Global Certificate in Neural Networks Fundamentals
-- ViewingNowThe Global Certificate in Neural Networks Fundamentals is a comprehensive course designed to provide learners with a solid understanding of neural networks and their applications. This certification is crucial in today's technology-driven world, where neural networks are at the forefront of artificial intelligence and machine learning innovations.
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⢠Introduction to Neural Networks: Basics of artificial neural networks, their structure and functionality. Understanding of neuron models and learning rules.
⢠Perceptron Learning Algorithm: Detailed explanation of perceptron model and learning algorithm. Exercises on linear separability and perceptron limitations.
⢠Multi-Layer Perceptrons (MLP): Introduction to MLPs, their architecture and learning algorithms, including backpropagation. Hands-on experience with MLP design and training.
⢠Convolutional Neural Networks (CNN): Understanding of CNNs, their layers (convolutional, pooling, and fully connected layers) and applications in image processing and computer vision.
⢠Recurrent Neural Networks (RNN): Overview of RNNs, architectures, and training methods. Hands-on exercises on sequence data processing, speech recognition, and natural language processing.
⢠Autoencoders and Restricted Boltzmann Machines (RBM): Introduction to autoencoders, RBMs, and deep belief networks (DBNs). Exploration of unsupervised learning and feature extraction techniques.
⢠Optimization Techniques in Neural Networks: Study of optimization algorithms, including stochastic gradient descent, momentum, and adaptive learning rate methods. Hands-on exercises on optimization techniques for faster and better learning.
⢠Regularization Techniques in Neural Networks: Understanding of regularization methods, such as L1, L2, dropout, and early stopping. Practical exercises on applying regularization in deep learning models.
⢠Evaluation Metrics and Hyperparameter Tuning: Review of evaluation metrics for classification, regression, and clustering tasks. Exercises on hyperparameter optimization, grid search, and random search.
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