Global Certificate in Neural Networks and Recommender Systems
-- ViewingNowThe Global Certificate in Neural Networks and Recommender Systems is a comprehensive course designed to empower learners with essential skills in artificial intelligence (AI). This program emphasizes the importance of neural networks, a crucial component of AI, and recommender systems, which are widely used in personalized services and marketing.
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⢠Fundamentals of Neural Networks: Introduction to neural networks, artificial neurons, network architectures, learning algorithms, and backpropagation.
⢠Deep Learning: Overview of deep learning, deep neural networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory (LSTM) networks.
⢠Recommender Systems: Basics of recommender systems, types of recommenders, collaborative filtering, content-based filtering, and hybrid filtering.
⢠Deep Learning in Recommender Systems: Deep learning techniques for recommender systems, such as neural collaborative filtering, deep neural networks for click-through rate prediction, and representation learning in recommender systems.
⢠Evaluation Metrics: Evaluation metrics for neural networks and recommender systems, including accuracy, precision, recall, F1-score, mean absolute error, root mean squared error, and area under the ROC curve.
⢠Optimization Techniques: Optimization techniques for training neural networks, including stochastic gradient descent (SGD), mini-batch gradient descent, momentum, adaptive learning rates, and second-order optimization methods.
⢠Regularization Techniques: Regularization techniques for preventing overfitting in neural networks, such as L1 regularization, L2 regularization, dropout, early stopping, and data augmentation.
⢠Special Topics in Neural Networks: Special topics in neural networks, such as transfer learning, meta-learning, reinforcement learning, attention mechanisms, and transformers.
⢠Special Topics in Recommender Systems: Special topics in recommender systems, such as context-aware recommenders, knowledge-based recommenders, social recommenders, trust-based recommenders, and fairness and explainability in recommender systems.
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