Machine Learning, Neural Networks, Deep Learning and Reinforcement Learning, GAN in Keras and TensorFlow
InstructorHolczer BalazsEditorial course preview
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The curriculum emphasizes the mathematical formulations of algorithms, including logistic regression, k-nearest neighbors, and numerical optimization.
Instruction spans a wide range of topics from classical machine learning to advanced neural networks like Transformers and GANs, ending with reinforcement learning.
Programming objectives often rely on existing libraries, which may hinder the ability to reinforce how algorithms are solved from scratch.
Some learners report that specific topics, such as SVMs, can be unclear or are covered at a pace that requires external research.
The curriculum aligns with the target of beginners through Python appendices and a progression from basic to complex topics.
