Deep Learning

Why should you attend?

This comprehensive course offers a deep dive into the fascinating world of deep learning, covering everything from the foundational concepts of neural networks to advanced applications in various domains. Starting with an exploration of neurons and their significance in neuroscience, the course lays the groundwork for understanding how neural networks mimic these biological processes to solve complex problems. Participants will learn to create neural networks using Python, gaining hands-on experience with actual coding and implementation. As the course progresses, it delves into specific types of neural networks, including Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN), each section building on the last to provide a thorough understanding of how these networks function, learn, and are constructed. Practical sessions on building ANNs, CNNs, and RNNs using real-world datasets like bank customer churn, image classification, and stock prediction datasets enable participants to apply theoretical knowledge in practical scenarios. Moreover, the course covers essential techniques for evaluating and improving the performance of neural networks through tuning and hyperparameter adjustments. It also explores cutting-edge topics like transfer learning and model deployment, preparing learners to tackle current challenges in the field. The final modules focus on real-world applications of deep learning across various sectors, ensuring that participants understand the vast potential of these technologies.