Artificial Neural Networks and Deep Learning in Practice
Learn the fundamentals of Artificial Neural Networks (ANNs) and Deep Learning with coding examples. Ideal for students and practitioners in academia and industry. Start your journey in Neural Networks and Deep Learning today.
What you’ll learn
- The fundamentals of Artificial Neural Networks (ANNs) and reviews state-of-the-art DL examples.
- The fundamental of Deep learning and the most popular algorithms.
- It has several coding examples.
- It has several quizzes for learning better.
Artificial neural networks and deep learning are the most recent and advanced machine learning topics, with numerous applications in a variety of fields. They show promising results in a variety of fields, including computer vision, drug discovery, and stock market prediction. A lot of researchers and people who aren’t in computer science or related fields are also interested in learning about artificial neural networks and deep learning architectures and using them in their projects. This is because they can solve different kinds of problems by using different kinds of data.
This course will teach you the fundamentals of artificial neural networks and deep learning, as well as some coding examples to help you understand the concepts. The course is designed for people who are new to the machine learning and deep learning fields and want to learn how to implement deep learning algorithms in Python, TensorFlow, and Keras.
The course includes some references and links to additional reading. You can ask your question during the Q&A session. You can also use the messaging system to contact the professor and ask questions.
I think that the information in this course will be well received by undergraduate and graduate students, researchers in deep learning, and practitioners in academia and business around the world.
Who this course is for:
- It is useful for undergraduate and graduate students, as well as practitioners in industry and academia.
- Anyone who would like to learn Neural networks and deep learning.
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