Deep Learning

ELI5 (Explain like I'm 5)

Imagine you’re learning to read.

At first, you don’t understand words. You just learn to recognize letters. 

Then you start recognizing small words.

Later, you understand sentences.

Eventually, you can understand a whole story.

Deep learning works the same way.

The computer doesn’t understand everything at once. It learns simple things first, then combines them step by step to understand more complex things. Each layer helps it see the data a little more clearly than before.

How Deep Learning Works

Deep learning models train using backpropagation and gradient-based optimization using:

• Labeled data (supervised learning)
• Unlabeled data (unsupervised or self-supervised learning)
• Feedback signals (reinforcement learning)

During training, the model:

• Makes a prediction
• Measures how wrong it was 
• Adjusts internal weights
• Repeats this process multiple times

This gradual adjustment is what allows the network to improve.

Standard Machine Learning Vs. Deep Learning

In standard machine learning, programmers often have to tell the software which features to look for, such as the specific edges of an object or the frequency of a sound.

Deep learning removes this manual step. The algorithm performs the feature extraction automatically and discovers patterns on its own. Therefore, it can handle unstructured data like images, video, and natural language much better than standard machine learning. 

Common Applications of Deep Learning

Common deep learning applications include:

• Computer vision: object detection, medical imaging, quality inspection
• NLP: translation, summarization, search, chatbots
• Speech: transcription, voice assistants
• Recommendations: ranking content/products based on behavior

Limitations of Deep Learning

Deep Learning requires two main ingredients to be effective: massive datasets and high-performance hardware. Therefore it is expensive to train and vulnerable to biases in the training data.