Sri Kavya Doctoral Researcher
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As an experienced tutor and current PhD candidate specializing in Deep Learning, I bring a wealth of knowledge and passion for advancing the forefront of artificial intelligence. With three years of teaching experience, I offer tailored tutoring sessions that cater to individual learning styles and goals. My expertise lies in developing personalized learning paths, emphasizing hands-on practical experience, and fostering interactive and engaging instruction. I am committed to providing continuous support and mentorship to empower students to excel in the dynamic field of deep learning, igniting curiosity and passion along the way. Join me in exploring the limitless possibilities of artificial intelligence and unlocking the potential of deep learning.

Subjects

Python Beginner-Expert
Machine Learning Beginner-Expert
Image Processing Beginner-Expert
Deep Learning Beginner-Expert
Computer Vision Beginner-Expert

Experience

No experience mentioned.

Education

Doctor of Philosophy (Feb, 2024–now) from Queensland University of Technology
Master of Technology (Computer Science and Engineering) (Aug, 2018–May, 2020) from Koneru Lakshmaiah Education Foundation
Bachelor of Technology (Computer Science and Engineering) (Sep, 2014–Apr, 2018) from Prasad V Potluri Siddhartha Institute of Technology

Fee details

AU$10/hour (US$6.37/hour)

Courses offered

Deep Learning Fundamentals
- AU$300
- Duration: 30 days
- Delivery mode: Online
- Group size: Individual
- Instruction language: English, Telugu
- Certificate provided: No
Deep Learning Fundamentals is an advanced course designed to provide students with a comprehensive understanding of deep learning techniques and their applications. Throughout this course, students will explore the theoretical foundations and practical implementations of deep neural networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and other state-of-the-art architectures.

Key topics covered in this course include:

1. Introduction to neural networks and deep learning
2. Fundamentals of gradient descent and backpropagation
3. Convolutional neural networks for image recognition and classification
4. Recurrent neural networks for sequence modeling and natural language processing
5. Generative adversarial networks (GANs) for image generation and synthesis
6. Transfer learning and fine-tuning pretrained models
7. Optimization techniques and regularization methods for deep learning models
8. Practical applications of deep learning in computer vision, speech recognition, and autonomous systems