Deep Learning - Artificial Neural Networks with TensorFlow

In this 4 hr course, gain hands-on expertise in building artificial neural networks using TensorFlow 2. You'll explore the principles of machine learning, from classification to regression, and learn key concepts such as loss functions and gradient descent. This course is tailored to provide a balance between theoretical insights and practical coding skills.

What I will be able to do after this course

• Confidently use TensorFlow 2 to develop artificial neural networks for various applications.
• Understand key concepts in machine learning including classification and regression.
• Implement advanced neural network architectures with TensorFlow effectively.
• Master essential loss functions like mean squared error and cross-entropy.
• Learn optimization techniques such as stochastic gradient descent and Adam.

Course Instructor(s)

Lazy Programmer, a seasoned professional in AI and machine learning, brings years of practical experience and teaching expertise to this course. Having worked extensively with deep learning frameworks, they emphasize grasping fundamental concepts while applying them through hands-on projects. Their approachable teaching style ensures that learners grasp complex ideas effectively.

Who is it for?

This course is ideal for individuals passionate about deep learning and its applications, especially those with a background in Python and data science libraries like NumPy and Matplotlib. It caters to learners aiming to build strong foundations in implementing neural networks and progressing toward advanced topics like CNNs, transformers, and more. If you're ready to deepen your understanding of AI concepts and practical implementations, this course is for you.