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What is deep learning?

Deep learning is a sophisticated type of machine learning dedicated to training computers to discern information from complex data sources, such as images and videos. Deep learning models are created through the use of complex, multi-layered networks that allow data to be passed between nodes in non-linear ways. These models require the use of massive volumes of data to train but require little human intervention once created.

What is an example of deep learning?
Answer Part 1

Some examples of deep learning include virtual assistants, self-driving cars and facial recognition software.

Answer Part 2

Deep learning is a subfield of machine learning that utilizes algorithms to abstractly mimic the structure and general functionality of the human brain, building what are known as artificial neural networks to transmit information in non-linear ways. Deep learning relies on the input of vast quantities of data in order to begin building knowledge within a system and training it on proper decision making while it scales. 

Though artificial neural networks require heavy training to provide value once implemented, the technology offers bounteous potential with little human intervention required, especially when it comes to analyzing the ever-growing quantity of raw, unlabeled data in the world. This is especially useful when it comes to analyzing data from non-tabular based sources, such as video and image content. Some deep learning examples include: 

  • Fraud detection software, such as programs that monitor credit card purchases to detect unauthorized usage 
  • Self-driving cars
  • Facial recognition software
  • Natural language processing that can translate text or power chatbots
  • Recommendation engines such as Netflix, which uncover patterns in a user’s viewing behavior and recommend new shows to watch
Is LSTM deep learning?
Answer Part 1

Long Short-Term Memory (LSTM) is a category of neural network that allows computers to learn order dependence in sequence prediction.

Answer Part 2

Long Short-Term Memory (LTSM) is a type of recurrent neural network deep learning that trains computers to be able to learn order dependence in problems that require sequence prediction. 

LTSMs work by creating repeating modules with interacting layers to add temporal dependencies into the equation and allow every element of an image or other data source to be analyzed. This is particularly useful when determining similarities and differences between large batches of data in order to produce a more nuanced output for each individual data point.

What is deep learning vs machine learning?
Answer Part 1

Deep learning is a subcategory of both machine learning and artificial intelligence that is unique in its ability to handle analog inputs and outputs.

Answer Part 2

Where deep learning excels over machine learning comes largely in its ability to understand data that does not come in tabular form, such as pixel data, text documents and audio files. One type of deep learning that is concerned entirely with images is known as a convolutional neural network, which applies weight-based filters to every element in an image to allow the computer to understand and react to the picture. Another form of deep learning is a recurrent neural network, which incorporates memory in order to keep past decision points in mind when reviewing recurring data.

Other ways in which deep learning differs from machine learning is in deep learning’s lack of necessary ongoing human intervention, need for more intense hardware such as powerful graphical processing units, time required to set up, efficiency in producing instantaneous results, use of unstructured data and use in complex autonomous programs.

Courses

Deep Learning Courses to Boost Your Skill Set

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Increase your deep learning capabilities and learn other in-demand skills through one of Udemy’s top-rated data science courses.

Topic: 

Neural Networks for Computer Vision, Time Series Forecasting, NLP, GANs, Reinforcement Learning, and More!

 

What you'll learn

  • Artificial Neural Networks (ANNs…

4.6
(6416)

Topic: 

Data science, machine learning, and artificial intelligence in Python for students and professionals

 

What you'll learn

  • program logistic regression from scratch…

4.7
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Topic: 

Learn to create Deep Learning Algorithms in Python from two Machine Learning & Data Science experts. Templates included.

 

What you'll learn

  • Understand the intuition behind…

4.6
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Topic: 

Master deep learning in PyTorch using an experimental scientific approach, with lots of examples and practice problems.

 

What you'll learn

  • The theory and math underlying deep…

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Certifications

Deep Learning & Data Science Certifications to Expand Your Possibilities

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Broaden your career’s horizons by earning a data science certification from Udacity.

Become an expert in neural networks, and learn to implement them using the deep learning framework PyTorch. Build convolutional networks for image recognition, recurrent networks for sequence generation, generative adversarial networks for image generation, and learn how to deploy models accessible from a website.

Intermediate
4 months
12 hours

You’ll master the skills necessary to become a successful Data Scientist. You’ll work on projects designed by industry experts, and learn to run data pipelines, design experiments, build recommendation systems, and deploy solutions to the cloud.

Advanced
4 months
10 hours
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