The Artificial Intelligence (AI) sector is rapidly growing with algorithms developing to meet and even exceed human capabilities. One awesome example is Deep Learning (DL), and emerging machine learning subfield which can continue to evolve on its own, without the need for continued programming. When companies want to use AI to expand and to get their startup to take off, one aspect is essential: the technology with which they choose to operate must be combined with an appropriate deep learning framework, particularly since each framework serves a specific purpose. In terms of smooth and quick business development, as well as efficient delivery, finding the perfect fit is not only important but also necessary.
Given that deep learning is the key to performing tasks of a higher level of complexity and logical thinking, successfully building and deploying them proves to be quite a difficult challenge for data scientists and data engineers worldwide. Today we have a multitude of frameworks that enable us to develop tools that can offer a better level of precision along with simplifying the challenging programming. Each platform is designed to benefit various purposes differently.
Here, we look at some of the top 5 Best deep learning frameworks to give you a better understanding of which framework can fit perfectly or be useful in solving your business tasks.
TensorFlow
TensorFlow, developed by Google and written in C++ and Python, is considered as one of the best open-source resources for numerical computing. Particularly if giants like Uber, DeepMind, AirBnB, or Dropbox have all chosen to exploit the system, we don’t need any other proof of its greatness. TensorFlow is ideal for advanced projects such as building neural networks with multilayers. It’s used for voice and image recognition and text-based applications (such as Google Translate).
Pros:
- Extensive documentation and help
- Monitoring and visualization of models
- On-device inference for mobile using TensorFlow Lite
- Model serving
- Distributed training
Cons:
- Low speed compared to other frameworks
- Learning and debugging can be difficult for beginners
Keras
Keras is an open-source neural network library developed in Python. It provides compatibility with TensorFlow, R, Microsoft Cognitive Toolkit, or Theano. Designed by Francois Chollet, a Google engineer, to allow rapid experimentation with deep neural networks, it is user-friendly, scalable, and extensible. It is a minimalistic library based on Python that can run on top of TensorFlow, Theano or CNTK.
Pros:
- Minimalistic and easy to use
- Large and helpful community
- Easy backend services
- Awesome compatibility with well-known frameworks
Cons:
- Limited customization
- Constrained to TensorFlow, CATKIN, and Theano backends
PyTorch
PyTorch is fairly new but is becoming increasingly popular. It is also open-source, built primarily by Facebook, and is renowned for its usability, versatility, and customizability. PyTorch is designed with a clean architectural model which makes it easy to learn and execute the training process and create deep learning models. Py stands for Python so anyone with a basic understanding of Python can start developing and training their deep learning models.
Pros:
- The define-by-run mode is just like the simple programming
- Easy debugging with common tools
- Provides declarative data parallelism
- Contains a lot of pre-trained parts
- Production-ready
Cons:
- A bit immature as with all new technologies………….
Read full Story at https://autome.me on February 20, 2020.
