If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
In this video we build on last week Multilayer perceptrons to allow for more flexibility in the architecture! However, we need to be careful about the layer of abstraction we put in place in order to facilitate the work of the user who want to simply fit and predict. Here we make use of the following three concept: Network, Layer and Neuron. These three components will be composed together to make a fully connected feedforward neural network neural network. For those who don't know a fully connected feedforward neural network is defined as follows (From Wikipedia): "A feedforward neural network is an artificial neural network wherein connections between the nodes do not form a cycle. As such, it is different from its descendant: recurrent neural networks. The feedforward neural network was the first and simplest type of artificial neural network devised. In this network, the information moves in only one direction, forward, from the input nodes, through the hidden nodes (if any) and to the output nodes. There are no cycles or loops in the network."
I think that the best way to really understand how a neural network works is to implement one from scratch. That is exactly what I going to do through this article. I will create a neural network class, and I want to design it in such a way to be more flexible. I do not want to hardcode in it a specific activation or loss functions, or optimizers (that is SGD, Adam, or other gradient-based methods). I will design it to receive these from outside the class so that one can just take the class's code and pass to it whatever activation/loss/optimizer he wants.
Reading through a data science book or taking a course, it can feel like you have the individual pieces, but don't quite know how to put them together. Taking the next step and solving a complete machine learning problem can be daunting, but preserving and completing a first project will give you the confidence to tackle any data science problem. This series of articles will walk through a complete machine learning solution with a real-world dataset to let you see how all the pieces come together. We'll follow the general machine learning workflow step-by-step: Along the way, we'll see how each step flows into the next and how to specifically implement each part in Python. The complete project is available on GitHub, with the first notebook here. After completing the work, I was offered the job, but then the CTO of the company quit and they weren't able to bring on any new employees. I guess that's how things go on the start-up scene!) The first step before we get coding is to understand the problem we are trying to solve and the available data. In this project, we will work with publicly available building energy data from New York City. The objective is to use the energy data to build a model that can predict the Energy Star Score of a building and interpret the results to find the factors which influence the score. We want to develop a model that is both accurate *-- it can predict the Energy Star Score close to the true value -- and *interpretable -- we can understand the model predictions. Once we know the goal, we can use it to guide our decisions as we dig into the data and build models. Contrary to what most data science courses would have you believe, not every dataset is a perfectly curated group of observations with no missing values or anomalies (looking at you mtcars and iris datasets). Real-world data is messy which means we need to clean and wrangle it into an acceptable format before we can even start the analysis. Data cleaning is an un-glamorous, but necessary part of most actual data science problems.
Using the right dimensions for your Neural Network, we discussed how to choose a consistent convention for the vector and matrix shapes in your neural network architecture. While different software implementation may use a different convention, having a solid understanding makes it easy to know what preprocessing may be needed to fit the module design. In this part, we are going to extend our knowledge to understand how a neural network learns through the training sets provided. In this article, we will build a simple binary classifier architecture to predict whether images show that of a cat, or non-cat (for some reasons, someone starting out ML years ago must love cats -- many examples you see involve these cute animals!) The original shape of the training set is (209, 64, 64, 3), representing 3-channel 64-by-64 images of cats and non-cats.
This project introduces a novel model: the Knowledge Graph Convolutional Network (KGCN), available free to use from the GitHub repo under Apache licensing. It's written in Python, and available to install via pip from PyPi. The principal idea of this work is to forge a bridge between knowledge graphs, automated logical reasoning, and machine learning, using Grakn as the knowledge graph. A KGCN can be used to create vector representations, embeddings, of any labelled set of Grakn Things via supervised learning. There are many benefits to storing complex and interrelated data in a knowledge graph, not least that the context of each datapoint can be stored in full.
An Artificial Neural Network (ANN) is a computational model that is inspired by the way biological neural networks in the human brain process information. Artificial Neural Networks have generated a lot of excitement in Machine Learning research and industry, thanks to many breakthrough results in speech recognition, computer vision and text processing. In this post, we will try to develop an understanding of a particular type of Artificial Neural Network called the Multi-Layer Perceptron. ANNs are at the core of Deep Learning. They are versatile, powerful, and scalable, making them ideal to tackle large and highly complex Machine Learning tasks, such as classifying billions of images (e.g., Google Images), powering speech recognition services (e.g., Apple's Siri), recommending the best videos to watch to hundreds of millions of users every day (e.g., Youtube), or learning to beat the world champion at the game of Go by examining millions of past games and then playing against itself (DeepMind's AlphaGo).
Over three decades, the Internet has grown from a small network of computers used by research scientists to communicate and exchange data to a technology that has penetrated almost every aspect of our day-to-day lives. Today, it is hard to imagine a life without online access for doing business, shopping, and socialising. A technology that has connected humanity at a scale never before possible has also amplified some of our worst qualities. Online hate speech spreads virally across the globe with short and long term consequences for individuals and societies. These consequences are often difficult to measure and predict. Online social media websites and mobile apps have inadvertently become the platform for the spread and proliferation of hate speech.
This blog covers another interesting machine learning algorithm called Decision Trees and it's mathematical implementation. At every point in our life, we make some decisions to proceed further. Similarly, this machine learning algorithm also makes the same decisions on the dataset provided and figures out the best splitting or decision at each step to improve the accuracy and make better decisions. This, in turn, helps in giving valuable results. A decision tree is a machine learning algorithm which represents a hierarchical division of dataset to form a tree based on certain parameters.