Introduction To Neural Networks

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Questions and Answers

How does machine learning primarily acquire knowledge?

  • Through random chance
  • By learning from data (correct)
  • By following pre-programmed instructions
  • Through explicit user commands

What is a key difference between traditional programming and machine learning?

  • Traditional programming requires explicit rules to process data, while machine learning learns rules from data. (correct)
  • Traditional programming uses data to generate rules, while machine learning uses rules to process data.
  • Traditional programming is suitable for complex problems, while machine learning is best for simple tasks.
  • Traditional programming handles only numerical data, while machine learning works with categorical data.

Consider a dataset with 'x' and 'y' values. Given the following pairs: (1, 3), (2, 5), (3, 7), (4, 9), what 'y' value would machine learning predict for x=5, assuming a linear relationship?

  • 11 (correct)
  • 10
  • 12
  • 8

Given a dataset with some 'noise' in the 'y' values: (1, 7.2), (2, 9.1), (3, 10.9), (4, 13.3), which function best models the relationship to predict 'y' for a new 'x' value?

<p>$f(x) = 2x + 5$ (C)</p>
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Machine learning models can handle complex relationships, with higher dimensions represented as what?

<p>Hyperplanes (C)</p>
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In a dataset, if x1 represents one variable and x2 represents another, and the result is determined by the rule: Result = 1 if x1 > 50 and x2 > 50, otherwise Result = 0. What would the result be for x1 = 55 and x2 = 51?

<p>1 (D)</p>
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Which type of predictive task involves assigning data points to predefined categories?

<p>Classification (A)</p>
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Which machine learning task is best suited for predicting continuous values, such as house prices?

<p>Regression (B)</p>
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Predicting whether a student will be placed based on their CGPA, IQ, and written test scores is an example of what?

<p>Classification (C)</p>
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Identifying whether an email is spam or not is best addressed through which type of machine learning task?

<p>Classification (C)</p>
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Which of the following is a key characteristic of machine learning?

<p>Ability to perform tasks without explicit instructions (B)</p>
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What characterizes supervised learning in machine learning?

<p>Learning from labeled data (C)</p>
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Which type of machine learning involves an agent learning to make decisions in an environment to maximize a reward?

<p>Reinforcement Learning (D)</p>
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Which machine learning type is characterized by finding patterns and relationships in data without explicit labels?

<p>Unsupervised learning (B)</p>
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In the machine learning workflow, what immediately follows the data collection phase?

<p>Data Analysis (A)</p>
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Which of these options represents a major challenge in traditional machine learning?

<p>Manual Feature Engineering (A)</p>
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Deep learning is particularly effective when dealing with what type of data and problems?

<p>Very large datasets and complex problems (C)</p>
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Which of the following best describes the relationship between Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL)?

<p>DL is a subset of ML, which is a subset of AI (B)</p>
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In the context of neural networks, what is the role of a neuron?

<p>To process and transmit signals (C)</p>
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What does 'forward propagation' refer to in the context of neural networks?

<p>The process of feeding input data through the network to generate an output (B)</p>
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If a neuron has inputs $x_1$, $x_2$, and $x_3$ with corresponding weights $w_1$, $w_2$, and $w_3$, what is the output before applying the activation function, according to the diagram?

<p>$w_1 * x_1 + w_2 * x_2 + w_3 * x_3$ (C)</p>
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In an artificial neuron, what role does the 'bias' (b) typically play?

<p>It adds a constant value to the weighted sum of inputs. (A)</p>
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What is the purpose of the 'step function' in a simple perceptron model?

<p>To introduce non-linearity into the model (B)</p>
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Given the equation for a perceptron's output: Output = 0 if w^T * X + b < threshold and Output = 1 if w^T * X + b > threshold, what does $w^T * X$ represent?

<p>The dot product of the weight vector and the input vector (A)</p>
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What distinguishes deep learning from traditional machine learning?

<p>Deep learning automatically learns feature representations, while traditional ML often relies on manual feature engineering. (C)</p>
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Flashcards

Artificial Intelligence

Designing intelligent agents to receive percepts and take actions in an environment.

Machine Learning

A subset of AI where computers learn from data without explicit programming.

Machine Learning Model

ML finds patterns in data to create a model to estimate new values.

Supervised Learning

ML approach using labeled data to train a model for classification or regression.

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Unsupervised Learning

ML approach where computers find the hidden patterns in unlabeled data.

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Reinforcement Learning

ML approach where an agent learns to make decisions by receiving rewards.

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Classification

Categorizing data into discrete classes (e.g., tumor classification).

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Regression

Predicting continuous values (e.g., stock market prediction).

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Feature

An individual characteristic or attribute used for prediction in machine learning.

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Deep Learning Motivation

Very large datasets with complex problems that traditional machine learning can’t solve.

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Deep Learning

Modeling connections between inputs and output in the brain.

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Artificial Neuron

A basic computational unit in a neural network modeling a biological neuron.

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Weighted Inputs

Input values to a neuron, each multiplied by a weight.

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Weighted Sum

The sum of weighted inputs in a neuron, plus a bias.

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Step Function

A function that produces the output of a neuron based on its input.

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Study Notes

  • Project School: Embedded Learning, 2024-2025

Introduction

  • Deep Learning, Topic: Introduction To Neural Networks

Chapter Breakdown

  • Chapter 1: Understand Machine Learning in brief, Applications of Machine Learning
  • Chapter 2: Types of Machine Learning, ML Algorithms for reference
  • Chapter 3: Neural Networks: A game changer, Perceptron, Forward Propagation

Artificial Intelligence (AI)

  • Involves designing and building intelligent agents.
  • These agents receive percepts from the environment.
  • These agents take actions that affect their environment.
  • Mimicking the intelligence or behavioral pattern of humans or any other living entity.

AI Techniques/Algorithms

  • Evolutionary algorithms
  • Expert systems
  • Search algorithms (e.g., A*)
  • Machine learning

Machine Learning Basics

  • Machine Learning can learn from data.
  • Data helps to learn from experience.
  • Machine follows instructions
  • Traditional programming inputs Data and Set of Rules to the computer which give results
  • Machine Learning puts Data and Results into the computer which in turn figures out the Set of Rules, or Model

Example: Predicting Y Values

  • Given x and y values, the task is to predict the value of y for a new x value.
  • For x = 1, y = 3; x = 2, y = 5; x = 3, y = 7; x = 4, y = 9; therefore, for x = 5, y = 11.
  • The predictive model is Y = 2x + 1.
  • When given a noisy data set such as x = 1, y = 7.2; x = 2, y = 9.1; x = 3, y = 10.9; x = 4, y = 13.3; x = 5 is 15 using the equation f(x) = 2x + 5
  • The correct equation would be k(x) = 2.1x + 7.2

Geometric Representation

  • The equation Z = 2x1 + 3x2 + 1 represents a plane.
  • In higher dimensions, it becomes a hyperplane.

Exercise: Predicting Results Based on Two Variables

  • Given x1 and x2, predict the Result.
  • If x1 = 55, x2 = 65, Result = 1; x1 = 45, x2 = 60, Result = 0; x1 = 52, x2 = 55, Result = 1; x1 = 48, x2 = 49, Result = 0; x1 = 60, x2 = 70, Result = 1; hence, x1 = 55, x2 = 51, Result = 1.
  • Rule: Result = 1 if x1 > 50 and x2 > 50; Result = 0 if x1 <= 50 or x2 <= 50.

Traditional Programming Example

  • In traditional programming, you need to explicitly define the conditions.
  • Example:
    • x1 = int(input("Enter a number for x1: "))
    • x2 = int(input("Enter a number for x2: "))
    • if x1 <= 50: print ("0")
    • elif x2 <= 50: print ("0")
    • else: print ("1")

Types of Predictive Tasks

  • Classification: splits discrete or categorical data and has a category associated, such as tumor classification.
  • Regression: fits real number data and has an associated number, such as prediction of stock market.

Feature Examples

  • Housing Price Prediction considers area, rooms, and parking.
  • Placement Prediction considers cgpa, IQ, and written test.

Machine Learning Tasks

  • Predicting house prices based on location and size
  • Identifying spam emails in an inbox
  • Diagnosing disease based on medical test results
  • Estimating stock market prices over time
  • Forecasting daily temperature from historical weather data
  • Identifying animals from images (e.g., cats vs. dogs)

Machine Learning in Brief

  • Computer performs a task without using explicit instructions
  • Computer learns from data

Machine Learning Types

  • Supervised Learning
  • Reinforcement Learning
  • Unsupervised Learning

Reinforcement Learning

  • Examples are Chess, Shogi, and Go

ML Workflow

  • Receive data
  • Analyze data
  • Find patterns
  • Make predictions
  • Send answer

Traditional Machine Learning - Challenges

  • Feature Engineering - Manual/domain-expert
  • (Relatively) small dataset
  • Less computation intensive
  • Ideal for "simple” problems

AI, Machine Learning, and Deep Learning Relationships

  • AI mimics the intelligence or behavioral patterns of living entities.
  • Machine Learning is a technique for computers to "learn" from data without complex rules, based on training a model from datasets examples: Support Vector Machine and Random Forest
  • Deep Learning is a technique to perform machine learning inspired by brain's neurons example: Convolutional Neural Network

Deep Learning - Motivation

  • Requires a very large dataset
  • Suited for Complex problems where traditional ML fails
  • Needs access to extensive computational resources

Neuron Structure and Function

  • Neuron receives inputs (x1, x2, x3) with associated weights (w1, w2, w3).
  • Output = w1 * x1 + w2 * x2 + w3 * x3
  • Artificial Neuron:
    • Summer takes the sum of x(i) * w(i) + b
    • Threshold unit decides the ouput

Perceptron

  • z = w1* X1 + w2* X2 + w3* X3 + w4X4... + wnxn + b
  • Output = 0 if ∑wjxj + b < threshold
  • Output = 1 if ∑wjxj + b > threshold, where w is weight and b is bias.
  • Output = 0 if w(transpose)X + b < threshold
  • Output = 1 if w(transpose)X + b > threshold
  • In this equation X are inputs and w is the assigned weight

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