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Deep Learning with Python

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Technology is driving change across many industries, with deep learning and Python at the forefront of this progress. From medical diagnostics to self-driving cars, deep learning has shown to be truly powerful and versatile. But did you know that most deep learning models are built with Python?

Read on to learn more about deep learning and why Python is the perfect choice for your deep learning projects.

What Is Deep Learning?

Deep learning refers to a set of algorithms that allow computers to behave intelligently. It’s a subset of machine learning, a broader area of computer science that enables machines to learn independently without any explicit programming. The main difference between machine learning and deep learning is that the latter always employs some kind of artificial neural network (ANN).

Artificial neural networks are mathematical models inspired by the human brain. They mimic how stimuli traverse biological neural networks, and how the networks transmit electrical signals from one neuron to another. ANNs have fewer neurons than biological systems, and they constrain the neurons to operate synchronously. But these limitations are necessary because building a replica of a biological neural network would be too computationally complex.

The “deep” in “deep learning” refers to the layers of neurons that are stacked on top of one another. An ANN with more than three layers is considered to be deep—yet many go much deeper. A popular language model called BERT consists of 24 layers, and another well-known computer vision model called ResNet-152 is 152 layers deep!

Deep Learning Applications

Deep learning’s capabilities are well-documented. But have you heard about deep learning’s impact on unstructured data? This is the type of data that typically does not adhere to any data model. Examples include genomic sequences, groups of pixels, and audio frequencies. 

In contrast to unstructured data, some data contains clear patterns. Spreadsheets are an example of structured data, where a sheet’s columns could represent properties like a person’s age and annual income, and each row could describe a different set of characteristics. 

The reason deep learning works well with unstructured data is the ability of ANNs to independently learn important patterns. It’s easy for us to design features that describe a person, like age and income. But it’s difficult to assign meaning to a spectrogram consisting of whale calls intermixed with noises coming from other marine life and ship propellers—a prime example of unstructured data. Because deep learning can filter out the noise from the important signal with ease, it has truly revolutionized the way we study marine biology. 

Consider the joint research effort between National Oceanic and Atmospheric Association (NOAA) and Google to study the songs of humpback whales. By using deep learning, they can get better insights into the whales’ population structure and migration patterns. Although humpback whales have bounced back since the international ban on whaling, they are still under threat. With the help of deep learning, scientists can now tackle whale endangerment by prohibiting travel to certain marine areas, or by warning ships about the presence of whales.

Another example of using deep learning to tackle environmental challenges comes from Stanford’s machine learning group. They used Python to create a deep learning model called ForestNet, and employed satellite imagery from Google Earth to detect the drivers of deforestation. Models like ForestNet are being used to help preserve green spaces, prevent loss of biodiversity, and slow down climate change.

Deep learning has no shortage of applications. If you have enough data, there’s a good chance you can use it to create a powerful deep learning model. Discover more deep learning applications in our article “Examples of the Most Advanced AI.”

Deep Learning With Python

Any discussion about deep learning is incomplete without Python. Indeed, most deep learning specialists will agree that Python is the best programming language for deep learning. Here are some reasons why: 

  • Python is as high-level as it gets. When we describe programming languages as “low” or “high” level, we refer to how many levels of abstraction they introduce on top of machine code, which are the raw instructions that computers execute. As a high-level language, Python spares deep learning experts the headache of low-level details like managing computer memory, and allows them to focus on building powerful models.
  • Python integrates well with a wide range of technologies. Data scientists often need to perform processor-intensive data transformations, move data from one server to another, use a GPU, or train a deep learning model on the cloud—Python’s versatility makes this a breeze.
  • Python’s deep learning ecosystem is unmatched by any other language. Two of the most popular libraries, PyTorch and TensorFlow, have fully matured since their release over five years ago. Recent years have seen new additions like DeepMind’s JAX library aimed at researchers, and libraries like HuggingFace that bring state-of-the-art natural language processing models to the fingertips of any developer.

If you’re looking to start training deep learning models with Python but are unsure how to begin, look no further! We’ve already discussed the differences between deep learning libraries PyTorch and TensorFlow. And if you’re looking for a beginner’s project, check out our TensorFlow object detection tutorial.

The Future of Deep Learning

Deep learning is currently performed on digital computers with electronic processors. But researchers are now looking into different ways of conducting deep learning computations.

A team of MIT researchers has recently published work on training neural networks with optics and processors that use photons. The technology is based on the principles of an optical device called a beam splitter. By employing clever tricks for manipulating light, they’ve managed to perform matrix multiplication—a linear algebraic operation that neural networks rely on. They estimate that using this technology for deep learning could be orders of magnitude more efficient than traditional processors.

Start Building Your Own Deep Learning Models

In this article, we looked at deep learning and explained why Python is the best programming language in this exciting space. We also covered a few applications showcasing deep learning’s potential, including wildlife conservation and environmental management. 

If you’re interested in a career in deep learning but need to brush up on your programming, check out our Introduction to Programming Nanodegree. This program is your first step towards your dream job. 

If you already have a basic working knowledge of Python, check out our Deep Learning Nanodegree instead. In this program you’ll build and apply your own deep neural networks to real-world challenges like time-series prediction and model deployment.

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