Certificate in Neural Networks: Foundations & Frontiers

• Introduction to Neural Networks: Understanding the basics of artificial neural networks, including their structure, components, and history.
• Mathematical Foundations: Diving into the mathematical concepts that underpin neural networks, such as linear algebra, calculus, and probability theory.
• Activation Functions: Learning about the different types of activation functions and their impact on neural network performance.
• Backpropagation Algorithm: Understanding the backpropagation algorithm, including its formula, implementation, and optimization techniques.
• Convolutional Neural Networks: Exploring convolutional neural networks (CNNs), their architecture, and their applications in image recognition and computer vision.
• Recurrent Neural Networks: Delving into recurrent neural networks (RNNs), their structure, and their use in sequential data processing tasks such as language translation and speech recognition.
• Deep Learning Frameworks: Getting hands-on experience with popular deep learning frameworks such as TensorFlow, Keras, and PyTorch.
• Natural Language Processing: Learning about natural language processing (NLP) techniques and their application in neural networks, including word embeddings, LSTM, and transformers.
• Generative Models: Understanding the theory and practice of generative models, including generative adversarial networks (GANs), variational autoencoders (VAEs), and normalizing flows.
• Ethical Considerations: Examining the ethical implications of using neural networks and deep learning in various industries, including bias, privacy, and security.