The Computer Engineering Department organized a Five-Day Workshop on “Deep Learning and Large Language Models†from 05 January to 09 January 2026 with the objective of equipping students and faculty members with in-depth knowledge and practical skills in emerging Artificial Intelligence technologies. The workshop was conducted by experts from Rashil Tech Labs, with sessions led by Mr. Sukhesh Kothari, a seasoned professional known for his expertise in Deep Learning and AI-driven solutions.
The program was designed to provide both foundational understanding and advanced exposure to Deep Learning concepts, with a strong emphasis on real-time implementation using Python. It began with an introduction to the broader domains of Artificial Intelligence, Machine Learning, and Deep Learning, helping participants understand the evolution, scope, and industry relevance of intelligent systems. Core theoretical concepts were explained in a simplified manner, making them accessible to learners with varying levels of prior experience.
As the workshop progressed, participants were introduced to various Deep Learning architectures and computational models. Detailed sessions were conducted on neural networks, layers, activation functions, optimization techniques, and model training processes. Special focus was placed on practical exposure through coding exercises using widely adopted Python libraries and frameworks such as TensorFlow and Keras. This hands-on methodology allowed participants to build, train, and evaluate their own Deep Learning models.
The workshop also explored advanced and industry-relevant applications including Generative Adversarial Networks (GANs) and Transfer Learning. These sessions demonstrated how modern AI systems generate content, enhance image processing, and accelerate model performance using pre-trained networks. Real-world case studies helped bridge the gap between academic theory and industrial implementation.
Mr. Sukhesh Kothari adopted an interactive teaching approach that included expert lectures, live demonstrations, coding practices, and doubt-solving discussions. Participants actively engaged in practical tasks, enabling them to directly apply their learning in simulated project environments.
Overall, the workshop proved to be highly informative, interactive, and skill-oriented. It enhanced participants’ technical competence in Deep Learning programming, provided exposure to current IT industry practices, and fostered meaningful interaction between academia and industry experts, preparing learners for future opportunities in AI and advanced computing domains.
