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Updated in [February 21st, 2023]

What skills and knowledge will you acquire during this course?
This course will provide learners with the skills and knowledge to develop deep learning models using PyTorch, a powerful Python library. Learners will gain an understanding of the fundamentals of Linear Regression, Logistic/Softmax Regression, Feedforward Deep Neural Networks, Convolutional Neural Networks, Transfer Learning, and other Deep Learning methods. They will also learn how to apply different activation functions, normalization and dropout layers to Deep Neural Networks. Additionally, they will be able to apply their knowledge of Deep Neural Networks and related machine learning methods to real-world applications.

How does this course contribute to professional growth?
This course provides professionals with the opportunity to gain a comprehensive understanding of Deep Neural Networks and related machine learning methods. Through the use of PyTorch, a powerful Python library, learners can develop deep learning models and apply their knowledge to real-world applications. By understanding the fundamentals of Linear Regression, Logistic/Softmax Regression, Feedforward Deep Neural Networks, Convolutional Neural Networks, Transfer Learning, and other Deep Learning methods, professionals can expand their skillset and increase their professional growth.

Is this course suitable for preparing further education?
This course is suitable for preparing further education as it covers the fundamentals of Linear Regression, Logistic/Softmax Regression, Feedforward Deep Neural Networks, Convolutional Neural Networks, Transfer Learning, and other Deep Learning methods. Learners will gain an understanding of how to develop deep learning models using PyTorch, a powerful Python library, and how to apply their knowledge of Deep Neural Networks and related machine learning methods to real-world applications.