Are you passionate about computer vision and eager to gain practical, hands-on experience in the field? Look no further! Our bootcamp program offers an immersive and interactive learning internship-like environment, empowering you to build a strong foundation in computer vision and qualify for exciting opportunities in the AI industry.
What sets our bootcamp apart is our focus on practicality and hands-on interaction with the computer vision ecosystem. We understand the importance of real-world experience, and we provide you with ample opportunities to work on challenging projects, engage in practical labs, and develop a comprehensive computer vision portfolio.
Throughout the bootcamp , you’ll have access to a diverse range of sessions, carefully designed to enhance your knowledge and skills. Our expert mentors will guide you through the intricacies of computer vision, teaching you the core concepts, algorithms, and methodologies used to build computer vision applications. You’ll gain a deep understanding of image processing, object detection, recognition, and tracking, as well as image and video analysis techniques.
To ensure you stay up to date with the latest advancements in the field, we prioritize exposing you to cutting-edge technologies and industry trends. We believe that staying ahead of the curve is crucial for success in the AI industry, and our internship equips you with the knowledge and skills to do just that.
But it doesn’t stop there. We believe that true learning happens when you apply theoretical knowledge to practical projects. Our internship program offers numerous opportunities to work on real-world computer vision applications. From building intrusion detection systems and developing facial recognition systems to creating immersive experiences, you’ll be immersed in hands-on projects that allow you to apply your skills and showcase your expertise.
By the end of our internship program, you’ll have a robust computer vision portfolio that demonstrates your ability to solve complex problems and develop innovative solutions. This portfolio will set you apart from the competition when seeking career opportunities in the AI industry.
Qualified students after the bootcamp will get an opportunity to join DevisionX team for a longer period as interns, contributing to one of our internal projects and platforms.
Don’t miss this chance to gain invaluable experience in computer vision and jumpstart your career. Join our internship program and embark on an exciting journey towards becoming a skilled computer vision professional. Apply now and unlock your potential in the ever-evolving field of AI.
Course Management
All sessions will be live online through Google Meet and will be recorded.
Fall Round:
- Arabic round will start on 3rd of November
- English round will start on 18th of November
Bootcamp total duration is 60 hours divided into:
- 10 sessions across 5 weeks (2 sessions per week on Friday & Saturday)
- Assignments
- Final Project
Bootcamp Extras :
- You will be added to Bootcamp Discord Community to connect with other campers from the current and previous rounds.
- You will receive a certificate after finishing the course and project delivery.
- There is an opportunity to get an internship at DevisionX after the course according to your evaluation.
Fees:
- 6000 EGP for Arabic Bootcamp
- 199 USD for English Bootcamp
Payment :
You will receive the payment link after registration via email so you can pay by any bank card or Fawry also there are installment options with Valu and NBE credit cards.
Also you can pay directly after registration here :
Course outline
Image processing Techniques
- Introduction
- Image Formation and Acquisition
- Intensity Transformations
- Spatial Filtering
- Image Segmentation Binary Morphology
- Blob Analysis & Edge Detection
- Local Invariant Features
- Object Detection
Neural Networks
- Model of a biological neuron
- Universal Approximation Theorem
- Activation functions
- Neural net architecture & representational power
- Setting up the Data and the Loss preprocessing
- Neural Network Hyperparameters : Weight initialization, batch normalization, regularization, loss functions, Learning and Evaluation, gradient checks, sanity checks, babysitting the learning process, momentum, second-order methods, Adagrad/RMSprop, hyperparameter optimization.
- Optimization : Stochastic Gradient Descent, optimization landscapes, learning rate, analytic/numerical gradient, Backpropagation, chain rule interpretation, real-valued circuits, patterns in gradient flow.
Neural Networks for Computer Vision
- Image Classification: Data-driven Approach, train/val/test splits, L1/L2 distances, hyperparameter search, cross-validation, Convolutional Neural Network, Transfer Learning.
- Image Object Detection & Segmentation : Traditional OD Algorithms, CNNs for object detection, SSD Models, R-CNN Models, DarkNet YOLO Models, Modern YOLO Models, Few-Shot & Zero-Shot Detection
- Vision Transformers : Attention Mechanism, SEER Model, Vision Transformers (ViT), Big Transfer (BiT), Applying Transformers to Image Data, Foundation Models
Deep Learning Systems in Production Software Systems
- Review on Deep Learning High Level Concepts
- History of Software Systems
- Introduction to Agile SW & DevOps
- Problems with Deep Learning Systems
MLOps Fundamentals
- Basics of MLOps-Enabled Systems
- MLOps vs DevOps
- Problems that MLOps solves
- MLOps Components & Tools
- MLOps Stages & Architecture
Designing MLOps Enabled Systems
- Designing your project
- Model & Dataset Versioning
- Model Monitoring in Training
- Model Monitoring in Production
- Model Deployment
Introduction to Game Theory & GANs Basics
- Basic Game Theory
- Deriving Loss Functions.
- Nash Equilibrium & Game Optimality
- Why do GAN models work?
- Problems with training GANs
- Build Basic Generative Adversarial Networks for tabular data.
- Building Basic GANs for Computer Vision
Building Advanced GANs
- Building StyleGAN
- Building CycleGAN
- Text-2-image GAN
Practical Project Lab
Students will build demo computer vision projects, after being divided into groups, and present them on the final day.
