In this video we build on last week Multilayer perceptrons to allow for more flexibility in the architecture! However, we need to be careful about the layer of abstraction we put in place in order to facilitate the work of the user who want to simply fit and predict. Here we make use of the following three concept: Network, Layer and Neuron. These three components will be composed together to make a fully connected feedforward neural network neural network. For those who don't know a fully connected feedforward neural network is defined as follows (From Wikipedia): "A feedforward neural network is an artificial neural network wherein connections between the nodes do not form a cycle. As such, it is different from its descendant: recurrent neural networks. The feedforward neural network was the first and simplest type of artificial neural network devised. In this network, the information moves in only one direction, forward, from the input nodes, through the hidden nodes (if any) and to the output nodes. There are no cycles or loops in the network."
In the past year I have also worked with Deep Learning techniques, and I would like to share with you how to make and train a Convolutional Neural Network from scratch, using tensorflow. Later on we can use this knowledge as a building block to make interesting Deep Learning applications. The pictures here are from the full article. Source code is also provided. Before you continue, make sure you understand how a convolutional neural network works.
Deep learning enables programs to train themselves to understand images and speech. But what exactly is it? Used by Siri, Cortana and Google Now to understand speech and recognise faces, deep learning is often confused with the concept of artificial intelligence (AI), so much so that the two terms are thought to be synonymous. Deep learning is a branch of machine learning, which in turn is a subset of AI. Take a plunge into the depths of deep learning Born with the development of computers, research in AI was quickly characterised by the emergence of different currents.