Alexis Cook (et al.) | Duration: 11:48 h | Video: H264 1280x720 | Audio: AAC 44,1 kHz 2ch | 2,29 GB | Language: English
Learn the deep reinforcement learning skills that are powering amazing advances in AI. Then start applying these to applications like video games and robotics.
Learn cutting-edge deep reinforcement learning algorithms—from Deep Q-Networks (DQN) to Deep Deterministic Policy Gradients (DDPG). Apply these concepts to train agents to walk, drive, or perform other complex tasks, and build a robust portfolio of deep reinforcement learning projects.
The demand for engineers with reinforcement learning and deep learning skills far exceeds the number of engineers with these skills. This program offers a unique opportunity for you to develop these in-demand skills. You'll implement several deep reinforcement learning algorithms using a combination of Python and deep learning libraries that will serve as portfolio pieces to demonstrate the skills you've acquired. As interest and investment in this space continues to increase, you'll be ideally positioned to emerge as a leader in this groundbreaking field.
Prerequisites and Requirements
• Intermediate to advanced Python experience. You are familiar with object-oriented programming. You can write nested for loops and can read and understand code written by others.
• Intermediate statistics background. You are familiar with probability.
• Intermediate knowledge of machine learning techniques. You can describe backpropagation, and have seen a few examples of neural network architecture (like a CNN for image classification).
• You have seen or worked with a deep learning framework like TensorFlow, Keras, or PyTorch before.
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