/ Data Science Projects

# An Intro to Deep Learning in Python

Deep learning is a type of machine learning that’s growing at an almost frightening pace. Nearly every projection has the deep learning industry expanding massively over the next decade. This market research report, for example, expects deep learning to grow 71x in the US and more than that globally over the next ten years. There’s never been a better time than now to get started.

To make that start easier for you, we’ve just launched a new course: Deep Learning Fundamentals.

This course is designed to give you an introduction to neural networks and deep learning. You’ll start with the theories behind these concepts, and gen familiar with representing linear and logistic regression models as graphs. Then you’ll start digging deeper into topics like nonlinear activation functions and work on improving your models by adding hidden layers of neurons, using the scikit-learn package in Python. Finally, you’ll build a deep learning model that’s capable of looking at images of handwritten numbers and identifying/classifying them correctly.

## Why you should dive into deep learning

Although salaries for general data scientists are already excellent, as specialists, machine learning and deep learning engineers can command even higher rates. According to Indeed.com data from the US, for example, machine learning engineer salaries average around 13% higher than data scientist salaries.

Having some deep learning skills can also help your resume stand out from the herd when it comes to applying for data science jobs, even if you haven’t yet reached the level of deep learning specialist.

### Demand for deep learning is growing

There’s no doubt that machine learning is a fast-growing field, and within it, deep learning is also growing at a breakneck pace. Specific market projections vary from firm to firm to firm, but everybody agrees on the general trend: demand for deep learning is headed through the roof.

### It saves time

If you’ve messed with other forms of machine learning, you know that feature engineering - converting your input’s parameters into “features” your algorithm can read - can be a fairly difficult and time-intensive process. But the neural networks used in deep learning are designed to do that conversion automatically. So, for example, instead of having to figure out how to pull color data, histograms, and other metrics from a set of images, you can just run the raw images through your neural network and it will do the work for you!

That’s making it sound easy, of course, and it isn’t; the challenge is getting the network to the point where it’s capable of doing that work for you. But that means you’ll spend more time working with your algorithms and less time fiddling with features.

### Specialize while staying flexible

Building a specialty can help you find work in any field, but it can also put you into a position where you’re doomed to be doing the same thing every day because your speciality is only appealing to a limited number of companies who are all doing the same sort of thing. Thankfully, that’s not the case with deep learning, which is in demand across a wide swath of industries and is being put to use to solve problems ranging from image recognition to translation to robotics.

### It’s fun!

Career advantages aside, let’s not forget that deep learning is just plain cool. You can use it to get machines to do everything, from automatically colorizing old photos to destroying the world’s greatest chess players without actually teaching them how to do those things.

Ready to dive into the deep? The first mission of the new course is completely free so everybody can try it out, but you will need a Premium subscription to complete the course.

#### Charlie Custer

Charlie is a content marketer at Dataquest.