Category: Other

#K An implementation of a Shallow Neural Network in Keras – Spirals dataset

In this post we will see how we can classify a spirals dataset with a shallow neural network implemented in Keras. Let’s start by importing libraries that we will need in the our code. Here, we will make our dataset and divide it into training and testing set. Let’s now create a shallow neural network! Next, we will make predictions and plot the accuracy and loss function of our model. Now, we will make some…
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# K An implementation of a Shallow Neural Network in Keras – Moons dataset

In this post we will learn how to make classification of Moons dataset with a shallow neural network implemented in Keras. With the following code we are going to import all libraries that we will need. First, we will generate a random dataset, then we will split it into train and test set. We will also print dimensions of these datasets. With the following code we will make a shallow neural network. Our shallow neural…
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#K An implementation of a Shallow Neural Network in Keras – Circles dataset

In the next post we will learn how to perform classification using a shallow neural network on Moons dataset using Keras. More resources on the topic: Classification With Shallow Neural Network on Moons Dataset. A Simple Neural Network in Keras, PyImageSearch. Shallow Neural Network in Keras, Datahacker. Hand digit Recognizer in TensorFlow.

# TF An implementation of a Shallow Neural Network in TensorFlow – Circles dataset

In this post we will see in details how to make a shallow neural network in TensorFlow. Our first step will be to import all libraries that we will need in the following code. Now, Let’s create the same dataset used in this post. Then split the dataset into two part, a train and test set. Now let’s make a visualization of what was generated. With the following code we will make sure that \(X…
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#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…
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