https://utsavdarlami.github.io/blogs/tags/deep-learning/Recent content in Deep Learning on felladogHugo -- gohugo.ioenTue, 13 Jul 2021 20:54:39 +0545https://utsavdarlami.github.io/blogs/notes/20210224173929-perceptron/Wed, 26 May 2021 17:16:00 +0545https://utsavdarlami.github.io/blogs/notes/20210224173929-perceptron/References : Done Reading: Sebastian Raschka Perceptron Lecture Slide Reading: Tom Mitchell Lectures slides of chapter 4 Tom Mitchell, Machine Learning Chapter 4 To Read: https://www.wikiwand.com/en/Perceptron Questions : A learning rule for the computational/mathematical neuron model Rosenblatt, F. (1957). The perceptron, a perceiving and recognizing automaton. Project Para. Cornell Aeronautical Laboratory Figure 1: A perceptron \(o(\overrightarrow{x}) = sgn(\overrightarrow{w} \cdot \overrightarrow{x})\) where, \( sgn(y) = \begin{cases} 1 & \text{if y > 0} \\ -1 & \text{otherwise} \end{cases} \) Space \(H\) of candidate hypotheses is \(H = \{ \overrightarrow{w} | \overrightarrow{w} \in \Re^{n+1} \}\) Representations A single layer perceptron can be used to represent many boolean functions AND \(w_{0}\) = -.