Diving into Deep Learning

  Image Source Machine learning (ML) : Subfield of Artificial Intelligence (AI) in the field of computer science focused on the development of intelligent systems, capable of "learning" (i.e., progressively improve performance on a specific task) with data , without being explicitly programmed. Ref Chronology : Build with GoogleCloud - Chronology tree for ML Perceptron : algorithm for supervised learning of binary classifiers (functions that can decide whether an input, represented by a vector of numbers, belongs to some specific class or not → classification in two parts). “It is a single layer NN”, thus in order to learn how a NN works, we should learn how a perceptron works. Weights : show the strength of the particular node Bias value : allows to shift the activation function Activation function : map input between required values like (0, 1) or (-1, 1).      Fig : Perceptron Deep learning : (also known as ...

4. Understand how a Neural Network works 4.1. Graphical Understanding In order to start in this world, it is important first to have a visual perception of what we are going to deal with, and understand the basics of how Neural Networks work, in the raw form. Image Source A Neural Network (NN) is nothing else than a net of perceptrons that are linked so that input fires another network that produces an output.  Of course, a NN has assigned values that allow to make further calculations and learning.   Image source So far the two images above represent a simple NN with 1 input, 1 output and 1 hidden layer. The hidden layer is said to be dense (each neuron in a layer x is connected to all neurons in the layer x-1 and all the neurons in the layer x+1). Depending on how the neurons are organized and how the connections are made, we can find many different types of NN:   Types of NN (image source and explanation) 4.2....