Getting through Deep Learning – Tensorflow intro (part 3)

This post is part of a tutorial series:

  1. Getting through Deep Learning – CNNs (part 1)
  2. Getting through Deep Learning – TensorFlow intro (part 2)
  3. Getting through Deep Learning – TensorFlow intro (part 3)

Alright, lets move on to more interesting stuff: linear regression. Since the main focus in TensorFlow, and given the abundancy of online resources on the subject, I’ll just assume you are familiar with Linear Regressions.

As previously mentioned, a linear regression has the following formula:

linear_regression

Where Y is the dependent variable, X is the independent variable, and b0 and b1 being the parameters we want to adjust.

Let us generate random data, and feed that random data into a linear function. Then, as opposed to using the closed-form solution, we use an iterative algorithm to progressively become closer to a minimal cost, in this case using gradient descent to fit a linear regression. Continue reading “Getting through Deep Learning – Tensorflow intro (part 3)”

Getting through Deep Learning – Tensorflow intro (part 2)

Yes, I kind of jumped the guns on my initial post on Deep Learning straight into CNNs. For me this learning path works the best, as I dive straight into the fun part, and eventually stumble upon the fact that maybe I’m not that good of a swimmer, and it might be good to practice a bit before going out in deep waters. This post attempts to be exactly that: going back to the basics.

This post is part of a tutorial series:

  1. Getting through Deep Learning – CNNs (part 1)
  2. Getting through Deep Learning – TensorFlow intro (part 2)

TensorFlow is a great starting point for Deep Learning/ Machine Learning, as it provides a very concise yet extremely powerful API. It is an open-source project created by Google initially with numerical computation tasks in mind, and used for Machine Learning/Deep Learning. Continue reading “Getting through Deep Learning – Tensorflow intro (part 2)”

Getting through Deep Learning – CNNs (part 1)

The number of available open source libraries making Deep learning easier to use is spreading fast as hype continuous to build. However, without understanding the background principles, it just feels like poking around a black box.

In this post (or several, most likely) will try to give an introduction to Convolution Neural Networks (CNNs). Note that, for the sake of brevity, I assume that you already know the basics about Neural Networks. If not, I would suggest you go through the following introduction.

This post is part of a tutorial series:

  1. Getting through Deep Learning – CNNs (part 1)
  2. Getting through Deep Learning – TensorFlow intro (part 2)
  3. Getting through Deep Learning – TensorFlow intro (part 3)

Disclaimer: this post uses images and formulas from distinct sources. I would suggest to have a look over the complete list of sources at the end of the post, as usual.

Inspiration

In 1958 and 1959 David H. Hubel and Torsten Wiesel performed a series of experiments, whereby they concluded that many neurons in the visual cortex focus on a limited region in the vision field.

This insight provided the notion of a local receptive field – a narrow sub-region of what is available in the whole visual field which serves as input – thus giving rise for a different architecture than the previously fully connected neural network architecture.

Basics – Convolution Layer

The first thing to realize is that Convolution networks are simply the application of “mini-neural networks” to segments of input space. In the case of images, that results in that neurons in the first convolutional layer are not connected to every single pixel in their Receiptive Field (RF).  The following image (source) shows an illustration of how a a a convolution layer is built using an image from the famous MNIST dataset – whereby the goal consists in identifyying the digits from handwritten numbers pictures.

Cnn_layer

 

Continue reading “Getting through Deep Learning – CNNs (part 1)”