Certificate in Neural Networks Simplified for Beginners
-- ViewingNowThe Certificate in Neural Networks Simplified for Beginners is a crucial course for those interested in diving into the field of artificial intelligence. This certificate program focuses on simplifying complex neural network concepts, making it perfect for beginners with little to no prior experience in the subject.
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• Introduction to Neural Networks: Basics of neural networks, artificial intelligence, and machine learning. Understanding the structure and function of artificial neurons.
• Perceptron Algorithm: Learning about the perceptron model, its limitations, and the process of training a perceptron.
• Activation Functions: Explanation of activation functions and their role in neural networks, including sigmoid, tanh, and ReLU functions.
• Multi-Layer Neural Networks: Introduction to multilayer neural networks, including input, hidden, and output layers, and their role in solving complex problems.
• Backpropagation Algorithm: Understanding the backpropagation algorithm, including the chain rule, gradient descent, and optimization techniques.
• Training Neural Networks: Hands-on experience with training neural networks, including splitting data into training and testing sets, validation techniques, and overfitting.
• Convolutional Neural Networks (CNNs): Overview of convolutional neural networks, including convolutional layers, pooling layers, and fully connected layers.
• Recurrent Neural Networks (RNNs): Introduction to recurrent neural networks, including the concept of time series data, vanishing gradient problem, and LSTM cells.
• Deep Learning Frameworks: Hands-on experience with popular deep learning frameworks such as TensorFlow, Keras, PyTorch, or Theano.
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