Intro to Deep Learning
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- Intro to Deep Learning
This course provides a practical introduction to Deep Learning using TensorFlow and Keras. You will learn how neural networks are built, trained, and optimized for structured data problems.Through...
This course provides a practical introduction to Deep Learning using TensorFlow and Keras. You will learn how neural networks are built, trained, and optimized for structured data problems.
Through guided tutorials and hands-on exercises, you’ll move from understanding a single neuron to building deep neural networks capable of solving real-world classification tasks. By the end of the course, you’ll have a strong foundation to continue into advanced topics such as computer vision and deep learning applications.
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Core concepts of Deep Learning and neural networks
How a single neuron and linear units work
Building deep neural networks with hidden layers
Training models using stochastic gradient descent
Understanding and handling overfitting and underfitting
Using dropout and batch normalization to improve performance
Implementing binary classification models
Practical experience with TensorFlow and Keras
Basic knowledge of Python programming
Familiarity with basic mathematics (algebra and simple functions)
Introductory understanding of Machine Learning is helpful but not required
A computer with internet access
Willingness to practice and experiment with code
Beginners who want to start learning Deep Learning
Machine Learning students ready to move to neural networks
Software developers interested in AI and data-driven systems
Data analysts and engineers expanding their skill set
Anyone preparing for advanced topics like Computer Vision
