Deep learning, AI, and neural networks are some of the hottest topics in tech right now – and for good reason. These technologies have the potential to revolutionize the way we live and work!
If you want to learn about deep learning, you’ve come to the right place. In this article, we’ll show you how to learn deep learning in nine different ways and answer some commonly asked questions on the topic.
What Is Deep Learning?
Deep learning is an element of data science that focuses on using artificial intelligence (AI) and machine learning (ML) to imitate how humans gain knowledge. It enables computers to learn independently by increasing their ability to find patterns.
Deep learning is based on a neural network, a type of machine learning inspired by the way the human brain works. Neural networks consist of a series of interconnected nodes, or neurons, similar to how nerve cells connect in the brain. These nodes work together to process information similarly to the human brain.
Deep learning algorithms learn and improve from experience automatically. They can make predictions based on data, such as images, sound, and text. For example, a deep learning algorithm can identify objects in pictures or recognize spoken words.
Why Learn Deep Learning?
There are tons of reasons to get into deep learning:
- It’s a hot and in-demand field
- It can lead to better job prospects and higher salaries
- It helps you better understand how AI works
- You can use it to solve complex real-world problems
How to Learn Deep Learning (9 Easy Ways)
Deep learning is an incredibly valuable skill, and there are plenty of ways to get started. The following are nine of the easiest ways:
1. Enroll in a Data Science Bootcamp
Data science bootcamps are professional courses designed to teach students the ins and outs of complex computing. Coding Dojo’s Data Science Bootcamp encompasses a range of important disciplines, including deep learning which is covered in Level Two.
What You’ll Learn in Level Two:
- How Deep Learning Has Transformed Industries
- Various Deep Learning Frameworks
- Sequential Artificial Networks
- Deep Learning Regularization
- When to Use Deep Learning Techniques
2. Take a Free Python Course
If you’re not ready for a big commitment just yet, free courses are an easy way to start dabbling in deep learning. With no barrier to entry and a world of topics to explore, free courses give you the opportunity to sample what you’re interested in before taking the plunge.
If you’re looking to build skills relevant to deep learning, consider Coding Dojo’s free Intro to Python Course.
What You’ll Learn:
- Python Coding Basics
- Python Libraries
- Variables, Data Types, Lists, and Conditional Logic
- How to Build a Game and Play vs Computer
3. Watch Deep Learning Tutorials for Beginners
Video tutorials are a fantastic way to learn complex topics without any pressure from one-on-one instruction. They can be found online and offered by independent creators and prominent organizations.
Some excellent deep learning tutorials for beginners include:
- Deep Learning Tutorial for Beginners (Simplilearn)
- Python Neural Networks for Beginners (Edureka)
- Python Machine Learning Tutorial (Data Science)
4. Read Deep Learning Books for Beginners
While they may feel outdated, books are still an excellent resource for learning new things. This is especially true when it comes to deep learning; there are countless titles on the subject, each offering a different perspective.
Here are a few of our favorites:
- Deep Learning With Python by Francois Chollet
- Grokking Deep Learning by Andrew Trask
- Deep Learning (Adaptive Computation and Machine Learning Series) by Ian Goodfellow
5. Practice With Deep Learning Projects for Beginners
You know what they say – practice makes perfect. And there’s no better way to practice deep learning than by completing projects. These can be found all over the internet, but here are some of the best:
Try these great deep learning project for beginners:
- Face Recognition Project (Github)
- Neural Network Development Project (Github)
- Music Genre Classification Project (Github)
6. Listen to Deep Learning Podcasts
If you’re not big on reading, podcasts are an excellent alternative. You can listen while commuting, working out, or even doing the dishes. They’re also a great way to get insights and perspectives from some of the brightest minds in the industry.
7. Join a Deep Learning Community or Group
Not ready for a full-on course or one-on-one instruction? Online communities are your next best resource for learning deep learning. These groups provide a great way to connect with other learners, ask questions, and get feedback on your progress. They can also be a fun and supportive way to stay motivated.
8. Practice With Deep Learning Flashcards
Flashcards are an age-old tool for mastering knowledge and can be just as effective for deep learning. Create a set of cards with key concepts, algorithms, and terms on one side and definitions on the other. Then, test yourself regularly to see how much you’ve learned. Doing so will help embed the material in your memory and make it easier to recall later.
If you’d rather a premade deck, try these:
9. Apply for a Deep Learning Internship
Once you’ve spent some time honing your skills, consider growing them further by putting them to the test in the real world. An internship is a great way to get first-hand experience working with deep learning systems and applications.
Not only will you have the opportunity to learn, but the experience you get can also give you a leg-up in the job market. Many employers prefer to hire candidates with relevant experience, and an internship can help you get your foot in the door.
Learn Deep Learning at Coding Dojo
In search of a simple, straightforward solution to beginning your journey in deep learning? Coding Dojo is your answer. Our extensive offering of courses and bootcamps are designed to help novices and veterans alike grow their skills in everything computer science.
Available online and part-time, Coding Dojo’s Data Science Bootcamp is perfect for those looking to launch a data science career. We offer the following course options:
- Data Science and Machine Learning in Python (16 Weeks)
- Data Science & Visualization (20 Weeks)
Both of these courses come with a project-based curriculum that is designed to give students practical experience working with data.
You’ll cover everything from data wrangling and visualization to machine learning and big data over the course of the program, and at the end, you’ll have a portfolio of projects to show employers as well as a certification in data science.
How to Learn Deep Learning FAQ
Do you still have questions? Check out these frequently asked questions about deep learning.
What Is Deep Learning Used For?
Deep learning is used for a variety of tasks, including image recognition, natural language processing, and time series prediction.
Does Deep Learning Require Coding?
Yes, deep learning generally requires coding. However, there are some tools that allow you to build models without having to code (e.g., Google’s TensorFlow Playground).
Deep Learning vs Machine Learning: What’s the Difference?
Deep learning is a subset of machine learning that is concerned with algorithms inspired by the structure and function of the brain. It differs from traditional machine learning in that it is able to automatically extract features from data, rather than needing humans to specify them.
Where to Learn Deep Learning?
Deep learning is best mastered through professional instruction and support. Bootcamps can be a great way to get both, and Coding Dojo’s Data Science Bootcamp is an excellent place to start.