Deep Learning Essentials

• •Deep dive into neural network architectures
• •Convolutional Neural Networks (CNNs) for computer vision
• •Recurrent Neural Networks (RNNs) for sequence modeling
• •Transformer models and attention mechanisms
• •Deep learning frameworks and tools
• •Optimization techniques and hyperparameter tuning

Instructor

Dr. Neetu Sharma

Co-founder

Ph.D in Computer Science with extensive expertise in deep learning research and implementation. Has led numerous successful deep learning projects in industry and academia.

Prerequisites

• Understanding of basic machine learning concepts
• Proficiency in Python programming
• Knowledge of linear algebra and calculus fundamentals
• Familiarity with data preprocessing techniques
• Prior experience with a deep learning or ML framework is helpful

What You'll Learn

• Design and implement advanced neural network architectures
• Apply convolutional networks to solve computer vision problems
• Build sequence models using RNNs and transformer architectures
• Develop generative models for creative AI applications
• Optimize and deploy deep learning models in production
• Select appropriate architectures for different ML problems