Ementorhub

What you'll learn

Understand the core components of deep learning models and their role in AI.

Apply CNN, R-CNN, and Faster R-CNN for object detection tasks.

Implement RNNs and LSTMs for sequential data processing.

Optimize and evaluate deep learning models for improved performance.

Skills you'll gain

Applied Machine Learning

Artificial Neural Networks

Performance Tuning

Network Architecture

Computer Vision

Supervised Learning

Artificial Intelligence and Machine Learning (AI/ML)

Deep Learning

Natural Language Processing

Machine Learning Methods

Image Analysis

There are 4 modules in this course

By the end of this course, you’ll be able to: - Describe the foundational components of deep learning models and their significance in artificial intelligence. - Illustrate the working of CNNs, R-CNNs, and Faster R-CNNs for object detection and related applications. - Understand the limitations of Perceptrons and how Multi-Layer Perceptrons (MLPs) address them. - Implement Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) architectures for sequential data analysis. - Optimize and evaluate deep learning models to achieve higher accuracy and efficiency. This course is designed for data scientists, machine learning engineers, and AI enthusiasts with a foundational knowledge of Python and machine learning who aim to expand their expertise in deep learning. Experience in building machine learning models, along with knowledge of statistics and proficiency in Python programming, is recommended for this course. Embark on this educational journey to enhance your expertise in deep learning and elevate your capabilities in building intelligent systems for the future of artificial intelligence.

Deep Learning with CNN, RCNN and Faster RCNN

Deep Learning with RNN, LSTM and Model Optimization

Course Wrap-Up and Assessment