Learning Objectives

• What and why Deep Learning?
• Machine Learning vs. Deep Learning
• Deep Learning Applications
• Deep Learning Frameworks

Some Cool Deep Learning Applications

Flappy Bird

• Below, we see a deep learning agent playing Flappy Bird!
• The agent can play without being told any information about the game's structure or rules.
• It automatically discovers the game's rules by determining how well it did each time. This approach is called Deep Reinforcement Learning.

Increasing the pixels of a blurry image

• Increase the resolution of a blurry image through deep learning.

LipNet

• Lip reading performed more accurately than humans.

Why Deep Learning?

• Older machine learning algorithms typically plateau (become constant) in performance after they reach a threshold of data.
• Deep learning is a one-of-a-kind algorithm whose performance continues to improve because the more the data is fed, the more the classifier is trained, resulting in outperforming the traditional models/ algorithms.

Deep Learning - An Analogy

Look at how a child learns language:

You show an apple to a kid and say, "this is an apple." When you repeat it 20 times, a connection is established in its brain, and it can now recognize apples. What's important is at the beginning, it can not differentiate small details. A small ball in your hand is going to be an apple because it follows the same pattern (small, round, red/green). Only an apple is rooted in a little brain. The child points at an object and says, 'apple.' The child's parent immediately provides feedback: 'Right' or 'No, that's a ball".

After enough feedback, the child eventually forms an internal mental model of how to label different objects in the world.

What is Deep Learning?

A Deep Learning algorithm can learn hidden patterns from the data alone (without human supervision), combine them, and build much more efficient decision rules.

Now, what does learning hidden patterns mean? Taking the same apple example, it means that:

• A deep learning model first identifies the low-level features like the edges of an apple
• Then it slowly understands the more complex features like body and stem.
• Finally, it combines all the learnings and learns the shapes, colors, and other characteristics that can be used to identify an apple.

The further you advance into it, the more complex the features it can recognize.

Machine Learning vs. Deep Learning

In practical terms, deep learning is just a subset of machine learning.

Technically, deep learning is the same as machine learning and functions similarly (hence, the terms are sometimes loosely interchanged). However, its capabilities are different.

If you've worked with Machine Learning in the past, you might have heard about Feature Extraction. What is it?

Feature Extraction means extracting the useful features of a dataset. For example, the stem and body of an apple are features that help us recognize it.

• In traditional Machine learning techniques, most of the features need to be identified by a domain expert to reduce the complexity of the data and make patterns more visible for learning algorithms to work.
  Unfortunately, feature extraction is a separate and often a manual component in the machine learning pipeline and is time-consuming.
• The biggest advantage of Deep Learning algorithms is that they try to learn features from data incrementally. As discussed, the model will first learn the basic constituents/low-level features before moving on to the high-level ones, i.e., it learns on ITS OWN!

This eliminates the need for domain expertise for performing feature extraction.

Have a look at the next figure to understand this better.

Why is Deep Learning so popular?

In 2012, deep neural networks began to outperform traditional classification algorithms, including machine learning algorithms.

This was primarily due to the increased performance of computer processors (CPUs & GPUs) and larger storage media for retaining massive training datasets. Every year since, deep learning has improved, becoming state-of-the-art for solving problems in many different domains. This explosion in deep learning is largely thanks to improvements in hardware and massive labeled data sets that allow deep learning models to improve quickly over time.

Google trend for the keyword "Deep Learning":

Deep Learning Applications in Daily Lives

Social Media

One of the most common applications of Deep Learning is Automatic Friend Tagging Suggestions on Facebook or any other social media platform.

Facebook uses face detection and image recognition to automatically find and match a person's face from its Database and hence suggests tagging that person based on DeepFace.

Virtual Personal Assistants

Have you ever thought about what lies behind the functioning of Google Assistant, Siri, or Alexa?

As the name suggests, Virtual Personal Assistants assist in finding helpful information when asked via text or voice. All you need to do is ask a simple question like "What is my schedule for tomorrow?" or "Show me my upcoming Flights ".

Recently, personal assistants are being used in Chatbots, which are being implemented in various food ordering apps, online training websites, and also in Commuting apps. It is even used for shopping purposes.

Healthcare

Deep learning allows the healthcare industry to analyze data at exceptional speeds without compromising on accuracy.

The technology has been applied across several other areas over the years. These include AI-powered chatbots that can identify patterns in patient symptoms, deep learning algorithms designed to identify specific cancers, pathology, and the identification of rare diseases.

Within each of these areas, deep learning plays a fundamental role in providing medical professionals with insights that allow them to identify issues early and provide highly personalized and relevant patient care.

Other Applications

Apart from the ones you just saw, the number of deep learning applications in our daily lives is immense.

Can you think about more such applications?

Add them here.

Deep Learning Frameworks

Both TensorFlow and PyTorch have their advantages as starting platforms to get into neural network programming.

Traditionally, researchers and Python enthusiasts have preferred PyTorch, while TensorFlow has long been the favored option for building large-scale deep learning models for use in production. However, the latest releases have seen the two libraries converge towards a similar profile. As long as you stick to either TensorFlow or PyTorch as your deep learning framework, you can do nothing wrong.

Slide Download Link: You can download the slides here.

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