Pruning Machine Learning Models in TensorFlow

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In a previous article, we reviewed some of the pre-eminent literature on pruning neural networks. We learned that pruning is a model optimization technique that involves eliminating unnecessary values in the weight tensor. This results in smaller models with accuracy very close to the baseline model.

In this article, we’ll work through an example as we apply pruning and view the effect on the final model size and prediction errors.

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Microsoft Azure’s QnA Maker: Making FAQs a bit more chatty

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Chatbots are a thing. Yes, they may seem gimmicky on the surface. But a lot has changed since the days of SmarterChild. Dig a little deeper and you’ll find there’s more to them than meets the eye.

First, chatbots are, essentially, gateways to voice apps. Whether you use Alexa, Siri, Google Assistant, or Cortana, building apps with a conversational interface is a new norm. Second, chatbots can provide a first line of support that users can feel comfortable interacting with.

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Swifty ML: An Intro to Swift for TensorFlow

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When it comes to machine learning, Python has been dominant. However, we can already foresee how Python has a limit in terms of how far it can scale with modern ML demands.

Google seems to have considered this as it’s considered the future of ML (especially through the lens of TensorFlow). They realize that, in lieu of overhauling Python, a more modern and adaptable language could change the game.

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Simple Semantic Image Segmentation in an iOS Application — DeepLabV3 Implementation

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I was working on a personal project on tumor detection and recognition and decided to us some image segmentation tools and techniques to refine my recognition model.

As I was working on this project, I found myself wondering—is it possible to do this on mobile? The answer is YES, and so I decided to give it a shot.

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5 App Ideas to Unleash the Power of Mobile Machine Learning

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With over 2 billion active Android devices and over 1 billion active iOS users, the mobile market provides the most engaging and profitable market to build and sell new digital solutions. There are less than 4 million unique applications for each of these operating systems, with most of them performing the same or related functions.

However, the arrival of cloud-based and device-based artificial intelligence tools provides a unique opportunity to recreate the mobile experience for existing apps, as well as build entirely new mobile apps that can only be possible through the use of AI-powered tools.

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Newsletters for Data Science & Analytics

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In our last newsletter roundup, we looked digests for mobile development, and we covered it all: iOS, Android, React Native, and Cross-Platform. Again, my sincerest thanks to Heartbeat All-Star Zain Sajjad, whose help in putting these lists together has been invaluable.

For the last installation of our newsletter journey, let’s take a look at some of the premier newsletters for data science and analytics.

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Newsletters for Mobile Development

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In our last newsletter roundup, we looked at the expansive landscape of AI and machine learning newsletters. Luckily for me (and Zain Sajjad, who was a huge help in putting this list together) the demarcations with mobile development newsletters are much more straightforward.

That doesn’t mean there’s any less variety or quality in the content that’s being sent all around the world every week (or every other week). I had a lot of fun researching these excellent newsletters, and I know there’s something in here that will capture your interest. So let’s jump right in!

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Augmented Reality on iOS and Android

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Augmented Reality on iOS and Android

Augmented reality (AR) is one of the fastest growing “next wave” technologies in mobile development. To define AR, I’ll turn to Jameson Toole and his post (linked below) that explores combining AR and AI in mobile development:

To help you get started with developing amazing mobile experiences with AR, we’ve compiled a helpful list of Heartbeat tutorials, explainers, and more.

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