In a previous post, I gave an introduction to Policy Gradient reinforcement learning. Policy gradient-based reinforcement learning relies on using neural networks to learn an action policy for the control of agents in an environment.… Read More »A2C Advantage Actor Critic in TensorFlow 2
Updated for TensorFlow 2 Google’s TensorFlow has been a hot topic in deep learning recently. The open source software, designed to allow efficient computation of data flow graphs, is especially suited to deep learning tasks. It is designed to be executed on single or multiple CPUs and GPUs, making it a good option for complex deep… Read More »Python TensorFlow Tutorial – Build a Neural Network
A growing trend in deep learning (and machine learning in general) is a probabilistic or Bayesian approach to the problem. Why is this? Simply put – a standard deep learning model produces a prediction, but… Read More »Bayes Theorem, maximum likelihood estimation and TensorFlow Probability
In a series of recent posts, I have been reviewing the various Q based methods of deep reinforcement learning (see here, here, here, here and so on). Deep Q based reinforcement learning operates by training… Read More »Policy Gradient Reinforcement Learning in TensorFlow 2
In previous posts (here and here) I introduced Double Q learning and the Dueling Q architecture. These followed on from posts about deep Q learning, and showed how double Q and dueling Q learning is… Read More »Atari Space Invaders and Dueling Q RL in TensorFlow 2
In this post, we’ll be covering Dueling Q networks for reinforcement learning in TensorFlow 2. This reinforcement learning architecture is an improvement on the Double Q architecture, which has been covered here. In this tutorial,… Read More »Dueling Q networks in TensorFlow 2
In previous tutorials, I’ve explained convolutional neural networks (CNN) and shown how to code them. The convolutional layer has proven to be a great success in the area of image recognition and processing in machine… Read More »Introduction to ResNet in TensorFlow 2
In previous posts (here and here), deep Q reinforcement learning was introduced. In these posts, examples were presented where neural networks were used to train an agent to act within an environment to maximize rewards.… Read More »Double Q reinforcement learning in TensorFlow 2
In this post, I’m going to cover the very important deep learning concept called transfer learning. Transfer learning is the process whereby one uses neural network models trained in a related domain to accelerate the development… Read More »Transfer learning in TensorFlow 2 tutorial
For those familiar with convolutional neural networks (if you’re not, check out this post), you will know that, for many architectures, the final set of layers are often of the fully connected variety. This is… Read More »An introduction to Global Average Pooling in convolutional neural networks