Build with modern libraries like Tensorflow, Theano, Keras, PyTorch, CNTK, MXNet. Train faster with GPU on AWS.
InstructorLazy Programmer Inc.Covers essential optimization theory including stochastic gradient descent, momentum, and adaptive learning rate procedures like Adam and RMSprop.
Instructional signals suggest the instructor explains complex deep learning concepts effectively, aiding understanding even for those with academic backgrounds.
Editorial course preview
These 3 complementary views highlight concrete, legible examples from the course presentation.
This slide outlines fundamental modern deep learning techniques including adaptive optimizers like Adam, dropout regularization, batch normalization, and frameworks such as TensorFlow.
This slide outlines fundamental modern deep learning techniques including adaptive optimizers like Adam, hyperparameter selection, dropout regularization, batch normalization, and GPU acceleration.
This slide displays a schematic of a neural network, labeling the distinct input, hidden, and output layers to explain the foundational structure used in modern deep learning.
Programming examples utilize legacy libraries like Theano and older TensorFlow 1.x syntax; while the course was updated in February 2026, this label does not guarantee that these specific technical elements were refreshed. One signal from a review dated April 2025 suggests a disconnect between theoretical lectures and the implementation of coding exercises, though the February 2026 update may have addressed this.
One signal from a review dated April 2025 suggests a disconnect between theoretical lectures and the implementation of coding exercises, though the February 2026 update may have addressed this.
Selected from the course's public promotional preview. These images document visible presentation material only; they do not represent the complete paid curriculum.
Aligns well with the target of deepening machine learning knowledge through framework-specific implementation.