter. It will use the Soli motion sensor plus geolocation on your phone to automatically enable Eco mode to save energy when you’re not home. Google says that these features can save owners an average of 10 to 12 percent on heating and 15 percent on cooling bills each year. The Nest Thermostat is also Energy Star certified, just like the other Nest thermostat models.

The Nest Thermostat lacks some capabilities compared to the more expensive versions. It doesn’t support Nest’s remote sensors for balancing the system off of a specific room, for example. Instead of a built-in rechargeable lithium battery, the Nest Thermostat runs off of two standard AAA batteries. (Google says it should last for “multiple years” on a set of AAAs.) The Farsight feature that lets you see the current temp from the across the room with other Nest models is not here, either. Google says that the install process is similar to the other models and the Nest Thermostat is compatible with nearly as many HVAC systems in use. It can also support multiple zone systems by linking multiple Nest Thermostats together and running them together.

A light pink Nest Thermostat mounted on the wall with a matching color Nest Audio speaker on a shelf next to it.
Color matching your smart thermostat with your smart speaker is now a thing you can do
Photo: Google

Google representatives say the Nest Thermostat does not replace its other models — the third-generation Nest Learning Thermostat remains available, though the Nest E will be only available to professional installers going forward. Instead, the company hopes that the lower cost and easier-to-understand system will be enticing to those that have not yet upgraded to a smart thermostat and are still using a traditional, programmable one.


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Sony will optimize PS5 fan performance with software updates using individual game data



Sony’s next-gen PlayStation 5 console has a massive internal cooling system composed of, among other things, a gigantic fan. But the device, a 120mm-wide and 45mm-thick double-sided intake fan, also has some smart software to power it, and that software will improve the fan’s performance over time based on data gathered from individual games, according to a new interview with Yasuhiro Ootori, Sony’s mechanical design chief in charge of the PS5, with Japanese language website 4Gamer.net.

Ootori was responsible for the refreshing teardown video Sony published earlier this month, showing off the entire console inside and out in a seven-minute video that revealed some telling new details about the mechanical design of the device. In the new interview, as translated by ResetEra user orzkare, Ootori points out that the fan will be controlled by the PS5’s Accelerated Processing Unit (APU), the custom AMD combined CPU / GPU chip that powers the console.

Sony’s double-sided intake fan on the PS5, which measures 120mm in diameter and 45mm thick.
Screenshot by Nick Statt / The Verge

“Various games will be released in the future, and data on the APU’s behavior in each game will be collected,” Ootori says. “We have a plan to optimize the fan control based on this data.” Ootori explains that multiple temperature sensors are placed on the console’s main board to collect data while the APU runs any given game, and that data is what will allow Sony to optimize the fan going forward on a per-game basis.

Ootori also reveals a pretty neat detail about the testing of the PS5’s cooling system, which involved making a transparent chassis and pumping the smoke from dry ice through to observe the effects on internal temperature.


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Support Indie Bookstores With Your Holiday Shopping



Illustration for article titled Support Indie Bookstores With Your Holiday Shopping

Photo: Logan Bush (Shutterstock)

The pandemic hasn’t been kind to independent bookstores. While indies have handled competition from Amazon and big-box sellers relatively well in recent years, having to shut their doors due to COVID-19 and respond to customers’ desire for online ordering has not been easy—and despite the American Association of Publishing noting steady book sales amid the pandemic, bookstore sales dropped 30.7 percent in August, according to industry newsletter Shelf Awareness. If you want to keep indie bookstores alive, consider directing some of your holiday gift-buying budget toward your local independent bookseller. We’ve rounded up a number of ways you can support indies this season.


Heads up, though: if you are going to gift books and book-related items, start shopping and ordering now. Indie booksellers can always use your support—and ordering earlier can help them manage their inventory and meet demand ahead of the holidays.

Order directly from your local indie

Some booksellers responded to the pandemic by setting up shop online and offering pickup, local delivery, or both. Search for indie bookshops in your neighborhood or city to find out what they’re doing. IndieCommerce has a directory of independent sellers that handle e-commerce.


Shop on IndieBound or Bookshop

If you can’t easily get a book from a store up the street, try IndieBound or Bookshop instead of defaulting to Amazon. IndieBound has a zip code search that directs you to local booksellers that have your desired title(s) in stock, while Bookshop allows you to either select a local seller to receive 100% of the profit from your order or to put your money into a pool that’s distributed among member shops.

Note: IndieBound will also connect you to Bookshop if it can’t find titles in stock in your area. Twitter users have suggested that Bookshop’s shipping times are actually comparable to Amazon’s and that the customer service is (unsurprisingly) far better.


Shop for non-book gifts

Many, if not most, indie bookstores sell more than just books. As writer Celeste Ng points out, you can order jigsaw puzzles, apparel, tote bags, and other book-adjacent gifts (coffee mugs with a literary quote, anyone?). Try to find all of your stocking stuffers–bookmarks, pens, magnets, socks—there too, along with your holiday cards.


Gift a subscription to Libro.fm

If audiobooks are more your friends’ and family’s thing, gift them a membership to Libro.fm. The platform allows you to choose a local, independent bookstore to receive a cut of the profit from each subscription. You can also gift individual audiobooks.


Gift an indie seller gift card

If you’re not sure about a gift recipient’s taste in books, send them a gift card to their local indie or your local indie.


Put together DIY book packages

Get creative with your gift-giving with themed book and book-adjacent packages. This Twitter thread from author Rebecca Makkai has a long list of ideas, from a city-specific gift box to a food-and-book (wine-and-book?) pairing, to a book and film package from the recipient’s birth year. A few more we like:

  • Long-distance book club: Send the same book to a group of friends along with an invite to a Zoom gathering and a cocktail recipe card.
  • Cookbook and spice package: Gift your food-loving friends a cookbook plus any hard-to-find ingredients.
  • Free Little Library: Gift a Free Little Library box filled with wrapped books to a friend who has a yard.


Many indies will wrap orders, too, so you can have items sent directly to friends and family.

Stop by for a cup of coffee

If your indie bookseller is open and serving coffee and food, stop in when you can (and follow all safety rules when you do).



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Learn Python machine learning with these essential books and online courses



Teaching yourself Python machine learning can be a daunting task if you don’t know where to start. Fortunately, there are plenty of good introductory books and online courses that teach you the basics.

It is the advanced books, however, that teach you the skills you need to decide which algorithm better solves a problem and which direction to take when tuning hyperparameters.

A while ago, I was introduced to Machine Learning Algorithms, Second Edition by Giuseppe Bonaccorso, a book that almost falls into the latter category.

While the title sounds like another introductory book on machine learning algorithms, the content is anything but. Machine Learning Algorithms goes to places that beginner guides don’t take you, and if you have the math and programming skills, it can be a great guide to deepen your knowledge of machine learning with Python.

Oiling your machine learning engine

Machine Learning Algorithm kicks off with a quick tour of the fundamentals. I really liked the accessible definitions Bonaccorso uses to explain key concepts such as supervised, unsupervised, and semi-supervised learning and reinforcement learning.

Bonaccorso also draws great analogies between machine learning and descriptive, predictive, and prescriptive analytics. The machine learning overview also contains some hidden gems, including an introduction to computational neuroscience and some very good precautions on the pitfalls of big data and machine learning.

That said, the machine learning overview does not go into too much details and would be hard to understand for novices. Given the audience of the book, it serves to refresh and solidify your understanding of machine learning, not to teach you the basics.

Next, Machine Learning Algorithms builds up on that brief overview and goes into more advanced concepts, such as loss functions, data generation processes, independent and identically distributed variables, underfitting and overfitting, different classification strategies (one-vs-one and one-vs-all), and elements of information theory. Again, the definitions are smooth and very accessible for someone who has already had hands-on experience with machine learning algorithms and linear algebra.

Before going into the exploration of different algorithms, the book covers some more key concepts such as feature engineering and data preparation. Here, you’ll get to revisit some of the key classes and functions of scikit-learn, the main Python machine learning library. If you already have a solid knowledge of Python and numpy, you’ll find this part a pleasant review of one-hot encoding, train-test splitting, imputing, normalization, and more. There is some very great stuff in the third chapter, including one of the best and most accessible definitions of principle component analysis (PCA) and feature dependence in machine learning algorithms. You’ll also get to see some of the more advanced techniques not covered in introductory books, such as non-negative matrix factorization (NNMF) and SparsePCA. Of course, without the background in Python machine learning, these additions will be of little use to you.

The real meat ofthe book starts in the fourth chapter, where you get to the machine learning algorithms. Here, I had mixed feelings.

A rich roster of machine learning algorithms


In general, Machine Learning Algorithms is nicely structured and stands up to the name. There are chapters on regression, classification, support vector machines (SVM), decision trees, and clustering. The book follows up with a few chapters on recommendation systems and natural language processing applications, and finishes off with a very brief overview of deep learning and artificial neural networks.

The main chapters offer in-depth coverage of principle machine learning algorithms in Python, including details not covered in introductory books. For instance, the regression chapter goes into an extensive coverage of outliers and methods to mitigate their effects. The classification chapter has a nice discussion on passive-aggressive classification and regression in online algorithms. The SVM chapter has a comprehensive (but complicated) discussion on semi-supervised vector machines. And the decision trees chapter provides a good coverage of the specific sensitivities of DTs such as class imbalance, and some practical tips on tweaking trees for maximum performance.

The clustering section really shines. It spans across three full chapters, starting with fundamentals (k-nearest neighbors and k-means) and goes through more advanced clustering (DBSCAN, BIRCH, and bi-clustering) and visualization techniques (dendrograms). You’ll also get a full account of measuring the effectiveness of the results and determining whether your algorithm has latched onto the right number and distribution of clusters.

Across the book, there are thorough discussions of the mathematical formulas behind each machine learning algorithm. You need to come strapped with solid linear algebra and differential and integral calculus fundamentals to fully understand this (if you need to hone your machine learning math skills, I’ve offered some guidance in a previous post).

The book also makes extensive use of functions numpy, scipy, and matplotlib libraries without explaining them, so you’ll need to know those too (you can find some good sources on those libraries here).

One of the most enjoyable things about Machine Learning Algorithms are the chapter summaries. After going through the nitty-gritty of the math and Python coding of each machine learning algorithm, Bonaccorso gives a brief review of where to apply each of the techniques presented in the book. There are also many references to relevant papers that provide more in-depth coverage of the topics discussed in the book. It’s refreshing to see some of the old but fundamental papers from early 2000s being mentioned in the book. Those things tend to get buried under the hype surrounding state-of-the-art research.

Machine Learning Algorithms finishes off with a good wrap-up of the machine learning pipeline and some key tips on choosing between the different Python tools introduced across the book.

Not enough real-world examples

deep neural network

The one thing, in my opinion, that should set a book on Python machine learning apart from research papers and theoretical textbooks are the examples. A good book should be rich in use-case oriented examples that take you through real-world applications and possibly build up through the book.

Unfortunately, in this respect, Machine Learning Algorithms leaves a bit to desire.

For one thing, the examples in the book are mostly generic, using data-generation functions in scikit-learn such as make_blobs, make_circles, and make_classification. Those are good functions to show certain aspects of Python machine learning, but not enough to give you an idea of how to use the techniques in real life, where you have to deal with noise, outliers, bad data, and features that need to be normalized and categorized.

The code is in plain Python scripts as opposed to the preferred Jupyter Notebook format (which is not much of a big deal, to be fair). Also, while the book omits much of the sample code and focuses on the important parts for the sake of brevity, it made it hard to navigate the sample files at times.

The book does cover some real-world examples, including one with airfoil data in the SVM chapter and another with the Reuters corpus in the NLP chapter. The recommendation systems chapter also includes a few decent use cases, but that’s about it. Without concrete examples, the book often reads like a disparate reference manual with code snippets, which makes it even more crucial to have solid experience with Python machine learning before picking this one up.

Another thing that didn’t really appeal to me were the two chapters on deep learning. Machine Learning Algorithms provides a good overview of deep learning and discusses convolutional neural networks, recurrent neural networks, and other key architectures. But the problem is that introductory books on Python machine learning already cover these concepts and much more. So most of the people who make it this far through the book without putting it down won’t find anything new here (aside from the mention of KerasClassifier maybe).

Midway through Python machine learning journey

So, where does this book stand in the roadmap to learning machine learning with Python? It’s neither beginner level, nor super-advanced. I would suggest picking up Machine Learning Algorithms after you read an introductory-to-intermediate book like Python Machine Learning or Hands-on Machine Learning, or an online course like Udemy’s “Machine Learning A-Z.” Otherwise, you won’t be able to make the best of the rich content it has to offer.

Once you finish this one, you might want to consider Bonaccorso’s Mastering Machine Learning Algorithms, Second Edition, which expands on many of the topics presented in this book and takes them into even greater depth.

This article was originally published by Ben Dickson on TechTalks, a publication that examines trends in technology, how they affect the way we live and do business, and the problems they solve. But we also discuss the evil side of technology, the darker implications of new tech and what we need to look out for. You can read the original article here.


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