- Differentiate between perceptrons and sigmold neurons.
- Describe the three types of layers of a neural network.
- Identify the purpose of weights.
- Recognize the steps for initializing a neural network.
- Explain how back propagation improves accuracy.
- Evaluate the effectiveness of supervised and unsupervised learning methods in a given situation.
Skill Level Beginner
- Humans are amazing at seeing different patterns. Most of us can quickly identify images of dogs, cats, balloons, and mountains. We can identify different handwriting. Some people even speak many different languages. These skills are so second nature that you don't even think about them, yet these same skills are extremely difficult to teach to machines. Imagine if you could create a machine that can translate all the world's languages or find any photograph on the internet just by asking.
Think about a machine that can identify faces, look through patient x-rays, or even find new pharmaceuticals. To do that, you can create a network that works like a human brain. In a sense, you want to create a machine that's more like us as a way to better understand us. This course gives you a broad overview of the different technologies around artificial neural networks. You'll see how computer scientists use programs and algorithms to mimic biological neurons.
Then, you'll see how to tweak the connections between these neurons to help your network learn and adapt. Finally, you'll see how you can use this technology to classify and find patterns in massive data sets. This course is designed for managers, entrepreneurs, students, or business professionals who want to better understand artificial neural networks. You'll see how neural networks fit under the larger umbrella of machine learning and artificial intelligence.
That way you can think about how you can use your neural network for your product, project, or even as a starting point for your career. Let's get that human brain ready to learn more about artificial neural networks.