Deep Learning for Computer Vision
Gain the skills and knowledge needed for deep learning for computer vision with Deep Learning for Computer Vision certification by NPTEL.
Online
12 Weeks
Free Quick Facts
| Medium Of Instructions | Mode Of Learning | Mode Of Delivery |
|---|---|---|
| English | Self Study | Video and Text Based |
Course Overview
The Deep Learning for Computer Vision certification course offers a comprehensive curriculum that covers the core aspects of deep learning for computer vision. The duration of this certification course is 12 weeks and completion of a basic course in Machine Learning course, and basic knowledge in probability, linear algebra, calculus, and Python are the prerequisites for this course.
The students will explore the traditional computer vision topics before they learn about the deep learning methods for computer vision. The Deep Learning for Computer Vision certification by NPTEL provides the basics as well as the latest advancement skills and knowledge in the field of computer vision and machine learning.
Also Read: Online Machine Learning Courses & Certifications
The Highlights
- 12 Weeks to Complete
- Certification from IIT, NPTEL
- For Senior UG/PG Students
- Completion Certification
Programme Offerings
- live sessions
- Hands-on Learning
- Books
- videos
- Transcripts
- assignments.
Courses and Certificate Fees
| Certificate Availability | Certificate Providing Authority |
|---|---|
| yes | IIT Madras (IITM) |
Eligibility Criteria
Academic Qualifications
Deep Learning for Computer Vision certification course requires certain prerequisites such as:
- Candidates must be Senior undergraduate students or post-graduate students.
- Students must have done a Basic course in Machine Learning.
- Deep Learning, or exposure to topics in Neural Networks (recommended, but not mandatory).
- Basic knowledge of probability, linear algebra, and calculus.
- Experience in Programming languages, preferably in Python.
What you will learn
After completing the Deep Learning for Computer Vision certification syllabus, the students will gain in-depth insights into Computer Vision and deep learning methods and techniques used by industry experts. They will also learn visual matching, Convolutional Neural Networks (CNNs), and CNNs for recognition, verification, detection, and segmentation.
Students will also gain proficiency in applying the methods and principles learned from the course in the real world. They will also learn the attention models, deep generative models, variants and applications of generative models in vision, and the recent trends as well after completing the Deep Learning for Computer Vision training.
Who it is for
The Deep Learning for Computer Vision online course is designed for aspiring students who are looking to enhance their skills and knowledge. This course can also be beneficial for:
Admission Details
To join the Deep Learning for Computer Vision classes, follow these steps:
Step 1: Click the link below:
https://nptel.ac.in/courses/106106224
Step 2: Login In by creating a separate account or Log In with Microsoft or Google Account.
Step 3: Submit the form by filling in the details mentioned in the form and joining the course.
The Syllabus
Introduction and Overview
- Course Overview and Motivation; History of Computer Vision; Image Representation; Linear Filtering, Correlation, Convolution; Image in Frequency Domain
- (Optional) Image Formation; Image Sampling
Visual Features and Representations
- Edge Detection; From Edges to Blobs and Corners; Scale Space, Image Pyramids and Filter Bank; SIFT and Variants; Other Feature Spaces
- (Optional) Image Segmentation, Human Visual System
- Deep Learning Basics Neural Networks: A Review Feedforward Neural Networks and Backpropagation Gradient Descent and Variants Regularization in Neural Networks Improving Training of Neural Networks Code Walkthroughs
- Convolutional Neural Networks for Image Classification Convolutional Neural Networks: An Introduction Backpropagation in CNNs CNN Architecture for Image Classification Code Walkthroughs
- Beyond Basic CNNs: Architectures, Finetuning and Visualization Evolution of CNN Architectures: VGG, Inception, ResNets ResNet Variants, MobileNet, EfficientNet Finetuning CNNs Visualizing CNNs Code Walkthroughs
- CNNs for Object Detection and Segmentation CNNs for Object Detection: Two-stage Models CNNs for Object Detection: Single-stage Models CNNs for Segmentation Code Walkthroughs
- Recurrent Neural Networks and their use in Vision Recurrent Neural Networks: Introduction Backpropagation in RNNs LSTMs and GRUs Video Understanding using CNNs and RNNs Code Walkthroughs
- Attention Models and Transformers Attention in Vision Models: An Introduction Soft and Hard Attention: Image Captioning Self-Attention and Transformers Code Walkthroughs
- Vision Transformers and Applications From Transformers to Vision Transformers Transformers for Detection Transformers for Segmentation Code Walkthroughs
- Deep Generative Models: GANs and VAEs Deep Generative Models: An Introduction Generative Adversarial Networks GAN Hacks and Improvements Variational Autoencoders and Disentanglement Code Walkthroughs
- Deep Generative Models: Diffusion Models Introduction to Diffusion Models: DDPMs Classifier and Classifier-Free Diffusion Guidance Text-conditioned Diffusion Models Under the Hood: Sampling, Prediction Space, Noise Schedules, Architectures Code Walkthroughs
Recent Trends
- Vision-Language Models and Recent Developments Self-Supervised Learning: SimCLR Contrastive Learning Vision-Language Models CLIP, BLIP, BLIP-2 Code Walkthroughs Course Conclusion Additional Material: Miscellaneous Advanced Topics (Additional, Optional) Applications and Case Studies Few-shot and Zero-shot Learning Adversarial Robustness Pruning and Model Compression Neural Architecture Search Recent Developments From VLMs to MM-LLMS: LLaVA, Video ChatGPT, ChatGPT-4V, Gemini 1.5 Dall-E-1,2,3 + Imagen
Evaluation process
Registering is necessary for students who want to take the examination to obtain the certifications from the IITs. Students must pass the examination with minimum marks to receive the completion certification.
Instructors
IIT Madras (IITM) Frequently Asked Questions (FAQ's)
The candidates must be Senior undergraduate students or post-graduate students who have done their basic course in Machine Learning and have basic knowledge of probability, linear algebra, calculus, and Python.
The certification course will take 12 weeks to complete. The candidates receive all the required training and study materials online and also get the opportunity to access the live sessions.
The candidates are required to pay the examination fee that is Rs 1000/- to appear for the examination and qualify with minimum marks, at least 10 out of 25 percent in assignments and 30 out of 75 percent in the final proctored exam.
This certification course learning applies to all the companies that are using computer vision for their products or services such as Microsoft, Google, Facebook, Apple, TCS, Cognizant, L&T, and others.
Yes, the certification course supports the students with industry expert training and as well as provides them the assignments, transcripts, books, and videos on the subject.