Complete Computer Vision Bootcamp With PyTorch & Tensorflow
Complete Computer Vision Bootcamp With PyTorch & Tensorflow Course by Krish Naik. Learn Computer Vision with CNN, TensorFlow, and PyTorch — Master Object Detection from Basics to Advanced
Complete Computer Vision Bootcamp Course Description
In this comprehensive Complete Computer Vision Bootcamp With PyTorch & Tensorflow course, you will master the fundamentals and advanced concepts of computer vision, focusing on Convolutional Neural Networks (CNN) and object detection models using TensorFlow and PyTorch. This course is designed to equip you with the skills required to build robust computer vision applications from scratch.
What You Will Learn
Throughout this course, you will gain expertise in:
- Introduction to Computer Vision
- Understanding image data and its structure.
- Exploring pixel values, channels, and color spaces.
- Learning about OpenCV for image manipulation and preprocessing.
- Deep Learning Fundamentals for Computer Vision
- Introduction to Neural Networks and Deep Learning concepts.
- Understanding backpropagation and gradient descent.
- Key concepts like activation functions, loss functions, and optimization techniques.
- Convolutional Neural Networks (CNN)
- Introduction to CNN architecture and its components.
- Understanding convolution layers, pooling layers, and fully connected layers.
- Implementing CNN models using TensorFlow and PyTorch.
- Data Augmentation and Preprocessing
- Techniques for improving model performance through data augmentation.
- Using libraries like imgaug, Albumentations, and TensorFlow Data Pipeline.
- Transfer Learning for Computer Vision
- Utilizing pre-trained models such as ResNet, VGG, and EfficientNet.
- Fine-tuning and optimizing transfer learning models.
- Object Detection Models
- Exploring object detection algorithms like:
- YOLO (You Only Look Once)
- Faster R-CNN
- Implementing these models with TensorFlow and PyTorch.
- Exploring object detection algorithms like:
- Image Segmentation Techniques
- Understanding semantic and instance segmentation.
- Implementing U-Net and Mask R-CNN models.
- Real-World Projects and Applications
- Building practical computer vision projects such as:
- Face detection and recognition system.
- Real-time object detection with webcam integration.
- Image classification pipelines with deployment.
- Building practical computer vision projects such as:
Who Should Enroll?
This course is ideal for:
- Beginners looking to start their computer vision journey.
- Data scientists and ML engineers wanting to expand their skill set.
- AI practitioners aiming to master object detection models.
- Researchers exploring computer vision techniques for academic projects.
- Professionals seeking practical experience in deploying CV models.
Prerequisites
Before enrolling, ensure you have:
- Basic knowledge of Python programming.
- Familiarity with fundamental machine learning concepts.
- Basic understanding of linear algebra and calculus.
Hands-on Learning with Real Projects
This course emphasizes practical learning through hands-on projects. Each module includes coding exercises, project implementations, and real-world examples to ensure you gain valuable skills.
By the end of this course, you will confidently build, train, and deploy computer vision models using TensorFlow and PyTorch. Whether you are a beginner or an experienced practitioner, this course will empower you with the expertise needed to excel in the field of computer vision.
Enroll now and take your computer vision skills to the next level!
