5 Tips for Getting Started with Deep Learning

Deep learning is a subset of machine learning that has become a cornerstone in many technological breakthroughs. At the core of deep learning, it’s a model inspired by the human brain, which we call a neural network.

Contrary to the traditional machine learning model, deep learning can automatically find feature representations from data. That’s why many domains, including computer vision, speech recognition, text generation, and many more, use deep learning as their technology basis.

With how valuable deep learning is, it’s beneficial for us to learn about them further. However, I understand teaching ourselves about deep learning is a hard thing to do. So, here are five tips you could follow if you are just getting started in the Deep Learning field.

1. Don’t Skip the Machine Learning Fundamentals

One thing I noticed about machine learning beginners is that they want to jump into deep learning directly while skipping all the basics. This is not good, as deep learning is still fundamentally a machine learning model. You need to understand machine learning basics to understand the advanced concepts in deep learning.

Try to understand a few basic concepts, such as:

• Supervised vs. Unsupervised Learning
• Standard ML Algorithms like Linear Regression and Decision Tree
• Model Evaluation
• Overfitting and Underfitting

A great resource for learning these fundamentals is Deep Learning, by Ian Goodfellow, Yoshua Bengio and Aaron Courville, especially its early chapters. A free online version of the book is available here.

These fundamentals could take you far in preparing yourself to study deep learning.

2. Choose the Starting Framework

There are many selections of deep learning for developing the deep learning model. However, this abundance of choices is sometimes becoming a double-edged sword. By trying to learn every available framework simultaneously, you would know nothing. That’s why, choose one framework and stick with it initially.

A few popular Deep Learning frameworks include:

• TensorFlow: Developed by Google, which is already widely used in research and industry.
• PyTorch: Developed by Facebook, known for its low-level usage and easy-to-use.
• Keras: This framework also developed by Google which runs on top of TensorFlow and offers a more user-friendly interface than TensorFlow.

Select one framework you feel comfortable with and start learning deep learning with it.

3. Start Learning the Fundamentals of Neural Network Architectures

The base of Deep Learning is the Neural Network, so it’s intuitive that you need to understand the neural network architectures when you start deep learning. Try to understand the neural network concept and the various types they have, including:

• Feedforward Neural Networks
• Convolutional Neural Networks (CNNs)
• Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM)
• Generative Adversarial Networks (GANs)
• Transformers

The list could go on even longer, but these fundamental architectures are the most common ones you would need to know in deep learning.

Andrew Ng’s introductory deep learning specialization is often lauded as a great place to learn these basics.

4. Start Simple

I understand that we want to build a complex project to impress everyone. However, starting with a complicated project would only become counter-intuitive as it would leave you with too many questions rather than clarity. Deep learning is already complex, so let’s start simple initially to grasp the fundamentals before moving to a much more complex project.

There are many datasets and projects you can try to start simple. For example, the MNIST Dataset, which contains handwritten digits for deep learning classification projects, is considered the “Hello World” of deep learning.

You can also use the CIFAR-10 for image classification projects or IMDb reviews for sentiment analysis projects with deep learning. They are the simple stuff you can start with before moving on to the more advanced projects.

5. Practice Regularly And Keep Up With The Community

Consistency is the key to everything, including understanding deep learning. You can only master deep learning if you practice regularly and keep studying until you understand the concept better. It might not take a day, but someday you will master deep learning if you maintain consistency.

For beginners and professionals alike, the community would help better when you studying deep learning. For example, participating in the Kaggle competition would give you more experience in deep learning development while still getting feedback for your project. Also, sharing your project as a written blog or GitHub repository is a better way to get feedback.

Keeping your learning consistent and active in the community is the best way to start with Deep Learning.

Conclusion

In this article, we have discussed the tips on getting started with deep learning. These tips include:

1. Don’t Skip the Machine Learning Fundamentals
2. Choose the Starting Framework
3. Start Learning the Fundamentals of Neural Network Architectures
4. Start Simple
5. Practice Regularly And Keep Up With The Community

I hope this has helped!