Activation Function, Hidden Layer and non linearity. _ day 12

Understanding Non-Linearity in Neural Networks Understanding Non-Linearity in Neural Networks Non-linearity in neural networks is essential for solving complex tasks where the data is not linearly separable. This blog post explains why hidden layers and non-linear activation functions are necessary, using the XOR problem as an example. What is Non-Linearity? Non-linearity in neural networks allows the model to learn and represent more complex patterns. In the context of decision boundaries, a non-linear decision boundary can bend and curve, enabling the separation of classes that are not linearly separable. Role of Activation Functions The primary role of an activation function is...

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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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47

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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177

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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247

What is Gradient Decent in Machine Learning? _ Day 7

Mastering Gradient Descent in Machine Learning Mastering Gradient Descent: A Comprehensive Guide to Optimizing Machine Learning Models Gradient Descent is a foundational optimization algorithm used in machine learning to minimize a model’s cost function, typically Mean Squared Error (MSE) in linear regression. By iteratively adjusting the model’s parameters (weights), Gradient Descent seeks to find the optimal values that reduce the prediction error. What is Gradient Descent? Gradient Descent works by calculating the gradient (slope) of the cost function with respect to each parameter and moving in the direction opposite to the gradient. This process is repeated until the algorithm converges...

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50

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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131

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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339

Models based, Instance Models, Train-Test Splits: The Building Blocks of Machine Learning Explained – Day 3

In machine learning and deep learning, the concepts of Model vs Instance Models and Train-Test Split are closely intertwined. A model serves as the blueprint for learning patterns from data, while an instance model represents the specific realization of that blueprint after training. The train-test split, on the other hand, plays a critical role in the creation and evaluation of these instance models by dividing the dataset into subsets for training and testing. This blog post will delve into the relationship between these concepts,   first we explain model vs instance based and then we explain train- test spilt and...

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204

integrate ML into iOS Apps _ Day 2

In the rapidly evolving field of mobile applications, incorporating machine learning (ML) can significantly enhance functionality and user experience. This guide highlights some machine learning frameworks available for iOS development in 2024, enabling developers to choose the right tools tailored to their specific needs. 1. Core ML Apple’s Core ML framework seamlessly integrates machine learning models into iOS apps, optimizing for on-device performance to ensure data privacy and swift operation. Ideal for a range of applications including image classification and natural language processing, Core ML is a cornerstone for developers aiming to implement intelligent features. Learn more about Core ML...

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113

Machine learning (ML) Overview _ Day 1

What is Machine Learning? Machine Learning (ML) is a subset of artificial intelligence (AI) that enables computers to learn from data and make decisions without being explicitly programmed. By identifying patterns and correlations in data, ML models can perform tasks such as prediction, classification, and optimization. For instance, Netflix uses machine learning to recommend shows and movies based on a user’s viewing history. ML has revolutionized fields like healthcare, finance, e-commerce, and robotics by automating complex decision-making processes and enabling systems to adapt to new information. The fundamental idea of machine learning is that machines can improve their performance over...

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180