Linux Skills for 2025 (+ How to Add Them to Your Resume)
February 19, 2025
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Instructor: Alex Aklson
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Looking to start a career in Deep Learning? Look no further. This course will introduce you to the field of deep learning and help you answer many questions that people are asking nowadays, like what is deep learning, and how do deep learning models compare to artificial neural networks? You will learn about the different deep learning models and build your first deep learning model using the Keras library.
After completing this course, learners will be able to: • Describe what a neural network is, what a deep learning model is, and the difference between them. • Demonstrate an understanding of unsupervised deep learning models such as autoencoders and restricted Boltzmann machines. • Demonstrate an understanding of supervised deep learning models such as convolutional neural networks and recurrent networks. • Build deep learning models and networks using the Keras library.
In this module, you will learn about exciting applications of deep learning and why now is the perfect time to learn deep learning. You will learn about neural networks and how most deep learning algorithms are inspired by how our brain functions and the neurons process data. You will also learn about how neural networks feed data forward through the network.
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In this module you will learn about the gradient descent algorithm and how variables are optimized with respect to a defined function. You will also learn about backpropagation and how neural networks learn and update their weights and biases. Furthermore, you will learn about the vanishing gradient problem and how activation functions are used in artificial neural networks.
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In this module, you will learn about the different deep learning libraries: Keras, PyTorch, and TensorFlow. You will also learn how to build regression and classification models using the Keras library.
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In this module, you will learn the difference between shallow and deep neural networks. You will also learn about convolutional networks and how to build them using the Keras library. Finally, you will also learn about recurrent neural networks and autoencoders.
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In this module, you will conclude the course by working on a final assignment where, you will classify aircraft damage using a pre-trained VGG16 model and generate captions using a Transformer-based pretrained model.
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We asked all learners to give feedback on our instructors based on the quality of their teaching style.
At IBM, we know how rapidly tech evolves and recognize the crucial need for businesses and professionals to build job-ready, hands-on skills quickly. As a market-leading tech innovator, we’re committed to helping you thrive in this dynamic landscape. Through IBM Skills Network, our expertly designed training programs in AI, software development, cybersecurity, data science, business management, and more, provide the essential skills you need to secure your first job, advance your career, or drive business success. Whether you’re upskilling yourself or your team, our courses, Specializations, and Professional Certificates build the technical expertise that ensures you, and your organization, excel in a competitive world.
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Reviewed on Jul 10, 2024
The course is quite complex for a person who does not have knowledge of algebra, statistics and calculus, the final project was good because it was challenging.
Reviewed on Oct 9, 2019
Good practical examples for ANN. It could be improved the theoretical part and compare better the architecture of the networks with the algorithms and code for Keras
Reviewed on Jul 11, 2024
The video and Jupyter notebooks were both concise and of excellent quality. However, the versions of dependent libraries are somewhat outdated, which makes it quite challenging to run locally.
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