GitHub

Nice work by @hereismari getting started with @TensorFlow on Android!

  • If you want to make your own version of this app or want to know how to save your model and export it for Android or other devices check the very simple tutorial bellow.
  • A full example can be seen here

    Keep an in memory copy of eveything your model learned (like biases and weights) Example: , where w was learned from training.

  • Rewrite your model changing the variables for constants with value = in memory copy of learned variables.
  • Example: Also make sure to put names in the input and output of the model, this will be needed for the model later.
  • Example:

    Export your model with:

    tf.train.write_graph(, , .

mnist-android-tensorflow – Handwritten digits classification from MNIST with TensorFlow in Android; Featuring Tutorial!
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Introducing a Smarter Way to Message and Build Meaningful Relationships on LinkedIn

The #nextbigthing on #LinkedIn? #ArtificialIntelligence - ! 🤖 #ai #SoSoShow

  • It’s not surprising as conversations are at the heart of unlocking opportunities such as a new job, business lead or career advice.
  • That’s why we’re rolling out an even smarter messaging experience with the ability to message a connection from wherever you may be on LinkedIn, along with suggestions that can help connect you to a new job.
  • For example, you can have a conversation with a connection without ever leaving their profile, or reach out to someone directly from the LinkedIn Feed.
  • In fact, nearly 50% of LinkedIn members have found a job through a mutual connection.
  • For example, when looking at a job posting at a company you’re interested in we’ll show you which of your connections work there, or who can introduce you to someone at the company.

Did you know that 80% of LinkedIn members consider professional networking to be important to career success? It’s not surprising as conversations are at the heart of unlocking opportunities such as a new job, business lead or career advice.
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Google used your pictionary sketches to teach its AI to draw

Google used your pictionary sketches to teach its AI to draw

  • Google has been working on a wide range of AI-based projects lately – earlier this week, it showed off one that can identify what you’re trying to draw and surface clean clipart that resembles your doodle.
  • Its latest experiment is called Sketch-RNN, and it’s a neural network system that has learned to draw on its own by looking at roughly 5.5 million sketches from people who played Pictionary with Google’s AI-powered Quick, Draw!
  • By triaging sketches in 75 different categories like cats, pigs and trucks, the AI can now draw basic representations of these things when presented with hand-drawn sketches.
  • Sketch-RNN can also draw without the help of a starting sketch, and can even complete sketches that a human has started, but not finished.
  • Sure, the sketches aren’t exactly photorealistic, but the idea here is to ‘train a machine to draw and generalize abstract concepts in a manner similar to humans’, and Google has achieved that.

Google’s latest experiment is a neural network system that has learned to draw by looking at roughly 5.5 million sketches from people who played pictionary.
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How Accounts Can Future-Proof Their Careers In The Era Of Artificial Intelligence

How Accounts Can Future-Proof Their Careers In The Era Of Artificial Intelligence

  • Many of these same experts point to the human experience as a key differentiator for accountants.
  • Many people and businesses have unique needs that first or second generation AI will be unable to understand.
  • Accountants today have the power to define the future of the profession.
  • The industry must develop the ability to adapt and evolve, as well as become proactive about the needs of tomorrow’s clients.
  • Accounting as a profession needs to change, providing consultation and guidance to help clients prepare for and meet the future.

How Accounts Can Future-Proof Their Careers In The Era Of Artificial Intelligence
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A Visual Introduction to Machine Learning

A Visual Introduction to #MachineLearning #abdsc

  • Using a data set about homes, we will create a machine learning model to distinguish homes in New York from homes in San Francisco.
  • Let’s say you had to determine whether a home is in San Francisco or in New York.
  • In machine learning terms, categorizing data points is a classification task.Since San Francisco is relatively hilly, the elevation of a home may be a good way to distinguish the two cities.
  • Based on the home-elevation data to the right, you could argue that a home above 240 ft should be classified as one in San Francisco.
  • The data suggests that, among homes at or below 240 ft, those that cost more than $1776 per square foot are in New York City.

This article was written by Stephanie and Tony on R2D3. 
In machine learning, computers apply statistical learning techniques to automatically identify pattern…
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5 things the Samsung Galaxy S8’s Bixby artificial intelligence service will do

5 things the Samsung Galaxy S8’s Bixby artificial intelligence service will do

  • According to Samsung, it sure can, and that’s what it the company out to prove with its Bixby AI service.
  • Bixby is being loaded on the Galaxy S8 and S8+ smartphones, which were announced on Tuesday.
  • Bixby is an agent that can help the smartphones talk, recommend, and remind, said Mok Oh, vice president of service strategy at Samsung.
  • The AI service is being positioned as a more intuitive way to use and interact with smartphones.
  • For example, Bixby can help smartphones execute tasks with a voice command.

5 things the Samsung Galaxy S8’s Bixby artificial intelligence service will do
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Google’s new machine learning API recognizes objects in videos

Google’s new machine learning API recognizes objects in videos

  • At its Cloud Next conference in San Francisco, Google today announced the launch of a new machine learning API for automatically recognizing objects in videos and making them searchable.
  • The new Video Intelligence API will allow developers to build applications that can automatically extract entities from a video.
  • Until now, most similar image recognition APIs available in the cloud only focused on doing this for still images, but with the help of this new API, developers will be able to build applications that let users search and discover information in videos.
  • Besides extracting metadata, the API allows you to tag scene changes in a video.

Google’s new machine learning API recognizes objects in videos
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