Deep Learning is a branch of Machine Learning. Though machine learning has various algorithms, the most powerful are the neural networks. Deep learning is the technique of building complex multi-layered neural networks. This helps us solve tough problems like image recognition, language translation, self-driving, etc.
There are tons of real-world applications of deep learning from Tesla cars to AI assistants like Siri. To build these neural networks, we use different frameworks like Tensorflow, CNTK, and MxNet. If you are new to deep learning, start here.
Without the right framework, constructing quality neural networks can be hard. With the right framework, you only have to worry about getting your hands on the right data. That doesn’t imply that knowledge of the deep learning frameworks alone is enough to make you a successful data scientist.
You need a strong foundation of the fundamental concepts to be a successful deep learning engineer. But the right framework will make your life easier.
Also, not all programming languages have their own machine learning / deep learning frameworks. This is because not all programming languages have the capacity to handle machine learning problems. Languages like Python stand out among others due to their complex data processing capability.
Let's go through some of the popular deep learning frameworks in use today. Each one comes with its own set of advantages and limitations. It is important to at least a basic understanding of these frameworks to choose the right one for your organization.
TensorFlow is the most famous deep learning library around. If you are a data scientist, you would have started with Tensorflow. It is one of the most efficient open-source libraries to work with. Google built TensorFlow to use as an internal deep learning tool before open-sourcing it. TensorFlow powers a lot of useful applications including Uber, Dropbox, and Airbnb.
Advantages of Tensorflow
- User Friendly. Easy to learn if you are familiar with Python.
- Tensorboard for monitoring and visualization. It is a great tool to see your deep learning models in action.
- Community support. Experts engineers from Google and other companies improve TensorFlow almost on a daily basis.
- You can use TensorFlow Lite to run TensorFlow models on mobile devices.
Limitations of Tensorflow
- TensorFlow is a bit slow compared to frameworks like MxNet and CNTK.
- Debugging can be challenging.
- No support for OpenCL.
Advantages of Apache MXNet
- Efficient, scalable, and fast.
- Supported by all major platforms.
- It provides GPU support, along with multi-GPU mode.
- Easy model serving and high-performance API.
Disadvantages of Apache MXNet
- Compared to TensorFlow, MXNet has a smaller open source community.
- Improvements, bug fixes, and other features take longer due to a lack of major community support.
- Despite being widely used by many organizations in the tech industry, MxNet is not as popular as Tensorflow.
Large companies usually use Microsoft Cognitive Toolkit (CNTK) to build deep learning models. Though created by Microsoft, CNTK is an open-source framework. It illustrates neural networks in the form of directed graphs by using a sequence of computational steps. CNTK is written using C++, but it supports various languages like C#, Python, C++, and Java.
Microsoft’s backing is an advantage for CNTK since Windows is the preferred operating system for enterprises. CNTK is also heavily used in the Microsoft ecosystem. Popular products that use CNTK are Xbox, Cortana, and Skype.
Advantages of Microsoft CNTK
- CNTK offers reliable and excellent performance.
- The scalability of CNTK has made it a popular choice in many enterprises.
- CNTK has numerous optimized components.
- It is easy to integrate with Apache Spark, an analytics engine for data processing.
- CNTK works well with Azure Cloud, both being backed by Microsoft.
- Resource usage and management are efficient in CNTK.
Disadvantages of Microsoft CNTK
- Minimal community support compared to Tensorflow but has a dedicated team of Microsoft engineers working full time on CNTK.
- Significant learning curve.
PyTorch is another popular deep learning framework. Facebook developed Pytorch in its AI research lab (FAIR). Pytorch has been giving a tough competition to Google’s Tensorflow.
Pytorch supports both Python and C++ to build deep learning models. Released 3 years ago, it has already been in use by companies like Salesforce, Facebook, and Twitter.
Image Recognition, Natural Language Processing, and Reinforcement Learning are some of the many areas in which PyTorch shines. It is also used in research by universities like Oxford and organizations like IBM.
PyTorch is also a great choice for creating computational graphs. Moreover, it supports cloud software development and offers useful features, tools, and libraries. It also works well with cloud platforms like AWS and Azure.
Advantages of PyTorch
- PyTorch has a user-friendly design and structure that makes constructing deep learning models transparent.
- PyTorch has useful debugging tools like PyCharm debugger.
- It contains many pre-trained models and supports distributed training.
Disadvantages of PyTorch
- PyTorch does not have interfaces for monitoring and visualization like TensorFlow.
- Comparatively, PyTorch is a new deep learning framework and currently has less community support.
DeepLearning4j is an excellent framework if your main programming language is Java. It is a commercial-grade, open-source, distributed deep-learning library. Deeplearning4j supports all major types of neural network architectures like RNNs and CNNs.
Deeplearning4j is written for Java and Scala. It also integrates well with Hadoop and Apache Spark. Deeplearning4j also has support for GPUs, making it a great choice for java based deep learning solutions.
Advantages of DeepLearning4j
- It is scalable and can easily process large amounts of data.
- Easy integration with Apache Spark.
- Excellent community support and documentation.
Disadvantages of DeepLearning4j
- Limited to the Java programming language.
- Relatively less popular compared to Tensorflow and PyTorch.
Each framework comes with its list of pros and cons. Choosing the right framework is crucial to the success of a project. You have to consider various factors like security, scalability, and performance. For enterprise-grade solutions, reliability becomes another primary contributing factor.
If you are just getting started, begin with Tensorflow. If you are building a windows based enterprise product, choose CNTK. If you prefer Java, choose DL4J.
I hope this article helps you choose the right deep learning framework for your next project. If you have any questions, let me know in the comments.