Source: Deep Learning on Medium
Deep Learning Frameworks You Need to Know in 2020
Deep learning networks have a mind-boggling ability to learn, so training these models requires massive computing power and intense amounts of data. You’ll need a framework to make that development easier. Deep learning requires massive processing power and lots of data. Because it uses unstructured, often non-text based data, you’ll need a framework that can handle the layers. Here are some great deep learning frameworks to get you started for 2020. Some have machine learning capabilities, and all have thriving communities for troubleshooting and documentation.
Google’s framework tends to make all the lists, and there’s a reason. It offers robust integrations and is currently used by many big-name players, including (allegedly) most of Google.
It’s Python-based and open source. You can get started right away, and with a dynamic community for support and updates, it remains one of the most popular machine learning and deep learning frameworks around.
Apache Spark Deep Learning Pipelines
DL Pipelines offers scalable deep learning in Python from Databricks with fewer lines of code and takes advantage of Apache’s distributed engine. Massive datasets benefit from this computing power, and it supports TensorFlow and TensorFlow backed Keras workflows.
You can turn deep learning models into SQL functions for those of you in environments where you’ll need to translate for less technical people, and it’s supported by a large number of users in a highly active community.
Keras was built on TensorFlow but manages to be simpler to use. It’s great for beginners, so if 2020 is your first real foray into deep learning, this framework could be a good option for you. The framework requires little code and offers TensorFlow backend workflows.
It’s a Python-based library designed for fast experimentation and is lightweight in terms of DL layers. The Keras interface format is now a standard in the DL world, and it supports multiple GPU training.
Microsoft Cognitive Toolkit
Microsoft’s deep learning framework offers support in Python, C++, C#, and Java. It facilitates training for voice, handwriting, and images with ease and provides scalable, optimized components.
It has limited support if you’re not in the Microsoft community, but Azure users have full, simple integration. It also has Apache Spark support and reasonable resource allocation. It also supports both convolutional and recurrent neural networks.
Even though PyTorch is still relatively new, it’s already gaining on deep learning darling, TensorFlow. It’s flexible and straightforward, with a lot of customizable pieces great for scaling your deep learning projects. As part of Python’s ecosystem, it’s heavily supported and offers dynamic community involvement.
It has a clean architecture, so if you’re new in the field, you can dip your toes into these deep learning models relatively easily. Training and developing are straightforward, and prototyping happens without a lot of fuss.
Chainer is an intuitive Python-based framework that allows you to make modifications during the runtime. It’s used primarily for things like sentiment analysis, machine translation, and speech recognition.
It has automatic differentiation APIs and object-oriented high-level APIs for training neural networks. It aims for flexibility and supports multi-GPU computation.
Apache offers a multi-language framework (Java, R, C++, Scala, etc.) and scales linearly across multiple GPUs. If you prefer writing the bulk of your code from scratch with a few modifications, this is your choice. Amazon trusts it, and it’s used for things like speech recognition or NLP.
The framework features advanced GPU support and works with several servers at once for faster training. It’s excellent in business settings, but if you’re solely in research, you may not have quite the support you need. However, MXNet shines with big, industrial style projects.
Getting Started in Deep Learning
Deep learning models require a lot of processing power, but their learning curve is worth it. The deep learning community is providing a lot of exciting updates and documentation for projects in these frameworks, so getting into the process shouldn’t be a solo project.
If you don’t see your preferred framework on the list, or you’ve got a big toolkit and experience under your belt, let us know how you’ve approached your projects in the past. With such rich community collaboration, the process of building deep learning models and which frameworks to use for specific fields and problems can always benefit from the discussion. We’d love to know how you’ve approached these models.
Want to learn more about these frameworks in person? Attend ODSC East in Boston April 13–17 and learn from some of the creators of these frameworks themselves!