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Updated in [June 30th, 2023]

This course, Fundamentals of CNNs and RNNs, provides learners with an introduction to the fundamental concepts of convolutional neural networks (CNNs) and recurrent neural networks (RNNs). These concepts are widely used in computer vision and natural language processing areas.

In the CNN part, learners will gain an understanding of the concepts of CNNs, the two major operators (convolution and pooling), and the structure of CNNs. In the RNN part, learners will learn the concept and the structure of RNNs, and the two variants of RNNs, LSTMs and GRUs.

By the end of this course, learners will have a basic understanding of CNNs and RNNs and the skills required for computer vision and natural language processing.

[Applications]
After completing this course, learners can apply their knowledge of CNNs and RNNs to various computer vision and natural language processing tasks. For example, they can use CNNs to classify images, and use RNNs to generate text. They can also use CNNs and RNNs to build models for other tasks such as object detection, image segmentation, and machine translation.

[Career Paths]
One job position path that is recommended to learners of this course is a Machine Learning Engineer. A Machine Learning Engineer is responsible for developing and deploying machine learning models and algorithms. They are also responsible for designing, building, and maintaining machine learning systems. They must have a strong understanding of the fundamentals of CNNs and RNNs, as well as other machine learning algorithms.

The development trend of this job position is that Machine Learning Engineers are increasingly in demand as more and more companies are looking to leverage the power of machine learning to improve their products and services. Companies are looking for Machine Learning Engineers who can develop and deploy machine learning models and algorithms quickly and efficiently. As such, Machine Learning Engineers must stay up to date with the latest advancements in machine learning and be able to apply them to their projects.

[Education Paths]
A recommended educational path for learners is to pursue a degree in Artificial Intelligence (AI). AI is a rapidly growing field that combines computer science, mathematics, and engineering to create intelligent machines that can think and act like humans. AI is used in a variety of applications, such as robotics, natural language processing, computer vision, and machine learning.

A degree in AI typically includes courses in computer science, mathematics, and engineering. Students will learn the fundamentals of AI, including algorithms, data structures, and programming languages. They will also learn about machine learning, deep learning, and natural language processing. Additionally, students will gain experience in developing AI applications and systems.

The development trend of AI is rapidly evolving. AI is becoming increasingly important in many industries, such as healthcare, finance, and transportation. AI is also being used to automate processes and improve efficiency. As AI technology continues to advance, it is expected to become even more prevalent in the future.