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
I love this kind of featured images. It makes people think that you are so smart and that you've figured out life. They're hilarious. It it makes the topic of the post seem like it's really deep, pun intended. If you've worked with neural networks even a little bit, you've probably come across the term activation function. … Continue reading Activation functions explained
Overfitting can be a serious problem in deep learning. Dropout is a technique developed to solve this exact problem. It is one of the biggest advancements in deep learning to come out in the last few years. What is dropout? Dropout is a technique for addressing the overfitting problem. The idea is to randomly drop … Continue reading What Is Dropout? – Deep Learning
For some very simple problems, a single layer neural might be able to do the job quite well . You might be able to do to process this data set with a single layer, but this is meant to show you how to build a multi layer neural network utilizing L2 regularization with Tensorflow and … Continue reading Build A Multi Layer Neural Network With L2 Regularization Using Tensorflow
I realize that some people, mainly social justice warriors, might find this gender insensitive and offensive, but I don't care. //NOTE: This simple example is going to use body measurements like weight, height etc. This is not going to be an image recognition app. We'll get to that eventually. Step 1: Dependencies For this simple … Continue reading Build A Simple Gender Guessing App Using Tensorflow