Deep Learning CNN: Convolutional Neural Networks with Python

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-Neur…

Type
video
Category
Data
publication date
2022-08-24
what you will learn

Understand the importance of CNNs in data science
Explore the reasons to shift from classical computer vision to CNNs
Learn concepts from the beginning with comprehensive unfolding with examples in Python
Study the evolutions of CNNs from LeNet (1990s) to MobileNets (2020s)
Deep-dive into CNNs with examples of training CNNs from scratch
Build your own applications for human face verification and neural style transfer

duration
926
key features
Learn from easy-to-understand, exhaustive, expressive, 75+ videos along with detailed code notebooks * Structured course with solid basic understanding and moving ahead with the advanced practical concepts * Practical explanation and live coding with Python to build your own application
approach
This course is comprehensive, easy to understand, and designed for beginners. However, it goes deep gradually over time. This course is a quick compilation of all the basics, and it encourages you to press forward and experience more than what you have learned. By the end of every module, you will work on the assigned activities, which will evaluate your learning based on the previous concepts and methods. Several of these activities will be coding-based to get you up and running with implementations.
audience
This course is designed for beginners in data science and deep learning. Any individual who wants to learn CNNs with real datasets in data science, learn CNNs along with its implementation in realistic projects, and master their data speak will gain a lot from this course.

No prior knowledge is needed. You start from the basics and slowly build your knowledge of the subject. A willingness to learn and practice is just the prerequisite for this course.
meta description
Learn Convolution Neural Networks using TensorFlow, CNN for Image Recognition, and CNN for Object Detection. Understand the concepts and methodologies of CNNs with respect to data science with live coding throughout.
short description
The course is crafted to reflect the in-demand skills in the marketplace that will help you in mastering the concepts and methodology of CNNs in data science with regard to Python.

The course helps you apply state-of-the-art CNNs that are much more recent, advanced in terms of accuracy and efficiency, and can be used for transfer learning on your own dataset.
subtitle
A comprehensive, hands-on, and easy-to-understand course on Convolution Neural Networks with Python
keywords
Python, CNNs, data science, DNN, YOLO, face recognition, neural style transfer, convolution neural network, deep neural network, TensorFlow, LeNet, MobileNets, classical computer vision, pre-trained CNNs
Product ISBN
9781803243726