Tensorflow and Deep Reinforcement Learning, Without a PhD

This course covers the newest developments in deep learning, focusing on neural network architectures, image recognition, natural language processing, and reinforcement learning. The goal is to equip software engineers with the knowledge and best practices to apply these techniques in their projects without needing a PhD. The course teaches concepts such as cross entropy loss, TensorFlow, placeholders, low-level TensorFlow, training, visualization, and provides examples like the pancake flipping robot, skeleton walking robot, and AlphaGo. The teaching method includes explanations, tips, engineering best practices, and pointers. The intended audience for this course is software engineers interested in adopting machine learning and deep neural networks in their projects.