Deep Learning Basics
While reading Hung Lee’s Recruiting Brainfood, I stumbled upon this deep learning primer:
The primer is great, and a quick read. Here is my quick summary below:
The basics of deep learning is to think about how the brain breaks up a specific task. For example, let’s say you are hiking the Appalachian Trail, and you see something in the distance running towards you. First, you might notice it is moving. Then, you might notice what shape it is. Then, you might notice how fast it is going. Then, you might notice a big snout.
Inputs > Algorithm > Prediction > Training:
The following are the key concepts for thinking about deep learning concepts. Yes, this is overly simplified, but it is still a helpful start.
- Inputs: Labels/Images
- Levels of Abstraction 1: Is this a shape?
- Level of Abstraction 2: Is this shape an ear?
- Level of Abstraction 3: Is this a cat?
- Prediction = Yes or No. Is this prediction correct?
Current-State of Deep Learning:
- Supervised Deep Learning: In effect, this is attempting to clone human behavior via labeled images, video, text or speech.
- Reinforcement Learning: This is where the model attempts to “learn” behaviors, codify those behaviors (i.e. what does that mean), and then implement strategies to optimize based on those strategies. As the article suggests, the following are some examples:
- E-Commerce: model learns customer behaviors and tailors service to suit customer interests.
- Finance: model learns market behavior and generates trading strategies.
- Robots: model learns how physical world behaves (through video) and then navigates that world.
Network Architecture to Detect Objects in Images:
- Input: Image
- Extract Feature: Extract the specific features
- Classification: Classify based on the probability of those features
- Output: Image prediction
Enjoy your deep learning explorations!
Originally published at Karma Advisory.