Certificate in Deep Learning for QA: Core Principles
-- ViewingNowThe Certificate in Deep Learning for QA: Core Principles is a comprehensive course that equips learners with essential skills for career advancement in the rapidly evolving field of deep learning and quality assurance. This program covers the core principles of deep learning, neural networks, and their applications in QA.
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⢠Introduction to Deep Learning: Understanding the basics of deep learning, its applications, and the differences between traditional machine learning and deep learning algorithms.
⢠Neural Networks: Learning about artificial neural networks, including perceptrons, multilayer perceptrons, and backpropagation.
⢠Convolutional Neural Networks (CNNs): Diving into the structure, components, and use cases of CNNs, focusing on image recognition and classification.
⢠Recurrent Neural Networks (RNNs): Exploring RNNs and their ability to handle sequential data, such as time series, natural language processing, and speech recognition.
⢠Deep Learning Frameworks: Getting hands-on experience with popular deep learning frameworks like TensorFlow, Keras, PyTorch, and Theano.
⢠Hyperparameter Tuning: Optimizing deep learning models by fine-tuning hyperparameters, such as learning rates, batch sizes, and regularization techniques.
⢠Transfer Learning and Fine-Tuning: Leveraging pre-trained models for transfer learning and fine-tuning, allowing for faster development and improved performance.
⢠Evaluation Metrics for Deep Learning: Measuring the performance of deep learning models using appropriate evaluation metrics, such as accuracy, precision, recall, and F1 score.
⢠Ethical Considerations in Deep Learning: Examining ethical concerns related to deep learning, such as bias, privacy, and fairness.
⢠Applications of Deep Learning in QA: Applying deep learning concepts and techniques in quality assurance, including anomaly detection, predictive maintenance, and computer vision tasks.
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