Understanding the Learning Rate in Neural Networks

When designing an artificial neural network, your algorithm “learns” from each training iteration, refining internal settings until it finds the best configuration. To maximize this learning process, you can set something known as the “learning rate,” which determines how quickly your model adapts after each run-through with the training data. By understanding what a learning rate is and different approaches, you can improve both the speed and accuracy of your machine learning model.

What is a learning rate?

In machine learning, the learning rate determines the pace at which your model adjusts its parameters in response to the error from each training example. A “parameter” is the internal value in your model that the algorithm adjusts to refine predictions and outputs. You can think of this as the “settings” of your model. When you optimize your settings, your model is more likely to respond to data inputs in a way that aligns with your goals. 

For example, imagine you’re a soccer player trying to shoot a goal from a certain angle. The first time you kick, the ball goes 10 feet too far to the left and 5 feet too high. You then adjust your aim, power, and angle, and try again. This time, the ball only goes 5 feet too far to the left and 1 foot too high. You repeat this adjustment process until the ball goes right into the goal at the placement you want. In this case, your aim, power, and angle are your parameters. Your learning rate is the size of the adjustment you make after each trial. If your adjustments are too big, you risk overcorrecting, while if your adjustments are too small, you may take a long time to reach your goal.

Types of learning rates

Your learning rate directly impacts the efficiency and efficacy of your model’s learning process. If set correctly, the learning rate should allow your model to make steady, effective progress toward the optimal solution. You can take several approaches to this, and deciding on the right one helps you balance your time and computational resources. Learning rate styles to consider include the following.

Constant learning rate

If you choose a constant learning rate, your model will keep a fixed learning rate throughout the training process, with consistent adjustments after each step. This is the simplest learning rate scheme and is often used in practice.

Pros

• Simple to implement

• Easier to tune than dynamic learning rates

Cons

• Choosing the right value is not always straightforward

• May have lower accuracy and slower convergence than dynamic learning rates

Adaptive learning rate

When using an adaptive learning rate, your model will change the learning rate for each parameter dynamically, depending on the result of each training step. This flexibility benefits complex models with varied parameters, and you can choose between several adaptive methods such as AdaGrad, AdaDelta, RMSProp, and Adam. 

Pros

• High accuracy

• Fast convergence 

• Useful when you want to tune your parameters at different rates

• Quick adaptation to new events

Cons

• Harder to tune than constant learning rates

• May lead to overfitting

• Can be difficult to choose the right style, no consensus on the “best” adaptive algorithm 

Step decay learning rate

After a certain number of training iterations, you reduce the learning rate by a certain factor. The step size may be reduced on a schedule, such as halving every few training iterations.

Pros

• High convergence and generalization

• Certain step decay schedules perform more effectively than other learning rate designs

Cons

• Convergence quality is dependent on the step size

• May overfit

Exponential decay learning rate

Exponential decay is a type of non-linear training algorithm that reduces the learning rate exponentially as the model progresses through training. This decay usually happens after a fixed number of training iterations.

Pros

• Smooth training curve

• Improved convergence and generalization ability of training

Cons

• May be difficult to choose the right frequency of decay

• May be computationally intensive

Time-based decay learning rate

With time-based decay learning, the learning rate will decrease fastest when the training begins and slow over time. The rate of decay adjusts after each iteration through training, diminishing based on an equation involving the initial rate of learning, decay rate, and number of training iterations.

Pros

• Simpler than adaptive learning rates

• Smaller adjustments later in training allow for more precise convergence

Cons

• May not perform as smoothly as exponential decay

• May have large performance fluctuations at the beginning of training

Learning rate applications

You can use learning rates across many types of machine learning applications to optimize models and train new systems. Setting an appropriate learning rate helps your model learn effectively and improves the accuracy of outputs. A few common machine learning tasks that require the model to continually learn from new data and refine its algorithm include:

• Computer vision: Learning rates help to train deep learning models to derive insights from visual information, such as images and videos. 

• Recommendation systems: Learning rates help machine learning models understand patterns in user data and learn user preferences to recommend products and media.

• Email automation: Machine learning algorithms learn how to detect spam emails and filter them into the correct inbox. 

• Fraud monitoring: In the banking industry, machine learning algorithms learn to detect anomalies in transactions, identifying fraud quickly to protect users.

Who uses learning rates?

Professionals who work with machine learning and artificial intelligence models use learning rates to ensure the algorithms perform optimally. For example, machine learning engineers design algorithms that identify trends in data and learn from these patterns. Researchers in industry and academia experiment with different learning rate algorithms to improve artificial neural network designs that can be used for a variety of fields such as computational biology, finance, and technology development.

How to start building machine learning skills

To build a foundation in machine learning, it helps to understand key concepts and gradually build hands-on experience, especially when it comes to parameter tuning, learning rates, and model optimization. You can start this journey with a few simple steps:

1. Study machine learning basics, such as algorithm development, model training, and parameter adjustments.

2. Learn the basics of a programming language, such as Python or R. With these languages, you can explore built-in packages that simplify machine learning algorithm building.

3. Practice optimizing your model. Once you learn how to build a basic model, you can experiment with practice data sets and guided projects to apply different learning rates and explore their effect on your model. 

Explore machine learning on Coursera

In machine learning, a learning rate refers to how quickly your model adapts based on new information and the way in which it weighs new information compared to older information. Learning rates are only one component of artificial neural networks, meaning it’s important to explore other facets of this field to build a comprehensive understanding. On Coursera, you can complete the Deep Learning Specialization, which includes specialized courses on Neural Networks and Deep Learning. This flexible learning path is designed for you to complete in three months, helping you learn how to build, train, and optimize your models.