The number of applications of deep learning and neural networks has multiplied in the last couple of years. Neural nets has enabled significant breakthroughs in everything from computer vision, voice generation, voice recognition and self-driving cars. Game development is also a key area where these techniques are being applied. This book will give an in depth view of the potential of deep learning and neural networks in game development.

We will take a look at the foundations of multi-layer perceptron's to using convolutional and recurrent networks. In applications from GANs that create music or textures to self-driving cars and chatbots. Then we introduce deep reinforcement learning through the multi-armed bandit problem and other OpenAI Gym environments.

As we progress through the book we will gain insights about DRL techniques such as Motivated Reinforcement Learning with Curiosity and Curriculum Learning. We also take a closer look at deep reinforcement learning and in particular the Unity ML-Agents toolkit. By the end of the book, we will look at how to apply DRL and the ML-Agents toolkit to enhance, test and automate your games or simulations. Finally, we will cover your possible next steps and possible areas for future learning.

Osoby które kupowały "Hands-On Deep Learning for Games", wybierały także:

• Data Science w Pythonie. Kurs video. Algorytmy uczenia maszynowego 179,00 zÅ‚, (53,70 zÅ‚ -70%)
• Data Science w Pythonie. Kurs video. Przetwarzanie i analiza danych 149,00 zÅ‚, (44,70 zÅ‚ -70%)
• Power BI Desktop. Kurs video. Wykorzystanie narz 299,00 zÅ‚, (89,70 zÅ‚ -70%)
• Korporacyjne jezioro danych. Wykorzystaj potencja 48,06 zÅ‚, (14,90 zÅ‚ -69%)
• J 86,77 zÅ‚, (26,90 zÅ‚ -69%)

Spis treści