The future of business is in machine learning. The applications of this technology run deep within the world of technology. From reinforcement learning to image classification to market predictions, there’s almost no realm of business that machine learning hasn’t or won’t touch. If your company isn’t already in the business of developing with and for machine learning, you are already behind the growth curve.
Machine learning can help businesses:
- Develop software capable of understanding human language.
- Improve the efficiency of logistics.
- Use preventative maintenance to prevent equipment breakdown and bottlenecks.
- Leverage consumer data to build profiles for consumers, trends, and predictions and improve brand loyalty.
And that’s just the short list of what machine learning can do for your company. With that in mind, your developers need to start heavily investing in programming for machine learning. That translates to the need for frameworks designed specifically for the task. Fortunately, there are plenty of frameworks for developing machine learning solutions.
Let’s take a look at some of the more popular machine learning frameworks on the market.
TensorFlow was developed by Google and has become the framework to use for machine learning. In fact, TensorFlow has become the de facto standard for today’s market. And because TensorFlow is free, and runs on both CPUs and GPUs, it’s even more attractive to more companies looking to leverage machine learning.
But don’t think you can just throw TensorFlow at your current developers and have them start implementing machine learning right away. This tool can be very challenging. Developers will need to have a solid understanding of both Python and C, which are the 2 languages that have stable and official TensorFlow APIs.
Although this full-blown machine learning research and production tool can be a bit intimidating at first, it’s possible to create simple predictions on data sets. But to get the most out of TensorFlow, you’re going to need experience.
What can you do with TensorFlow? This machine learning framework can be applied to use cases like:
- Voice and sound recognition
- Text-based applications for things like threat detection and sentiment analysis.
- Image recognition
- Analyzing time-series data
- Video detection
- Self-driving vehicles
- Flaw detection
- Air, land, and sea drones
- Mobile image and video processing
To find out more about TensorFlow check out What can TensorFlow Do for Your Company?
PyTorch was created by Facebook AI Research (FAIR) to serve as a leading competitor to TensorFlow. PyTorch immediately took off and has become one of the most popular machine learning frameworks on the market. When deciding on an ML framework, the choice generally comes down to either TensorFlow or PyTorch.
Like TensorFlow, PyTorch can run on both CPUs and GPUs. However, where TensorFlow can make it possible to deploy a new model with incredible speed, PyTorch offers far more customization by following a traditional Object-Oriented Programming approach.
PyTorch also has some of the fastest training times of all machine learning frameworks. Although the speed might seem insignificant on a project-by-project basis when those projects scale to enterprise proportions (such as when using massive data), the speed becomes consequential.
In order for your developers to make use of PyTorch, they’ll need to understand Python. Once up to speed, your developers can use PyTorch for such things as:
- Implement network architectures like RNN, CNN, LSTM.
- Building deep learning libraries.
- Automatic calculation of differentiation.
PyTorch is primarily used for research, which makes it a great fit for businesses needing deep dives into data, which can result in detailed analysis.
Some of the benefits of PyTorch include:
- Building computational graphs and changing them on the fly.
- Running ML models in batches, in parallel, or on multi-GPU setups.
- Integrating it with NumPy testing tools for enhanced unit testing.
- Transferring computations before GPU and CPU.
- Shrinking images for increased performance.
- Easy debugging.
- Dynamic computational graph support.
- Cloud support.
Keras is built on top of TensorFlow and provides a Python interface to make machine learning modeling a bit easier. By simplifying a number of the steps (such as offering all-in-one models), Keras can work with the same code on either CPUs or GPUs. Keras also includes several commonly-used neural-network building blocks, such as:
- Activation functions
All of this put together makes Keras considerably easier to use for working with both text and images. Of course, Keras isn’t limited to neural networks and can work with convolution, recurrent neural networks, dropout, batch normalization, and pooling. Keras can be used for machine learning models on iOS, Android, the web, or even within a Java Virtual Machine.
Keras was created using 4 guiding principles:
- Modularity – All concerns of a deep learning module are discrete components and can be combined in arbitrary ways.
- Minimalism – Keras provides just enough to achieve the stated outcome.
- Extensibility – New components can be easily added.
- Python – Everything must be native to Python.
Because Keras is a minimalist Python library (that can run on top of TensorFlow), it’s important that your developers have a deep understanding of Python and TensorFlow, as well as how deep learning research works (and how it can be applied to your business).
To learn more about Keras, make sure to read the official guides.
There are quite a few machine learning frameworks available. If your developers start with one of these three, there’s almost no limit to how they can add machine learning into the technology that drives your business. By employing machine learning, you’ll be able to better leverage data, predict trends, and more easily interact with your customers and clients.