cover a wide range of topics, starting from the fundamentals of neural networks to advanced techniques used in deep learning architectures. Participants are introduced to the key concepts of artificial neural networks, including neurons, activation functions, and forward/backward propagation. Understanding the core principles allows candidates to grasp how neural networks process and learn from data.
As the certification progresses, participants dive hot dataset into the different types of neural networks, such as convolutional neural networks (CNNs) for image processing and recognition, recurrent neural networks (RNNs) for sequential data analysis, and transformer networks for natural language processing tasks. In-depth knowledge of these architectures equips learners to choose the right model for specific AI challenges.