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What is a Neural Network Neural Networks for Machine Learning Simply Explained

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Lessons List | 6 Lesson

Optimization Techniques in Neural Networks Neural Network for Machine Learning

Optimization Techniques in Neural Networks Neural Network for Machine Learning

Types of Activation Functions in Neural Network Artificial Intelligence Machine Learning

Types of Activation Functions in Neural Network Artificial Intelligence Machine Learning

Activation Function in Neural Networks Machine Learning

Activation Function in Neural Networks Machine Learning

Cost Function in Neural Networks Neural Networks for Machine Learning

Cost Function in Neural Networks Neural Networks for Machine Learning

How Neural Network Works Neural Networks in Machine Learning

How Neural Network Works Neural Networks in Machine Learning

What is a Neural Network Neural Networks for Machine Learning Simply Explained

What is a Neural Network Neural Networks for Machine Learning Simply Explained

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4.2

13 Reviews

shiv a

Good 2025-08-22

KAVINAYA S

KAVINAYA S 2025-08-18

Vamsi Kumar

SUPER 2025-03-27

Blessy.s

Good 2025-03-24

Mohammad Rahimunnisa

good 2025-03-24

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Very Good 2025-03-21

Margareta Strimbeanu

Good 2025-03-08

Dhiraj Sugre

Gud 2025-03-08

Samuel Alabi

I had a good time learning Neural Network. 2025-03-04

Niharika Mathur

good 2025-02-25

Fatemeh Rezaeian

Good 2025-02-23

Yarram Lohitha Naga Gayathri

Good 2025-02-16

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Course Description

Neural network optimization, in this course we will learn about the Neural Network Optimization techniques essential for improving model performance and training efficiency. Starting with foundational concepts, we explore optimization algorithms like Gradient Descent, Stochastic Gradient Descent (SGD), and advanced methods such as Adam, RMSProp, and AdaGrad. You’ll discover how learning rate schedulers enhance convergence, including step decay and exponential decay strategies. The course covers essential techniques like Regularization (L1, L2, and Dropout) to prevent overfitting, Batch Normalization to stabilize training, and Early Stopping to avoid unnecessary computations. Additionally, we’ll dive into Hyperparameter Tuning, Weight Initialization methods, and Data Augmentation for robust model training. By the end, you’ll master the tools and strategies needed to optimize neural networks effectively for real-world applications. TutorialsPoint

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