Anthony Bryant

1614072120

Working with Kernels in TensorFlow

The prior SVMs worked with linearly separable data. If we would like to separate non-linear data, we can change how we project the linear separator onto the data. This is done by changing the kernel in the SVM loss function. In this article, we introduce how to change kernels and separate non-linear separable data.

Getting ready

We will motivate the usage of kernels in support vector machines. In the linear SVM section, we solved the soft margin with a specific loss function. A different approach to this method is to solve what is called the dual of the optimization problem. It can be shown that the dual for the linear SVM problem is given by the following formula:

Image for post

Here, the variable in the model will be the b vector. Ideally, this vector will be quite sparse, only taking on values near 1 and -1 for the corresponding support vectors of our dataset. Our data point vectors are indicated by_ xi _and our targets ( 1 or -1 ) are represented by _yi _.

The kernel in the preceding equations is the dot product,_ xi.xj,_, which gives us the linear kernel. This kernel is a square matrix filled with the _i,j _dot products of the data points.

Instead of just doing the dot product between data points, we can expand them with more complicated functions into higher dimensions, in which the classes may be linearly separable. This may seem needlessly complicated, but if we select a function, k, that has the property where:

Image for post

#deep-learning #tensorflow #machine-learning #kernel

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Working with Kernels in TensorFlow

Anthony Bryant

1614072120

Working with Kernels in TensorFlow

The prior SVMs worked with linearly separable data. If we would like to separate non-linear data, we can change how we project the linear separator onto the data. This is done by changing the kernel in the SVM loss function. In this article, we introduce how to change kernels and separate non-linear separable data.

Getting ready

We will motivate the usage of kernels in support vector machines. In the linear SVM section, we solved the soft margin with a specific loss function. A different approach to this method is to solve what is called the dual of the optimization problem. It can be shown that the dual for the linear SVM problem is given by the following formula:

Image for post

Here, the variable in the model will be the b vector. Ideally, this vector will be quite sparse, only taking on values near 1 and -1 for the corresponding support vectors of our dataset. Our data point vectors are indicated by_ xi _and our targets ( 1 or -1 ) are represented by _yi _.

The kernel in the preceding equations is the dot product,_ xi.xj,_, which gives us the linear kernel. This kernel is a square matrix filled with the _i,j _dot products of the data points.

Instead of just doing the dot product between data points, we can expand them with more complicated functions into higher dimensions, in which the classes may be linearly separable. This may seem needlessly complicated, but if we select a function, k, that has the property where:

Image for post

#deep-learning #tensorflow #machine-learning #kernel

Alice Cook

Alice Cook

1614329473

Fix: G Suite not Working | G Suite Email not Working | Google Business

G Suite is one of the Google products, developed form of Google Apps. It is a single platform to hold cloud computing, collaboration tools, productivity, software, and products. While using it, many a time, it’s not working, and users have a question– How to fix G Suite not working on iPhone? It can be resolved easily by restarting the device, and if unable to do so, you can reach our specialists whenever you want.
For more details: https://contactforhelp.com/blog/how-to-fix-the-g-suite-email-not-working-issue/

#g suite email not working #g suite email not working on iphone #g suite email not working on android #suite email not working on windows 10 #g suite email not working on mac #g suite email not syncing

Xfinity Stream Not Working?

Xfinity, the tradename of Comcast Cable Communications, LLC, is the first rate supplier of Internet, satellite TV, phone, and remote administrations in the United States. Presented in 2010, previously these administrations were given under the Comcast brand umbrella. Xfinity makes a universe of mind boggling amusement and innovation benefits that joins a great many individuals to the encounters and minutes that issue them the most. Since Xfinity is the greatest supplier of link administrations and home Internet in the United States, it isn’t amazing that the organization gets a ton of investigating and inquiry goal demands on its telephone based Xfinity Customer Service.

#my internet is not working comcast #comcast tv remote not working #my xfinity internet is not working #xfinity stream not working #xfinity wifi hotspot not working

5 Steps to Passing the TensorFlow Developer Certificate

Deep Learning is one of the most in demand skills on the market and TensorFlow is the most popular DL Framework. One of the best ways in my opinion to show that you are comfortable with DL fundaments is taking this TensorFlow Developer Certificate. I completed mine last week and now I am giving tips to those who want to validate your DL skills and I hope you love Memes!

  1. Do the DeepLearning.AI TensorFlow Developer Professional Certificate Course on Coursera Laurence Moroney and by Andrew Ng.

2. Do the course questions in parallel in PyCharm.

#tensorflow #steps to passing the tensorflow developer certificate #tensorflow developer certificate #certificate #5 steps to passing the tensorflow developer certificate #passing

Mckenzie  Osiki

Mckenzie Osiki

1623139838

Transfer Learning on Images with Tensorflow 2 – Predictive Hacks

In this tutorial, we will provide you an example of how you can build a powerful neural network model to classify images of **cats **and dogs using transfer learning by considering as base model a pre-trained model trained on ImageNet and then we will train additional new layers for our cats and dogs classification model.

The Data

We will work with a sample of 600 images from the Dogs vs Cats dataset, which was used for a 2013 Kaggle competition.

#python #transfer learning #tensorflow #images #transfer learning on images with tensorflow #tensorflow 2