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Author: datahacker.rs

#006 The moral dilemma of self-driving cars

24.01.2020

How should autonomous vehicles decide who to sacrifice? Almost 1.3 million people die in road crashes each year. One of the most compelling reasons in favor of the introduction of autonomous vehicles is to enhance driving safety and reduce road casualties. However, this prompts the question of whether these cars will be able to make decisions in a split second, and respond just as well as experienced human drivers. And most importantly, how an AI-powered…
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#009 TF An implementation of a Convolutional Neural Network in tf.keras – MNIST dataset

20.01.2020

In this post we will see how we can classify handwritten digits using Convolutional Neural Network implemented in TensorFlow 2.0. Required packages: Numpy Matplotlib Tensorflow Sklearn Seaborn Table of Contents: Load the digit dataset Implementing a Neural Network Visualization and Testing 1. Load the digit dataset Let start with importing all necessary libraries. After imports, we can use imported module to load mnist data. The load_data() function will automatically download and split our data into…
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#007 TF 2.0 An implementation of a Shallow Neural Network in tf.keras – Spirals dataset

20.01.2020

Highlights: In this post we will see how we can classify a spirals dataset with a shallow neural network implemented in TensorFlow 2.0. Tutorial Overview: Imports and Dataset preparation Building a Neural Network Visualization 1. Dataset: import and preparation Let’s start by importing libraries that we will need in our code. Last time we were using a function from sklearn to create a dataset. Now we are going to make our dataset from scratch. First,…
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#006 TF 2.0 An implementation of a Shallow Neural Network in tf.keras – Moons dataset

20.01.2020

In this post we will learn how to make a classification of Moons dataset with a shallow Neural network. The Neural Net we will implemented in TensorFlow 2.0 using Keras API. 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…
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#005 TF 2.0 An implementation of a Shallow Neural Network with tf.keras – Circles dataset

20.01.2020

Highlights: In previous post we have talked about TensorFlow Wrappers and there we concluded that tf.keras is the most convenient way to build neural networks. Now we are going to implement one very simple network using this high-level API. Tutorial Overview: Imports and Dataset preparation Building a Neural Network Visualization 1. Imports and Dataset preparation Let’s start with basic imports. Don’t worry if some things are not familiar with all of these libraries, we will…
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