Computer Visioning Masterclass
Virtual Masterclass, 20th November,
Sunday (9:00AM - 1:00PM) |
Fee: PKR 5,000 (early bird 20% off till 13th Nov)
For Students: PKR 3,500
What you'll learn
About this Course
1. Introduction to Deep Learning – Begin by learning the fundamentals of deep learning. Then examine the foundational algorithms underpinning modern deep learning: gradient descent and backpropagation. Once those foundations are established, explore design constructs of neural networks and the impact of these design decisions. Finally, the course explores how neural network training can be optimized for accuracy and robustness.
2. Convolution Neural Network – This part introduces convolutional neural networks, the most widely used type of neural networks specialized in image processing. You will learn the main characteristics of CNNs that make them better than standard neural networks for image processing. Then you’ll examine the inner workings of CNNs and apply the architectures to custom datasets using transfer learning. Finally, you will learn how to use CNNs for object detection and Image classification.
3. Building Generative Adversarial Networks – Become familiar with generative adversarial networks (GANs) by learning how to build and train different GANs architectures to generate new images. Discover, build, and train architectures such as DCGAN, CycleGAN, ProGAN, and StyleGAN on diverse datasets including the MNIST dataset, Summer2Winter Yosemite dataset, or CelebA dataset.
Prerequisites of course:
Knows programming in python to some extent, and understands basic Computer Science Concepts.
Machine Learning and Computer Vision Leader, specialized in deep learning and active learning with domain expertise’s in Climate Tech, Facial Identity, Medical Imaging and self-driving vehicles.
Experienced in delivering computer vision products and APIs for use cases including image recognition and classification, object detection and segmentation in 2D and 3D images/videos – application ranging from climate, satellite, medical, facial identity, self-driving vehicles, retail and fashion.
Over a decade in Machine Learning and Computer Vision education and experience, applying and training others to apply deep learning algorithms (CNN, RCNN, LSTM, Transformers), image processing methods, feature extraction techniques, pattern recognition, feature selection/regularization, boosting methodologies, generalized regression models, data visualization, analytic approximation theory, enumerative and graphical combinatorics, advanced implementations of operator algebras, probability theory, and statistics.