Introduction to Deep Learning

Course Summary

This course provides a comprehensive introduction to deep learning, covering neural network architectures, model training techniques, and real-world applications. Participants will learn how deep learning models work, the role of activation functions, optimization techniques, and how to build neural networks using TensorFlow and PyTorch. The course will explore practical applications in image recognition, natural language processing (NLP), and time-series forecasting.

Key Learning Outcomes

Understand deep learning fundamentals and how neural networks function.
Learn to build feedforward, convolutional (CNN), and recurrent neural networks (RNN).
Implement backpropagation and optimization techniques for model training.
Explore deep learning applications in computer vision, NLP, and predictive analytics.
Gain hands-on experience using TensorFlow and PyTorch.

Target Audience

Data scientists, AI practitioners, engineers, and researchers interested in deep learning applications.

Prerequisites

Basic understanding of Python and machine learning concepts.

Familiarity with linear algebra, probability, and calculus is beneficial but not required.

Course Duration & Format

5 days (Online) – Includes hands-on coding, theoretical concepts, and real-world case studies.

Instructor(s)

Deep learning experts, AI specialists, and data scientists.

Course Fee & Registration