Deep Learning Fundamentals

Deep Learning refers to the ability of an "artificial agent" to learn by trial-and-error and rewards-and-punishments. An artificial agent aims to emulate a human's ability to obtain and construct knowledge on its own, directly from raw inputs such as vision. To realize reinforcement learning, deep learning and neural networks are used. Reinforcement learning is different from machine learning and does not rely on supervised and unsupervised learning approaches.

The Machine Learning and Deep Learning course provides an in-depth understanding of neural networks, TensorFlow, and AI model deployment. Participants will explore convolutional neural networks (CNNs) for image recognition and recurrent neural networks (RNNs) for NLP applications. Learn how to optimize AI models, tune hyperparameters, and deploy machine learning solutions using TensorFlow and Keras. This course is designed for software engineers, data scientists, and statisticians looking to advance their careers in AI. Join us to gain practical experience in Machine Learning and Deep Learning and master cutting-edge AI technologies.