Deep Learning

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.

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,.

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.

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.

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.

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.

Neural Networks Basics Neural Networks are the foundation of Deep Learning. They are inspired by the structure and functioning of the human brain. Just like our brain has billions of.

Introduction to Deep Learning Deep Learning is one of the most powerful fields in modern computer science. It gives computers the ability to learn from experience, understand patterns, and make.