Deep Learning Vs Machine Learning

Deep learning is the foundation for developing AI robots. it attains recognition accuracy at top levels than ever before. In this blog, we will cover everything about Deep Learning (DL) and Machine Learning (ML) which is a hot buzz nowadays.

Topics covered:

1. What Is Deep Learning?
2. Why Deep Learning?
3. How Deep Learning Works?
4. What Is Machine Learning?
5. Why Machine Learning?
6. When You Go for Machine Learning?
7. How Does Machine Learning Works?
8. What’s the Difference Between Deep Learning and Machine Learning?
9. Choosing Between Deep Learning and Machine Learning.
10. Real-Time Use Cases of Deep Learning.

What Is Deep Learning?

• Deep learning is a subset of machine learning that train computer to do what comes naturally to humans: learn by example.
• Behind driverless cars research, and recognize a stop sign, voice control in devices in our home. DL is a key technology.
• In DL, we trained our model to perform classification tasks directly from text, images, or sound.
• Sometimes deep learning models exceeding human-level performance.
• Models are learned from a large set of labeled data and artificial neural network architectures that contain many layers.

Why Deep Learning?

• DL earns recognition accuracy at top levels than ever before.
• It is used in safety-critical applications like driverless cars.
• It came in the 1980s, but due to a lack of labeled data and computing power, it was not popular at that time.
• Today, we have large amounts of labeled data. For example, a driverless car requires millions of images and thousands of hours of video to train with high accuracy.
• For computing power, we have high-performance GPUs, cloud computing that is efficient for deep learning.

How Deep Learning Works?

• Deep learning work on neural network architectures.
• The number of hidden layers in the neural network usually refers to “deep”. Hidden layers in deep neural networks can have as many as 150.
• These models are trained by using large sets of labeled data & and neural networks learn features directly from the data.
• Convolutional Neural Networks is one of the most popular types of deep neural networks.
• CNN’s eliminates the manual work of feature extraction; it works by extracting features directly from images.

What Is Machine Learning?

• Machine learning is a data analytics technique that learns from experience.
• Machine learning algorithms directly learn from data without relying on a predetermined equation.

Why Machine Learning?

• With the rise in big data daily from IoT devices, machine learning has played a very important role in solving problems in areas, such as:
  • Computer vision
  • Computational finance
  • Energy production
  • Aerospace, and Manufacturing
  • Natural language processing

When You Go For Machine Learning?

• Go for machine learning when you have a complex task or problem involving a Big data and lots of variables, but you don’t know the formula or equation.

How Does Machine Learning Works?

• ML uses two types of techniques: supervised learning, and unsupervised learning.
• supervised learning trains a model on labeled data so that it can predict future outputs.
• unsupervised learning trains a model on labeled data & finds hidden patterns in input data.

What’s the Difference Between Deep Learning and Machine Learning?

• DL is a particular form of machine learning.
• A machine learning pipeline begins with relevant features manually pulling from images. The features are then used to develop a model that classifies the objects in the image.
• With a DL pipeline, suitable features are automatically extracted from images.
• Deep learning implements “end-to-end learning”. where a neural network is given raw data and a task to do classification, and it learns how to do this automatically.
• DL algorithms scale with data, whereas machine learning plateau at a certain level of performance when we add more data.

Choosing Between Deep Learning and Machine Learning

• When choosing between deep learning and machine learning, consider whether you have lots of labeled data and a high-performance GPU.
• If you don’t have these two things, then go for machine learning instead of DL.
• DL is usually a more complex and high-performance GPU to analyze all images.

Real-Time Use Cases Of Deep Learning

• Autonomous Car: self-driving car researchers are using DL to automatically detect objects without human input such as traffic lights and stop signs.
• Defense and Aerospace sector: DL is used to detect objects from satellites that locate areas of interest, and detect safe or unsafe zones for troops.
• Industrial Automation: DL automatically detecting worker safety around heavy machinery.
• Medical Research: DL automatically detects cancer cells. High-dimensional data set used to train a DL application to exactly identify cancer cells.
• Electronics: Home assistance devices that reply to our voice and know our preferences are powered by DL applications.

Next Task For You

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