Making Deep Learning Smarter

By now I hope I have managed to demystify the artificial intelligence behind Deep Learning. To put it simply it’s all about exploiting the patterns in the data and we do this by getting clues from examples. Using the clues, we ever so slightly nudge our neural network in a direction that makes its results more correct. Deep learning practitioners […]

Training A Network in Practice

In the last post I highlighted precautions that have to be taken when preparing data for a deep learning as well as the problems that may arise when proper care is not taken in handling and preparing data. In this post I will talk about the training process -the tools to use, steps to take, challenges that may arise and […]

Preparing Data For Deep Learning

So far we have looked at how Deep Learning works, different kinds of deep learning techniques and specific examples of deep learning, but perhaps one of the most important aspects of Deep Learning is preparing the data that we use to train a neural network. In this post I will describe common pitfalls and best practices for handling data. I […]

Deep Learning with Sequences in Practice

In the last post I described how deep neural networks, particularly Recurrent Neural Networks (RNN), can be used on input with varying sizes or sequences. In this post I’m going to describe an example of an RNN and show the results it produces. Before I do that  I’m going to explain what is in the RNN block which I called […]

Deep Learning with Sequences

In the last two posts I describe how convolutional neural networks work and how the design of the network takes into account the grid-like shape of the input images by using a grid of neurons. I also describe how sections on the input image are mapped to each neuron in the first layer through the use of a filter and […]

Deep Learning with Images in Practice

In the last post I introduced convolutional neural networks as a specialized technique for doing deep learning with images and I described how to build one. In this post I will look at record breaking convolutional neural networks like VGG and ResNet. I will focus on the metrics that we use to compare networks and the architectural difference between them. […]

Deep Learning with Images – Convolutional Neural Networks

In the last post I introduced the simple model of a neuron and used it in two examples: comparing two numbers and identifying digits drawn on a 7×7 grid of squares. In this post, I will go a step further and build a network that can see a photo and correctly identify what is in the photo. Images Something that […]

What is Deep Learning? (Continued)

In my last post I describe Deep Learning as a way to teach computers to hear, see, talk, or, more generally, think in order to solve a problem or create new ideas. I also demonstrated how a simple model of a neuron could be used to build a neural network, which, in turn, can be used to perform a task like […]

What is Deep Learning?

Deep Learning is a way to teach computers to hear, see, talk, or, more generally, think in order to solve a problem or create new ideas. To do this, we show the computer a lot of examples of how the task is done such that when presented with a problem that is similar but not the same as the examples, […]