Intro to Deep Learning

A comprehensive introduction to deep learning, covering fundamental concepts, neural network architectures, and practical applications using popular frameworks.

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Deep Learning Fundamentals and Neural Networks

Unit 1: Introduction to Deep Learning

Unit 2: Neural Network Fundamentals

Convolutional Neural Networks (CNNs) for Image Recognition

Unit 1: Understanding Convolutional Layers

Unit 2: Pooling Layers and Activation Functions

Unit 3: Building and Training CNNs

Unit 4: Applications and Advanced CNN Concepts

Recurrent Neural Networks (RNNs) for Sequential Data

Unit 1: Fundamentals of Recurrent Neural Networks

Unit 2: Addressing the Vanishing Gradient Problem with LSTMs and GRUs

Unit 3: Applying RNNs for Text Classification

Autoencoders, GANs, and Deep Reinforcement Learning

Unit 1: Introduction to Autoencoders

Unit 2: Variational Autoencoders (VAEs)

Unit 3: Generative Adversarial Networks (GANs)

Unit 4: Deep Reinforcement Learning (DRL)

Unit 5: Ethical Considerations and Trends