I am briefly sharing a video from the last TensorFlow Dev Summit in February 2017. My choice has fallen to a presentation by François Chollet of the deep learning library API Keras and its integration with TensorFlow. As Dr. Chollet explains, Keras integrated with TensorFlow promises to streamline deep learning frameworks in ways that will…
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- Back in November, Google released artificial intelligence experiment that asks you to draw a random object and see if the neural network can identify your doodle.
- was eventually turned into a tool that transformed drawings into clip art based on the best results it got, helping people add a visual icon to their work without requiring any particular artistic talent.
- I wasn’t very confident in my frog (croak) so I felt that adding “Ribbit” to the drawing might provide context, even if the AI might not be able to read.
- Face is a neat one too — I’d guess that depending on the artist, most of these drawings were interpreted as a self portrait.
- While you can argue that most people see frog in mostly the same way, dragon produced a variety of results — from fire-breathing to horned.
Back in November, Google released artificial intelligence experiment that asks you to draw a random object and see if the neural network can identify your doodle. Quick, Draw! was eventually turned…
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- Want to get started on data science?
- This book has been written in layman’s terms as a gentle introduction to data science and its algorithms.
- Each algorithm has its own dedicated chapter that explains how it works, and shows an example of a real-world application.
- With this book, we hope to give you a practical understanding of data science, so that you, too, can leverage its strengths in making better decisions.
Want to get started on data science? Our promise: no math added.
This book has been written in layman’s terms as a gentle introduction to data science and its…
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- That’s the idea being developed by biomedical researchers from Newcastle University, who have developed a prototype prosthetic limb with a AI-powered camera mounted on top.
- When the wearer of the limb moves to grab, say, a mug, the camera takes a picture of the object, and moves the hand into a suitable “grasp type.”
- “Using computer vision, we have developed a bionic hand which can respond automatically — in fact, just like a real hand, the user can reach out and pick up a cup or a biscuit with nothing more than a quick glance in the right direction,” said Dr. Kianoush Nazarpour, a biomedical lecturer at Newcastle University, in a press statement.
- The end result is a hand that is much quicker to use than contemporary prosthetics — up to 10 times faster than others on the market, say the researchers.
- The neural network used to recognize objects wasn’t always correct (it had about an 80 to 90 percent success rate) and amputees who tested the prosthetic had to be able to override its actions when necessary.
When the wearer of a prosthetic arm wants to grab something, there are a number of ways they can communicate this signal. With a basic prosthetic, the grip mechanism might be controlled…
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- The blue line gives the actual house prices from historical data (Yactual)
The difference between Yactual and Ypred (given by the yellow dashed lines) is the prediction error (E)
So, we need to find a line with optimal values of a,b (called weights) that best fits the historical data by reducing the prediction error and improving prediction accuracy.
- So, our objective is to find optimal a, b that minimizes the error between actual and predicted values of house price:
This is where Gradient Descent comes into the picture.
- Gradient descent is an optimization algorithm that finds the optimal weights (a,b) that reduces prediction error.
- Lets now go step by step to understand the Gradient Descent algorithm:
Initialize the weights(a & b) with random values and calculate Error (SSE)
Calculate the gradient i.e. change in SSE when the weights (a & b) are changed by a very small value from their original randomly initialized value.
- Adjust the weights with the gradients to reach the optimal values where SSE is minimized
Use the new weights for prediction and to calculate the new SSE
Repeat steps 2 and 3 till further adjustments to weights doesn’t significantly reduce the Error
We will now go through each of the steps in detail (I worked out the steps in excel, which I have pasted below).
In Data Science, Gradient Descent is one of the important and difficult concepts. Here we explain this concept with an example, in a very simple way. Check this out.
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- 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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- 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.
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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