Location

Technical and Vocational Institute

Objectives

• ● - Teach the fundamentals of Deep Learning and its techniques.● - Provide an understanding of Artificial Neural Networks, Activation Functions, Optimizers, and Cost Functions.● - Introduce Image Processing concepts and their relation to Deep Learning.● - Explain modern Deep Learning techniques used in Computer Vision, including CNN, R-CNN, YOLO, VIT, and Swin.● - Focus on practical applications and provide hands-on exercises.● - Help students apply the taught concepts and techniques in real-world projects.● - Instruct students on the use of the latest tools and frameworks in Deep Learning and Computer Vision.● - Prepare students to pursue a career in Deep Learning and Computer Vision.

Outcomes

● Understanding the fundamentals of Deep Learning and its applications in Computer Vision.● Ability to use modern tools and frameworks for developing Deep Learning models.● Ability to analyze and process images using Deep Learning techniques.● Ability to design and develop Artificial Neural Networks to solve Computer Vision problems.● Ability to apply Deep Learning techniques in solving real-world Computer Vision problems.● Ability to evaluate and improve existing models and increase their accuracy.● Ability to work individually or as a team on Computer Vision projects.● Acquiring the necessary skills to work in the fields of Computer Vision and Deep Learning.● Preparedness to continue studies in the fields of Computer Vision and Deep Learning at a higher level.

Course Contents

Day 1 : ● Neural Networks Foundation ● a. Artificial Neural Networks Day 2 : ● b. Activation Functions 1. Linear 2. Sigmoid 3. Relu 4. Softmax● Optimizers 1. Stochastic Gradient Descent 2. GD with Momentum 3. Nesterov Accelerated Gradient Descent 4. Adagrad 5. Adadelta 6. Rms Prop 7. AdamDay 3 : ● Cost Functions 1. Mean Squared Loss 2. Cross Entropy Loss● Image Processing Foundation 1. Image Arithmetics 2. Image InterpolationDay 4 : ● Geometric Transformation ● Image Smoothing. ● Image Sharpening Day 5 : ● Convolutional Neural Networks (CNN) ● Convolutional Neural Networks ِApplication Day 6 : ● Region-based Convolutional Neural Networks(R-CNN) ● You Only Look Once (YOLO) Day 7 : ● Vision Transformer (VIT) ● Swin Transformer (Swin)