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1. What is a Neural Network?A Neural Network is a computational model inspired by the human brain. It consists of layers of artificial neurons that process input data to make predictions or classifications. It is commonly used in machine learning for tasks like image recognition, speech processing, and pattern detection.…
Anupama Yeragudipati
updated on 24 Mar 2025
A Neural Network is a computational model inspired by the human brain. It consists of layers of artificial neurons that process input data to make predictions or classifications. It is commonly used in machine learning for tasks like image recognition, speech processing, and pattern detection. Neural networks learn by adjusting weights and biases through training on data.
Deep Learning is a subset of machine learning that involves neural networks with multiple hidden layers (deep neural networks). These deep structures allow the model to learn complex patterns and representations from raw data, making it highly effective for tasks like natural language processing, autonomous driving, and medical diagnosis.
An activation function determines whether a neuron should be activated or not by applying a transformation to the input. It introduces non-linearity, allowing neural networks to learn complex patterns instead of just simple linear relationships.
Sigmoid Function:
Output: 00 to 11
Used for binary classification
Formula: σ(x)=11+e−xσ(x)=1+e−x1
Disadvantage: Vanishing gradient problem
Tanh (Hyperbolic Tangent) Function:
Output: −1−1 to 11
Used in hidden layers for zero-centered activation
Formula: tanh(x)=ex−e−xex+e−xtanh(x)=ex+e−xex−e−x
Disadvantage: Still suffers from vanishing gradients
ReLU (Rectified Linear Unit):
Output: 00 for negative inputs, xx for positive inputs
Used in most deep networks
Formula: f(x)=max(0,x)f(x)=max(0,x)
Advantage: Avoids vanishing gradient issue for positive values
Disadvantage: "Dying ReLU" problem (neurons can become inactive permanently)
Leaky ReLU:
Allows small negative values instead of zero
Formula: f(x)=xf(x)=x if x>0x>0, else αxαx where αα is a small value (e.g., 0.01)
Helps prevent "dying ReLU" problem
Softmax Function:
Used for multi-class classification
Converts logits into probabilities
Formula: S(xi)=exi∑jexjS(xi)=∑jexjexi
Backpropagation (Backward Propagation of Errors) is an algorithm used to train neural networks by adjusting weights and biases. It works in two main steps:
Forward Propagation: Compute the output using current weights.
Backward Propagation: Calculate the error (difference between actual and predicted output) and update the weights using Gradient Descent.
Key Concepts in Backpropagation:
Uses the chain rule to compute gradients
Updates weights using learning rate
Helps neural networks minimize loss function efficiently
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Project 2
First KMeans (Initial Run) Objective: To apply KMeans clustering on the car dataset (likely based on features like fuel type, city mpg, highway mpg, etc.), without much prior tuning or pre-processing. Steps: Standard KMeans clustering applied using default parameters (e.g., random initialization, fixed number of clusters).…
28 Apr 2025 12:48 PM IST
Project 1
???? Automobile Dataset (1985) Analysis – Project Report ???? Objective The purpose of this project is to perform data cleaning, exploratory data analysis (EDA), and basic machine learning modeling on the 1985 Automobile dataset. This analysis aims to uncover insights into vehicle fuel efficiency, engine performance, and trends…
09 Apr 2025 06:25 PM IST
Unsupervised Learning - Kmeans Week 11 Challenge
How does similarity is calculated if data is categorical in nature1. Hamming Distance Used when categorical variables are binary (0/1, Yes/No, True/False). It calculates the number of positions at which two strings of equal length are different. Formula: d(x,y)=∑i=1nI(xi≠yi)d(x, y) = \sum_{i=1}^{n} I(x_i \neq y_i)d(x,y)=i=1∑nI(xi=yi)…
06 Apr 2025 05:16 PM IST
Supervised Learning - Classification Week 9 Challenge
1. What is a Neural Network?A Neural Network is a computational model inspired by the human brain. It consists of layers of artificial neurons that process input data to make predictions or classifications. It is commonly used in machine learning for tasks like image recognition, speech processing, and pattern detection.…
24 Mar 2025 04:10 PM IST
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