The Department of Electronics and Communication Engineering, in association with the Placement Department, organised a Six-Day Workshop on “Deep Learning using TensorFlow and Network Security” for the 5th Semester students from 21/09/2026 to 26/09/2026. Around 30 students attended the Deep Learning using TensorFlow training, while the remaining students participated in the Network Security training. The objective of the programme was to provide students with practical exposure to emerging technologies, strengthen their technical and problem-solving skills, and familiarise them with the application of deep learning and cybersecurity concepts in real-world engineering scenarios.

The Deep Learning using TensorFlow training covered the fundamentals of Deep Learning, the evolution of AI, ML and DL, neural networks, TensorFlow and Keras architecture, tensors and operations, and computational graphs. The syllabus further included Artificial Neural Networks, forward and backward propagation, activation functions, loss functions, optimisers, model evaluation, overfitting and regularisation. Students were also introduced to Convolutional Neural Networks, convolution, pooling, data augmentation and transfer learning, followed by Recurrent Neural Networks, LSTM, GRU, tokenisation, embeddings, text classification and sentiment analysis. The programme also covered hyperparameter tuning, model checkpointing, saving and loading models, and basic deployment using TensorFlow Lite and Flask, Deep Learning using TensorFlow

The Network Security training covered Defence in Depth, Operating System Security, Network Device Security and Secure Computing. The syllabus included security principles such as confidentiality, integrity, availability, threats, risks and vulnerabilities, physical security, security policies and common cyberattacks including malware, ransomware, DoS, XSS, SQL injection, brute-force attacks, MITM attacks and social engineering. It also covered system hardening, authentication, permissions, auditing, encryption, VPNs, wireless security, firewalls, IDS/IPS, SIEM, network isolation techniques, secure protocols, DNSSEC, network sniffing, email protection, phishing, browser security and anti-malware practices.

The programme enhanced the students’ understanding of deep learning, neural network development, model training, evaluation and deployment, while also developing their awareness of network security threats, vulnerabilities, authentication, encryption and security mechanisms. The training provided valuable hands-on exposure to TensorFlow and Keras and strengthened the students’ ability to apply deep learning techniques to image and text-based datasets. Deep Learning using TensorFlow. Overall, the workshop improved the students’ technical competence, practical skills, cybersecurity awareness and readiness to apply emerging technologies in real-world engineering applications.