Deep Learning CNN: Convolutional Neural Networks with Python

In this 15 hr course, you will explore the world of Convolutional Neural Networks (CNNs) and their applications in data science and beyond. Starting from the basics, the course delves into topics such as image processing, network architectures, and transfer learning, accompanied by hands-on Python coding and practical projects.

What I will be able to do after this course

• Understand foundational concepts of Convolutional Neural Networks and their application in data science.
• Master key neural network architectures and techniques, including gradient descent and transfer learning.
• Develop skills to implement deep learning models using Python and TensorFlow.
• Build robust applications using CNNs for tasks such as face verification and neural style transfer.
• Gain a comprehensive understanding of the evolution of CNNs from LeNet to modern architectures.

Course Instructor(s)

AI Sciences is a team of experienced educators and professionals specializing in deep learning and data science. They bring years of expertise in teaching vibrant, engaging courses aimed at simplifying complex topics. Their approach combines theoretical understanding with practical implementation to ensure a holistic learning experience.

Who is it for?

This course is for beginners and enthusiasts eager to learn Convolutional Neural Networks (CNNs). Learners should have interest in data science, willingness to practice Python coding, and aspire to build real-world CNN projects. No prior knowledge is required, making it ideal for anyone starting with deep learning or transitioning into the field.