What you'll learn

Understand how Deep Neural Networks work, practically and mathematically

Understand Forward- and Backpropagation processes, mathematically and practically

Design and implement a Deep Neural Network for multi-class classification

Understand and implement the building blocks of Convolutional Neural Networks

Understand and Implement cutting-edge Optimization, Regularization and Initialization techniques

Train and validate a Convolutional Model on widely used datasets like MNIST and CIFAR-10

Understand and implement Transfer Learning

Use a Convolutional Model to create a Real-Time, Multi-Object Detection System

Requirements

No prior knowledge is required

Description

This course is for anyone willing to really understand how Convolutional Neural Networks (CNNs) work. Every component of CNNs is first presented and explained mathematically, and the implemented in Python.Interactive programming exercises, executable within the course webpage, allow to gradually build a complete Object-Detection Framework based on an optimized Convolutional Neural Network model. No prior knowledge is required: the dedicated sections about Python Programming Basics and Calculus for Deep Learning provide the necessary knowledge to follow the course and implement Convolutional Neural Networks.In this course, students will be introduced to one of the latest and most successful algorithms for real-time multiple object detection. Throughout the course, they will gain a comprehensive understanding of the Backpropagation process, both from a mathematical and programming perspective, allowing them to build a strong foundation in this essential aspect of neural network training.By the course's conclusion, students will have hands-on experience implementing a sophisticated convolutional neural network framework. This framework will incorporate cutting-edge optimization and regularization techniques, enabling them to tackle complex real-world object detection tasks effectively and achieve impressive performance results. This practical knowledge will empower students to advance their capabilities in the exciting field of Computer Vision and Deep Learning.