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Practical hands-on exercises to build intuition on deep neural networks
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Learn essential concepts in deep learning through practical applications
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A comprehensive understanding of neural network architecture and design
Unlock the power of deep learning and take your machine learning skills to the next level with our comprehensive course on deep neural networks.
This hands-on course will provide you with a solid understanding of the fundamentals of deep learning, including artificial neural networks, activation functions, bias, data, and loss functions. You will learn the basics of Python, with a focus on data science, as well as the essential tools for cleaning and examining data, plotting with Matplotlib, and working with NumPy and Pandas.
With this foundation in place, you will dive deep into the world of deep learning, starting with the MP Neuron model and progressing to the Perceptron, the Sigmoid Neuron, and the Universal Approximation Theorem. You will explore common activation functions, such as ReLU and SoftMax, and learn how to apply them in real-world applications.
Through a series of practical exercises, you will gain hands-on experience with TensorFlow 2.x, one of the most popular deep learning frameworks in use today. You will learn how to create and train deep neural networks, evaluate their performance, and fine-tune them for optimal results.
By the end of the course, you will be well on your way to becoming a deep learning expert in no time.
This course is suitable for anyone interested in exploring the field of deep learning and building a solid foundation in artificial neural networks. No prior experience in programming or machine learning is required, making it an ideal starting point for beginners. It is ideal for students, professionals, and anyone who wants to enhance their skills and stay up-to-date with the latest developments in the field of artificial intelligence. Whether you are looking to kickstart your career or simply want to explore the exciting world of deep learning, this course is a great choice.
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Learn about the fundamentals of Python and some of its well-known libraries
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Understand the fundamentals of deep learning and neural networks
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Build and train your own deep neural network models
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Learn different activation functions and optimization algorithms
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Learn techniques for improving model performance and reducing overfitting
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Apply deep learning to real-world problems in various fields
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