TensorFlow in Action: TensorBoard, Training a Model, and Deep Q-learning
In data science, Q-learning represents an approach to learning about a state space and following the optimal policy thereafter. A classic example is a robot navigating a maze. Below are the videos from the TensorFlow London meetup featuring deep dive into TensorFlow’s architecture and a Q-learning example.
Technical overview of TensorFlow
In her session, Rebecca Murphy of Ocado Technology focused on TensorFlow under-the-hood mechanisms and demonstrated how to install the solution, load data, feed, checkpoint, and load the model. Then, she moved on to explaining what TensorBoard is and how to use it for visualizing learning. Finally, she talked about the further development of TensorFlow.
Deep Q-learning with TensorFlow and PyGame
In his presentation, Daniel Slater from Bank of America Merrill Lynch touched upon such topics as reinforcement learning and Q-learning and how to apply these techniques to implement the PyGame framework with TensorFlow.
The PyGame solution described by Daniel Slater was also presented at the PyData conference earlier this year. In addition to a detailed description and great live demo, Daniel talked on TensorFlow, neural networks, reinforcement learning, Q-learning, and convolutional networks.
- What are the main challenges that can be solved with TensorFlow?
- The recommendations for those getting started with TensoFlow
- How can TensorFlow be further improved? How can the community contribute?
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- Visualizing TensorFlow Graphs with TensorBoard
- Monitoring and Visualizing TensorFlow Operations in Real Time with Guild AI
- Learning Game Control Strategies with Deep Q-Networks and TensorFlow
- Mastering Game Development with Deep Reinforcement Learning and GPUs
About the speakers