machine learning Deep Q-Learning: Combining Deep Learning and Q-Learning The idea in deep Q networks is that the states and possible outcomes in Q-Learning is replaced with a neural network which tries to approximate Q Values. It is referred to as the approximator
machine learning Introduction to Q Learning and Reinforcement Learning Read on to learn the basics of reinforcement learning and Q-Learning through an intuitive explanation, and a TensorFlow implementation!
machine learning Image to Image Translation using CycleGANs with Keras implementation Want to know how to generate Monet style paintings from any photograph of any scenery around the world? Enter CycleGANs. Read on to know more about CycleGANs and how they can be used in Image-to-Image Translation.
machine learning Face Aging using Conditional GANs with Keras implementation Felt intrigued when the FaceApp generated realistic photos of you at an older age? Read on to know how conditional GANs can be used for face aging, and how to implement it on your own using Keras!
machine learning Understanding Deep Convolutional GANs with a PyTorch implementation In this article, we will briefly describe how GANs work, what are some of their use cases, then go on to a modification of GANs, called Deep Convolutional GANs and see how they are implemented using the PyTorch framework.
