Deep Learning

Deep Learning

Chapter 12: Practical Deep Learning Projects Using CNN and RNN – Step-by-Step Guide

Practical Projects Using CNN and RNN In this chapter, you will apply everything you have learned throughout the course. We will build real-world deep learning projects using both CNNs (for.

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Deep Learning

Chapter 11: Transfer Learning in Deep Learning – Complete Beginner Guide with Real-Life Examples

Transfer Learning One of the biggest challenges in deep learning is the amount of data required. Training powerful models from scratch often demands millions of images, weeks of GPU time,.

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Deep Learning

Chapter 10: Autoencoders in Deep Learning – Complete Guide with Examples

Autoencoders Autoencoders are one of the most fascinating and useful deep learning architectures. They are designed to learn efficient representations of data, usually for the purpose of dimensionality reduction, noise.

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Deep Learning

Chapter 9: LSTM (Long Short-Term Memory) Networks – Complete Beginner Guide with Examples

Long Short-Term Memory (LSTM) LSTM (Long Short-Term Memory) networks are one of the most important inventions in deep learning. They are a special type of Recurrent Neural Network (RNN) designed.

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Deep Learning

Chapter 8: Recurrent Neural Networks (RNNs) – Complete Guide with Real-Life Examples

Recurrent Neural Networks (RNNs) While Convolutional Neural Networks (CNNs) are designed to process images, Recurrent Neural Networks (RNNs) are designed for sequence-based data — data that changes with time or.

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Deep Learning

Chapter 7: Convolutional Neural Networks (CNNs) Explained – Simple Guide with Real-Life Examples

Convolutional Neural Networks (CNNs) CNNs are one of the most powerful deep learning architectures ever created. They are the reason behind breakthroughs in computer vision — including face recognition, self-driving.

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Deep Learning

Chapter 6: Introduction to TensorFlow and Keras – Build Deep Learning Models Easily (Beginner Friendly)

Introduction to TensorFlow and Keras After understanding neural networks, perceptrons, activation functions, and backpropagation, it’s time to explore how deep learning is built in the real world. The most popular.

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Deep Learning

Chapter 5: Forward and Backpropagation Explained – How Neural Networks Learn (Beginner Friendly)

Forward and Backpropagation In the earlier chapters, you learned about perceptrons, neural networks, and activation functions. Now it’s time to understand the heart of every deep learning model: Forward Propagation.

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Deep Learning

Chapter 4: Activation Functions in Deep Learning – Complete Guide with Examples

Activation Functions Activation functions are one of the most important concepts in deep learning. Without activation functions, a neural network becomes nothing more than a simple linear equation—unable to learn.

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Deep Learning

Chapter 3: Perceptron and Multilayer Perceptron (MLP) – Complete Beginner Guide with Examples

Perceptron and Multilayer Perceptron (MLP) In the previous chapters, you learned about deep learning and the basics of neural networks. Now it’s time to study the most fundamental building block.

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