Convolutional Neural Networks: Deep Learning

About this course

In this course, you'll be learning the fundamentals of deep neural networks and CNN in depth.

This course offers an extensive exploration of deep neural networks with a focus on Convolutional Neural Networks (CNNs).

The course begins by delving into the fundamental concepts to provide a strong foundation for learners.

Initial sections of the course include:

• Understanding what deep learning is and its significance in modern machine learning.
• Exploring the intricacies of neural networks, the building blocks of deep learning.
• Discovering where CNNs fit into the larger landscape of machine learning techniques.
• In-depth examination of the fundamentals of Perceptron Networks.
• Comprehensive exploration of Multilayer Perceptrons (MLPs).
• A detailed look into the mathematics behind feed forward networks.
• Understanding the significance of activation functions in neural networks.

A major portion of the course is dedicated to Convolutional Neural Networks (CNNs):

• Exploring the architecture of CNNs.
• Investigating their applications, especially in image processing and computer vision.
• Understanding convolutional layers that extract relevant features from input data.
• Delving into pooling layers, which reduce spatial dimensions while retaining essential information.
• Examining fully connected layers for making predictions and decisions.
• Learning about design choices and hyperparameters influencing CNN performance.

The course also covers training and optimization of CNNs:

• Understanding loss functions and their role in training.
• Grasping the concept of backpropagation.
• Learning techniques to prevent overfitting.
• Introduction to optimization algorithms for fine-tuning C

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