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.
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.
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
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.
There are lots of reasons to learn mathematical notation. Maybe you just want to stretch yourself and learn a new skill? Learning something outside of your comfort zone is a fantastic way to keep your mind sharp. Maybe the greatest reasons to learn math notation is that it lets you express complex ideas in a very compact way. Without it, it would take pages and pages to explain every equation. Yet even with all the resources out there, it can still be intimidating to face a string of those alien characters. Have no fear.
Let you set out to uncover what insights NLP could give you about your own area of mastery. Had NLP uncovered the hidden keys to writing heart-wrenching poems? There’s a rhythm to language. Words can spark fiery images in your mind. They can overwhelm you with emotion, making you break down with tears or get you quivering with anticipation. They create sound and fury, movement and feeling. Can a machine do all of that?
AI is already radically changing the world. In the short term, the promise and peril of AI is legion. AI will deliver some of our brightest fantasies and our darkest nightmares. Why both? Because AI is a universal technology. It’s flexible enough to do whatever we want it to do. And that means it will reflect the good and evil of its creators: Us. Let’s dive in and take a look at how AI will change society in the next few years, and by the time you’re old and grey, and when you’re long since turned to dust.
AI created an explosion of new jobs the likes of which the world had never seen. First came the destruction but then came the creation. New jobs we couldn’t imagine before the old ones disappeared started to flourish. People worked side by side with AI, as politicians and big companies and decentralized autonomous organizations (DAOs) learned how to balance the needs of man and machine. In just fifty years we saw the mustard seeds of the dawn of the Age of Intelligence grow into a wild and uncontrolled forest.
The rise of AI taught us that humans are nothing but another input and output. Words and emotions are the programming language and the machines can program us with ease. We once imagined we were the only masters of our destiny and the sole captain of our ships. Our minds were made up by our own free will. But we know it’s not true now. As we untangled the inner mysteries of our minds’ complex electrochemical field we realized we were just another kind of artificial intelligence, one evolved through the great genetic algorithms of the Earth’s biome.