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Updated in [July 04th, 2023]

This course provides an overview of Neural Networks, from the basics to image classification with Convolutional Neural Networks. StatQuest breaks down complicated Statistics and Machine Learning methods into small, bite-sized pieces that are easy to understand. The course is designed to build up the student's understanding of Statistics and Machine Learning, without dumbing down the material. Topics covered include the fundamentals of Neural Networks, activation functions, backpropagation, and image classification with Convolutional Neural Networks.

[Application]
After completing this course, learners can apply their knowledge of Neural Networks to a variety of tasks, such as image recognition, natural language processing, and forecasting. Learners can also use the concepts learned in this course to develop their own Neural Network models. Additionally, learners can use the StatQuest approach to further their understanding of Statistics and Machine Learning.

[Career Path]
Neural Networks are a powerful tool for Machine Learning. They are used to solve complex problems that are difficult to solve with traditional methods. Neural Networks are used in a variety of applications, such as image recognition, natural language processing, and robotics.

Job Position Path:Deep Learning Engineer
Deep Learning Engineers are responsible for developing and deploying deep learning models. They use a variety of techniques, such as supervised and unsupervised learning, to create models that can be used to solve complex problems. Deep Learning Engineers must have a strong understanding of mathematics, statistics, and computer science. They must also be familiar with the latest deep learning frameworks and technologies.

The demand for Deep Learning Engineers is increasing as more companies are looking to leverage the power of deep learning to solve complex problems. Companies are investing heavily in research and development of deep learning technologies, and they are looking for engineers who can help them develop and deploy these models. As deep learning becomes more widely used, the demand for Deep Learning Engineers will continue to grow. Additionally, the development of new deep learning frameworks and technologies will create new opportunities for Deep Learning Engineers.

[Education Path]
The recommended educational path for learners is to pursue a degree in Artificial Intelligence (AI). This degree will provide learners with a comprehensive understanding of the fundamentals of AI, including machine learning, deep learning, natural language processing, computer vision, and robotics. Learners will also gain an understanding of the development trends in AI, such as the use of big data, cloud computing, and the Internet of Things. The degree will also provide learners with the skills to develop and deploy AI applications in various industries. Additionally, learners will gain an understanding of the ethical and legal implications of AI, as well as the potential risks and benefits of AI. Finally, learners will gain an understanding of the current and future applications of AI, such as autonomous vehicles, healthcare, and finance.