Activation Function _ day 11

Activation Functions in Neural Networks Activation Functions in Neural Networks: Why They Matter ? Activation functions are pivotal in neural networks, transforming the input of each neuron to its output signal, thus determining the neuron’s activation level. This process allows neural networks to handle tasks such as image recognition and language processing effectively. The Role of Different Activation Functions Neural networks employ distinct activation functions in their inner and outer layers, customized to the specific requirements of the network: Inner Layers: Functions like ReLU (Rectified Linear Unit) introduce necessary non-linearity, allowing the network to learn complex patterns in the data....

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15

Regression vs Classification Multi Layer Perceptrons (MLPs) _ day 10

Regression with Multi-Layer Perceptrons (MLPs) Introduction Neural networks, particularly Multi-Layer Perceptrons (MLPs), are essential tools in machine learning for solving both regression and classification problems. This guide will provide a detailed explanation of MLPs, covering their structure, activation functions, and implementation using Scikit-Learn. Regression vs. Classification: Key Differences Regression Objective: Predict continuous values. Output: Single or multiple continuous values. Example: Predicting house prices, stock prices, or temperature. Classification Objective: Predict discrete class labels. Output: Class probabilities or specific class labels. Example: Classifying emails as spam or not spam, recognizing handwritten digits, or identifying types of animals in images. Regression with...

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56

3 Types of Gradient Decent Types : Batch, Stochastic & Mini-Batch _ Day 8

Understanding Gradient Descent: Batch, Stochastic, and Mini-Batch Understanding Gradient Descent: Batch, Stochastic, and Mini-Batch Learn the key differences between Batch Gradient Descent, Stochastic Gradient Descent, and Mini-Batch Gradient Descent, and how to apply them in your machine learning models. Batch Gradient Descent Batch Gradient Descent uses the entire dataset to calculate the gradient of the cost function, leading to stable, consistent steps toward an optimal solution. It is computationally expensive, making it suitable for smaller datasets where high precision is crucial. Formula: \[\theta := \theta – \eta \cdot \frac{1}{m} \sum_{i=1}^{m} \nabla_{\theta} J(\theta; x^{(i)}, y^{(i)})\] \(\theta\) = parameters \(\eta\) = learning...

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19

Can we make prediction without need of going through iteration ? yes with the Normal Equation _ Day 6

Understanding Linear Regression: The Normal Equation and Matrix Multiplications Explained Understanding Linear Regression: The Normal Equation and Matrix Multiplications Explained Linear regression is a fundamental concept in machine learning and statistics, used to predict a target variable based on one or more input features. While gradient descent is a popular method for finding the best-fitting line, the normal equation offers a direct, analytical approach that doesn’t require iterations. This blog post will walk you through the normal equation step-by-step, explaining why and how it works, and why using matrices simplifies the process. Table of Contents Introduction to Linear Regression Gradient...

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26

Regression & Classification with MNIST. _ day 4

  A Comprehensive Guide to Machine Learning: Regression and Classification with the MNIST Dataset Introduction to Supervised Learning: Regression and Classification In the realm of machine learning, supervised learning involves training a model on a labeled dataset, which means the dataset includes both input data and the corresponding output labels. Supervised learning tasks can be broadly categorized into two types: regression and classification.     Regression tasks aim to predict continuous numerical values. For example, predicting house prices based on various features such as location, size, and number of bedrooms. The output is a continuous value that can range over...

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