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The Truth About Deep Learning


I've been studying and writing about DL for close to two years now, and it still amazes the misinformation surrounding this relatively complex learning algorithm. This post is not about how Deep Learning is or is not over-hyped, as that is a well documented debate. This discussion/rant is somewhat off the cuff, but the whole point was to encourage those of us in the machine learning community to think clearly about deep learning. Let's be bold and try to make some claims based on actual science about whether or not this technology will or will not produce artificial intelligence. After all, aren't we supposed to be the leaders in this field and the few that understand its intricacies and implications?

Deep Neural Network from Scratch in Python


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."

Deep Learning And Neural Networks


If you've been following developments over the last few years, you may have noticed that deep learning and neural networks have grown wildly. Neural network architecture is able to make predictive judgments in in sports, medicine and the financial sector.

Building Convolutional Neural Networks with Tensorflow


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.

Top AI Algorithms in Healthcare


This is a standard machine learning algorithm that uses supervised learning methods for classification, regression, and detection of outliers. They are used for protein classification, image segmentation and text categorization. A group of deep learning algorithms inspired by the nervous system. More precisely, they are inspired by a neuron organization in animal brains. They consist of units – artificial neurons that receive a signal from the previous layer, process it and send it to the next layer.