Convolutional Neural Networks (CNNs) are considered game-changers in the field of computer vision, particularly after AlexNet in 2012. They are everywhere now, ranging from audio processing to more advanced reinforcement learning. So, the understanding of CNNs becomes almost inevitable in all fields of data science. With this course, you can take your career to the next level with an expert grip on the concepts and implementations of CNNs in data science.

The course starts with introducing and jotting down the importance of Convolutional Neural Networks (CNNs) in data science. You will then look at some classical computer vision techniques such as image processing and object detection. It will be followed by deep neural networks with topics such as perceptron and multi-layered perceptron. Then, you will move ahead with learning in-depth about CNNs. You will first look at the architecture of a CNN, then gradient descent in CNN, get introduced to TensorFlow, classical CNNs, transfer learning, and a case study with YOLO. Finally, you will work on two projects: Neural Style Transfer (using TensorFlow-hub) and Face Verification (using VGGFace2).

By the end of this course, you will have understood the methodology of CNNs with data science using real datasets. Apart from this, you will easily be able to relate the concepts and theories in computer vision with CNNs. All the resource files are added to the GitHub repository at: https://github.com/PacktPublishing/Deep-Learning-CNN-Convolutional-Neural-Networks-with-Python