In this post we will understand one of the most important part of a Neural Network : Hidden Layers. We will be able to use this information to control the parameters related to hidden layers. Hidden layers and their role Multi Layer Neural Network The Role of Hidden Layers The …

Read More »## 204.5.9 Building a Neural Network in Python

Building the Neural Network The good news is… We don’t need to write the code for weights calculation and updating There readymade codes, libraries and packages available in Python The gradient descent method is not very easy to understand for a non mathematics students Neural network tools don’t expect the …

Read More »## 204.5.8 Neural Network Algorithm-Demo

In previous post we briefly discussed how the algorithm works. In this post we will implement the algorithm on a simple case. Neural network Algorithm-Demo Looks like a dataset that can’t be separated by using single linear decision boundary/perceptron Lets consider a similar but simple classification example XOR Gate Dataset …

Read More »## 204.5.7 The Neural Network Algorithm

In all previous posts we progressed to this part. We will breakdown the steps how a neural network starts and ends. The Neural Network Algorithm Step 1: Initialization of weights: Randomly select some weights Step 2 : Training & Activation: Input the training values and perform the calculations forward. Step …

Read More »## 204.5.6 Neural Network Intuition

Before going further into neural network algorithm, we need to understand and break down how the algorithm is working. Neural Network Intuition Final Output y=out(h)=g(∑Wjhj)hj=out(x)=g(∑w(jk)xk) y=out(h)=g(∑Wjg(∑w(jk)xk)) So h is a non linear function of linear combination of inputs – A multiple logistic regression line Y is a non linear function …

Read More »## 204.5.5 Practice : Implementing Intermediate outputs in Python

In this post we will learn how to implement the concept of intermediate outputs using python. We will cover many things in this session. Practice : Intermediate output Dataset: Emp_Productivity/ Emp_Productivity_All_Sites.csv Filter the data and take first 74 observations from above dataset . Filter condition is Sample_Set<3 Build a logistic …

Read More »## 204.5.4 Issue with Non Linear Decision Boundary

In previous post we just tried solving a non linear data using linear boundary. We can use multiple linear boundaries to separate classes in this kind of datasets. However, this will cause some issues which we will understand bu visualizing the problem and think about a possible solution too. Non-Linear …

Read More »## 204.5.3 Practice : Non Linear Decision Boundary

Linear decision boundaries is not always way to go, as our data can have polynomial boundary too. In this post we will just see what happens if we try to use a linear function to classify a bit complex data. Non-Linear Decision Boundaries Dataset: “Emp_Productivity/ Emp_Productivity.csv” Draw a scatter plot …

Read More »## 204.5.1 Neural Networks : A Recap of Logistic Regression

Welcome to this Blog series on Neural Networks. In the series 204.5 we will go from basics of neural networks to build a neural network model that recognizes digit images and reads them correctly. In this post we will just revise our understanding of how logistic regression works, which can …

Read More »## 204.5.2 Decision Boundary – Logistic Regression

In last session we recapped logistic regression. There is something more to understand before we move further which is Decision Boundary. Once we get decision boundary right we can move further to Neural networks. Decision Boundary – Logistic Regression The line or margin that separates the classes Classification algorithms are …

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