“You really don’t need maths to get started with AI”. Follow the empirical path of trial and error, it’s a slow process but with passion and interest, you can see progress. In this series, you will find “ How can you progress in AI if you lack mathematics skills.
Today, we’re going to write our own Python image recognition program. To do that, we’ll explore a powerful deep learning architecture called a deep convolutional neural network (DCNN). Convnets are the workhorses of computer vision. They power everything from self-driving cars to Google’s image search. So why are neural networks so powerful? One key reason: They do automatic pattern recognition. So what’s pattern recognition and why do we care if it’s automatic? Patterns come in many forms but let’s take two critical examples: The features that define a physical form.
The problem is most guides talk about tensors as if you already understand all the terms they’re using to describe the math. So what is a tensor and why does it flow? At its core it’s a data container. Mostly it contains numbers. Sometimes it even contains strings, but that’s rare. There are multiple sizes of tensors. Let’s go through the most basic ones that you’ll run across in deep learning
This article guides you through getting a powerful deep learning machine setup and installed with all the latest and greatest frameworks. We’re going to build our own Deep Learning Dream Machine. We’ll source the best parts and put them together into a number smashing monster. We’ll also walk through installing all the latest deep learning frameworks step by step on Ubuntu Linux 16.04. This machine will slice through neural networks like a hot laser through butter.
If you’re a developer or sys-admin you probably already use a lot of libraries and frameworks that you know little about. You don’t have to understand the inner workings of web-scraping to use curl. The same is true with AI. There are a number of frameworks and projects that make it easy to get going fast without needing a data science Ph.D. The math helps you feel confident about what’s going on behind the scenes. If you want to start using AI, you can do that today. Let’s get started with some practical projects.
You’re fascinated with AI. Maybe you’d love to dig deeper and get an image recognition program running in TensorFlow or Theano? Perhaps you’re a developer or systems architect and you know computers incredibly well but there’s just one little problem: You suck at math. That’s all right! Here are some little secrets that help you get rolling fast. No matter how you cut it, AI solves big, intractable problems that have eluded us for decades. AI is hot for good reasons.
A Comparison of Tableau and Power BI, the two Top Leaders in the BI Market
Why you should forget loops and embrace vectorization for Data Science
Insights to Agile Methodologies for Software Development
Greedy Algorithm and Dynamic Programming
Cloudera vs Hortonworks vs MapR: Comparing Hadoop Distributions
Introduction Access to and control of data is one of the biggest challenges faced by data analysts and data scientists. Creative, persistent analysts find ways to get access to at least some of this data but doing that efficiently in a way that is also standardized and centralized for everyone on the team is difficult.
Introduction: Prediction is a tricky business. You have to step outside of your comfort zone, your fainted vision of the world and see it thorough across all possible dimensions. In this series, we will discuss the future of “AI”, applications that are yet unexplored.
Introduction: Humans are wired to make tough decisions bringing all the context and principles to bear. Similarly, can devices apply the available information to make the right judgment calls? In this series, we shall discuss some ethical dilemmas faced by emerging technologies.