Deep Learning

Deep Learning is designed to provide you with a comprehensive understanding of deep learning. Throughout this course, you will delve into various deep learning models, learn about neural network topologiesYou will gain hands-on experience with TensorFlow and its different data types, and learn how to use frameworks such as Keras for building and training models. The course covers essential deep learning concepts including convolutional and recurrent neural networks, autoencoders, and data augmentation techniques, culminating in a hands-on project to solidify your learning.

This course covers crucial topics, including the principles and applications of deep learning, the structure and functioning of neural networks, and practical implementation using TensorFlow and Keras. You will also learn how to work with datasets such as MNIST, and apply advanced techniques like data augmentation to enhance model performance.

What will students learn in your course?

>Introduction to TensorFlow: Understand the basics of TensorFlow and its significance in the deep learning landscape.

>Deep Learning Frameworks: Understand the various frameworks available for deep learning and their unique features.

>Neural Networks: Delve into the structure and functioning of neural networks, including activation functions and optimization techniques.

>Data Augmentation: Implement data augmentation techniques to improve model robustness and performance.

>Hands-On Project: Apply the knowledge gained throughout the course to a comprehensive hands-on project, showcasing your skills in deep learning.