In the previous article we talked about perceptrons as one of the earliest models of neural networks. As we have seen, single perceptrons are limited in their computational power since they can solve only linearly separable problems. In this article we will discuss multi-layer perceptrons (MLPs), which are networks consisting of multiple layers of perceptrons and are much more powerful than single-layer perceptrons. We will see how these networks operate and how to use them to solve complex tasks such as image classification. A multi-layer perceptron (MLP) is a neural network that has at least three layers: an input layer, an hidden layer and an output layer.

Generally the first thought that comes to mind when one is about to apply Supervised Learning techniques on images is to make use of Convolutional Neural Networks (CNNs). Indeed, this type of neural network is the most suitable for this type of tasks, mainly due to the reduction of dimensionality. If we imagine a dataset of images where the images have been flattened (for example, an image that is a 4x4 matrix is converted to a 16-dimensional vector, as shown in Figure 1), the images are data points in an n-dimensional space, where n is the number of pixels in the image. As can be deduced, the dimensionality of the data when we talk about images is enormous, and therefore this implies having an immense number of parameters in the neural network, which in turn leads to a higher computational cost and execution time. CNNs reduce the dimensionality of the image in each layer of the neural network, also reducing the number of parameters required in training and optimizing the performance of the model for this type of tasks.

The fundamental role of neural networks (NNs) is readily apparent from their widespread use in machine learning in applications such as natural language processing [72], social network analysis [26], medical diagnosis [6, 35], vision systems [66], and robotic path planning [44]. The greatest success of these models lies in their flexibility, their ability to represent complex, nonlinear relationships in high-dimensional data sets, and the availability of frameworks that allow NNs to be implemented on rapidly evolving GPU platforms [40, 29]. The industrial appetite for deep learning has led to very rapid expansion of the subject in recent years, although, as pointed out by Dunson [19], at times the mathematical and theoretical understanding of these methods has been swept aside in the rush to advance the methodology. The potential impact on society of machine learning algorithms demands that their exposition and use be subject to the highest standards of clarity, ease of interpretation, and uncertainty quantification. Typical NN training seeks to optimize the parameters of the network (biases and weights) under the constraint that the training data set is well approximated [28, 23].

Welcome back to my second post of the series Deep Learning for Rookies (DLFR), by yours truly, a rookie;) Feel free to refer back to my first post here or my blog if you find it hard to follow. Or highlight on this page with notes or leave a comment below! Your feedback will be highly appreciated, too. We will go deeper into neural networks this time and the post will be slightly more technical than last time. But no worries, I will make it as easy and intuitive as possible for you to learn the basics without CS/Math background.

Welcome to the first post of my series Deep Learning for Rookies by me, a rookie. I'm writing as a reinforcement learning strategy to process and digest the knowledge better. But if you are a deep learning rookie, then this is for you as well because we can learn together as rookies!

Welcome to the first post of my series Deep Learning for Rookies by me, a rookie. I'm writing as a reinforcement learning strategy to process and digest the knowledge better. But if you are a deep learning rookie, then this is for you as well because we can learn together as rookies! Deep learning is probably one of the hottest tech topics right now. Large corporations and young startups alike are all gold-rushing this fancy field. If you think big data is important, then you should care about deep learning. The Economist says that data is the new oil in the 21st Century. If data is the crude oil, databases and data warehouses are the drilling rigs that digs and pumps the data on the internet, then think of deep learning as the oil refinery that finally turns crude oil into all the useful and insightful final products.

Welcome back to my second post of the series Deep Learning for Rookies (DLFR), by yours truly, a rookie;) Feel free to refer back to my first post here or my blog if you find it hard to follow. Or highlight on this page with notes or leave a comment below! Your feedback will be highly appreciated, too. We will go deeper into neural networks this time and the post will be slightly more technical than last time. But no worries, I will make it as easy and intuitive as possible for you to learn the basics without CS/Math background.