DeepMind x UCL RL Lecture 2 – Solutions
Suggested answers to the Reinforcement Learning questions posed in the second lecture by DeepMind x UCL.
On Machine Intelligence
Why Artificial Intelligence and Machine Learning are changing the world
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Nethack Reinforcement Learning
If you’re a fan of old 1980’s games, then you’ll be interested in this reinforcement learning environment. NetHack is a turn-based Dungeons Dragons style video game. The player controls a character tasked with finding the Amulet of Yendor, which is buried deep within a dungeon. During the game, the character will encounter lots of [ ]
Solving MNIST with a Neural Network from the ground up
Note: Here s the Python source code for this project in a Jupyter notebook on GitHub I ve written before about the benefits of reinventing the wheel and this is one of those occasions where it was definitely worth the effort. Sometimes, there is just no substitute for trying to implement an algorithm to really understand what s [ ]
The Softmax Function Derivative (Part 3)
Previously I ve shown how to work out the derivative of the Softmax Function combined with the summation function, typical in artificial neural networks. In this final part, we ll look at how the weights in a Softmax layer change in respect to a Loss Function. The Loss Function is a measure of how bad the estimate [ ]
The Softmax Function Derivative (Part 2)
In a previous post, I showed how to calculate the derivative of the Softmax function. This function is widely used in Artificial Neural Networks, typically in final layer in order to estimate the probability that the network s input is in one of a number of classes. In this post, I ll show how to calculate the [ ]
The Softmax Function Derivative (Part 1)
Introduction This post demonstrates the calculations behind the evaluation of the Softmax Derivative using Python. It is based on the excellent article by Eli Bendersky which can be found here. The Softmax Function The softmax function simply takes a vector of N dimensions and returns a probability distribution also of N dimensions. Each element of [ ]
Bad headlines distract from real AI problems
For several years now, few articles about artificial intelligence in the popular press are published without being accompanied by a picture of a Terminator robot. The point is clear: artificial intelligence is coming and it is terrifying. Having sown the seeds of fear, the headline writers are now subtly reinforcing that view. Take TechCrunch, which [ ]
Neural Networks and the generalisation problem
Over the last few weeks, a robust debate has been taking place online about the prospects that Deep Learning neural networks would lead to advances in the quest for Artificial General Intelligence. All current AI is what is known as Artificial Narrow Intelligence. This means that the models work well (sometimes extremely well) on specific [ ]
Deep Learning Dead-End?
Deep Learning is at the core of much of modern Artificial Intelligence. It has had some spectacular recent successes, not least being a major part of the system that beat the world champion at Go. Key to its success is the Back-Propagation algorithm, usually shortened to Backprop . I ve written elsewhere about how this algorithm works, [ ]
XOR Revisited: Keras and TensorFlow
A few weeks ago, it was announced that Keras would be getting official Google support and would become part of the TensorFlow machine learning library. Keras is a collection of high-level APIs in Python for creating and training neural networks, using either Theano or TensorFlow as the underlying engine. Given my previous posts on implementing an [ ]