Gradient descent is the backbone of a lot of machine learning algorithms, deep learning included. It is used only during the training, and it is the most computationally expensive part of machine learning. Gradient descent is a very complicated mathematical topic which I cannot explain in its entirety in a single blog post. However, I … Continue reading Mathematical Basics Of Gradient Descent
The primary function of a feedfoward neural network is to create a prediction of some sorts. The most popular task that is handled by a feedfoward neural network is classification or categorization. Classification is a task where a program is handed some sort of data, and the program classifies the data as something. One popular … Continue reading Neural Network Output Units
In part one of this series, I covered two very basic probability distributions - Bernoulli and multinouli. If you want to find out more about those, or if you wish to learn a bit about what are probability distributions, discrete and continuous random variables, go here. In this post, we're covering two slightly bit more … Continue reading Probability Distributions Part II – Gaussian, Exponential
Probability distributions are used in statistics to describe how likely a random variable is to take on each of it's possible states. Random variables can be discrete and continuous. A discrete random variable has a finite number of possible outcomes, whereas a continuous random variable has an infinite number of possible outcomes. The Bernoulli distribution … Continue reading Probability Distributions Part I – Bernoulli, Multinoulli
Linear algebra is a branch of mathematics which deals with solving a system of linear equations. It is widely used throughout science and engineering and it is essential to understanding machine learning algorithms. Linear algebra defines three basic data-structures - vectors, matrices and tensors, which are constantly used in machine and deep learning. In this … Continue reading Vectors, Matrices, Tensors – What’s The Difference?
Pretrained networks are very useful. A pretrained network is a deep learning model which has been already trained on some data and the weights of the model have been made publicly available for free use. The famous example of a pretrained network is the VGG series of networks. VGG stands for "Visual Geometry Group", which … Continue reading Where Do I Get My Pretrained Networks?
A few days ago, I published a blog post on writing a python program which transfers style onto a content image using Keras, which you can find here. The reason why I wrote it using Keras and not Tensorflow, is that I've been trying to write a functioning Tensorflow style transfer program for two weeks … Continue reading Tensorflow Style Transfer That Actually Works
This has got to be one of the coolest implementations of machine learning. If you don't know what neural style transfer is, it's basically taking a content image, like a photograph that you took of you and your family, and a style image, most of the time you would choose a famous painting with a … Continue reading Neural Style Transfer In Keras
Image recognition is currently my favorite type of machine learning. I say currently because I find language translation and NLP quite interesting. Convolutional neural networks, at the time of writing this, are the most efficient and accurate method used for image recognition. While you could use a standard fully connected deep neural network with a … Continue reading Implementing A Convolutional Neural Network Using Tensorflow
Siraj Raval's video on how to make word vectors out of five A Song Of Ice And Fire books is a helpful demonstration of word embeddings, but not so helpful as a tutorial because he uses a bunch of smaller libraries which enable you to do the training of the model in just a couple … Continue reading Rewriting Siraj Raval’s Game of Thrones Word Vectors Using Tensorflow