Certificate in Neural Networks & Deep Learning Fundamentals
-- ViewingNowThe Certificate in Neural Networks & Deep Learning Fundamentals is a comprehensive course designed to provide learners with a solid understanding of artificial neural networks and deep learning principles. This course is essential in today's tech-driven world, where neural networks and deep learning are at the forefront of many innovative solutions.
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⢠Introduction to Neural Networks: Understanding of basic concepts, architecture, and components of neural networks.
⢠Perceptron & Multilayer Perceptron (MLP): Studying perceptron as a single-layer artificial neural network and MLP as a feedforward neural network.
⢠Deep Learning Fundamentals: Exploring the principles and concepts of deep learning, including backpropagation and gradient descent algorithms.
⢠Convolutional Neural Networks (CNN): Learning about CNN architecture, its components, and applications in image and video processing.
⢠Recurrent Neural Networks (RNN): Understanding RNNs, including LSTM and GRU, and their applications in sequence data processing and natural language processing.
⢠Deep Learning Frameworks: Hands-on experience with popular deep learning frameworks, such as TensorFlow, Keras, and PyTorch.
⢠Applications of Neural Networks: Applying neural networks and deep learning techniques to real-world problems, such as image recognition, speech recognition, and natural language processing.
⢠Evaluation Metrics for Neural Networks: Learning about evaluation metrics, such as accuracy, precision, recall, and F1 score, for assessing the performance of neural networks.
⢠Hyperparameter Tuning for Deep Learning: Understanding the impact of hyperparameters, such as learning rate, batch size, and number of layers, on the performance of deep learning models.
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