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# Category: Other

### #003 D TF Gradient Descent in TensorFlow

In this post we will see how to implement Gradient Descent using TensorFlow. Next, we will define our variable $$\omega$$ and we will initialize it with $$-3$$. With the following peace of code we will also define our cost function $$J(\omega) = (\omega – 3)^2$$. With the next two lines of code, we specify the initialization of our variables (here we have just one variable $$\omega$$ and the gradient descent for…

### #TF Logistic Regression in TensorFlow

In this post we will see how to implement Logistic Regression in TensorFlow. Let’s generate a dataset using random values. It’s very crucial to check the size of your array. Let’s split our dataset into a train and test set. For the training set 80% of the original dataset would be used, while the remaining 20% would be used for testing. Let’s set the parameters which would be used in training our model. Now, let’s…

### #024 CNN Convolutional Operation of Sliding Windows

Convolutional operation of sliding windows In the previous post we learned about the sliding windows object detection algorithm using a $$convnet$$, but we saw that it was too slow. In this post we will see how to implement that algorithm convolutionaly. Let’s see what that means. To build up the convolutional implementation of sliding windows, let’s first see how we can turn $$Fully \enspace connected$$ layers in our neural network into $$Convolutional$$ layes.…
Object Detection We have learned about object localization as well as landmark detection, now let’s build an object detection algorithm. In this post we’ll learn how to use a $$convnet$$ to perform object detection using a Sliding windows detection algorithm. Car detection – an example Let’s say we want to build a car detection algorithm. Some examples of a training set We can first create a labeled training set $$(x,y)$$ with closely cropped…
Landmark Detection In the previous post we saw how we can get a neural network to output $$4$$ numbers: $$b_{x}$$, $$b_{y}$$ ,$$b_{h}$$, and $$b_{w}$$ to specify the bounding box of an object we want neural network to localize. In more general cases we can have a neural network which outputs just $$x$$ and $$y$$ coordinates of important points in the image, sometimes called landmarks.  Let’s see a few… 