# Text Understanding from Scratch: Paper Summary This is a new format in whihc I am going to try myself in the next couple of weeks - paper summaryes. This is a paper by Xiang Zhang and Yann LeCun from NYU. Be sure to read the full paper after reading this summary. You can find the paper here. In this paper, they … Continue reading Text Understanding from Scratch: Paper Summary

# Why aren’t you using Anki – The greatest study tool EVER! I've been using Anki for a while now, and I have to say that it's a been game changer.  In this blog post, I am going to outline how I use Anki, as well as how can you use Anki to learn anything you want. What is Anki? Anki is an open source spaced repetition … Continue reading Why aren’t you using Anki – The greatest study tool EVER!

# Mathematical Basics Of Gradient Descent 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

# Neural Network Output Units 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

# Probability Distributions Part II – Gaussian, Exponential 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 Part I – Bernoulli, Multinoulli 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

# Vectors, Matrices, Tensors – What’s The Difference? 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?

# Where Do I Get My Pretrained Networks? 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?

# Tensorflow Style Transfer That Actually Works 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

# Neural Style Transfer In Keras 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