Deep Learning with Python (Video 1)

This video tutorial introduces a deep learning course led by Adder Santana, an electrical engineer with expertise in memory attention and video processing. The course covers neural network-based technology for machine intelligence, focusing on building and training models in Python. Key topics include deep learning basics, backpropagation, Theano, Keras, image classification, and recurrent neural networks. The course concludes with a project to apply learned concepts and a brief discussion on TensorFlow. Prior knowledge of Python, calculus, and linear algebra is recommended, but not essential.

What is the main focus of the PH.D. thesis mentioned in the text?

Describe the process of back propagation in deep learning.

Explain the significance of automatic differentiation in deep learning.

What are the applications of recurrent neural networks as mentioned in the text?

What prerequisites are assumed for participants in the deep learning course?