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This course is all about understanding how Neural Networks work. This course talks about the ways to create, label and annotate the image datasets. It starts by explaining the use of CNNs for performing image datasets and later teaches facial recognition with VGG Face. It describes concepts such as neutral style transfer, computer vision techniques and the use of pre-trained modules. This course aims to teach the use of OpenCV and describes in detail how TensorFlow’s Object Detection APIs are used.
Topics Covered:
- Computer Vision: Know what computer vision and deep learning are and learn how images are stored in computer data.
- Installation: Learn how to install TensorFlow and understand its entire framework in detail by setting up your deep learning virtual machine.
- Handwriting Recognition: Get started with handwriting recognition and get a demo on simple object classification.
- Neural Networks: Know what neural networks are and how they are classified. Also, get briefed about the machine learning process.
- CNNs: Learn in detail about CNNs and understand the convolutions, image features and feature maps.
- And Many More Topics..
Who Will Benefit?
- Data Scientists: All those who want to learn in detail about computer applications and deep learning.
- Software Engineers: Individuals who want to learn about machine learning processes and neural networks in detail.
- Students Of Computer Science: All those who want to learn how to install Tensorflow and work on multiple visual AI applications.
Why Choose This Course?
As you choose this course, you will learn about the ways to use Cloud GPUs on a Paper Surface. This course benefits you by teaching you important frameworks such as PyTorch, Caffe, MXNET and CV APIs. You will also get an overview of the computer vision world and thoroughly understand how neural networks work. By the end of this course, you will have hands-on experience related to popular CNNs such as AlexNet, LeNet and VGG.
