PyTorch
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PyTorch
After TensorFlow, PyTorch is the best one among other Deep Learning platforms. Specifically, it was improved for providing the services for Facebook but it is already used for its own tasks by companies like SalesForce and Twitter.
Key Features:
• The PyTorch DL platform operating with the dynamically updated graph which allows making changes to the architecture in the process.
• You can also use standard debuggers like PyCharm or PDB.
• It has a simple and clear process of training for a neural network and supports the data parallelism and distributed learning models.
• It is well suited for small projects and prototyping.
Sonnet
Sonnet has been built on top of Tensor Flow that designed by the company DeepMind with the motive of designing a complex architecture. The main benefit of using Sonnet is to reproduce the Deep Mind’s research papers with ease when compared to Keras.
Key Features:
• It has high-level object-oriented libraries that can bring out the abstractions while developing the neural networks or other machine learning algorithms.
• The main theme of the creation of Sonnet is to construct the primary Python objects. It simplifies the complex architecture by separating the process of creating objects and associating them with a graph.
Keras
If you have a lot of data, Keras is the best deep learning platform and ideal for learning and prototyping simple concepts. It helps to design the beautiful API which can be used for more exotic applications.
Key Features:
• Keras is limited to the prototyping and allows creating massive models of deep learning that makes a less configurable environment than low-level frameworks.
• Keras can’t compare with the TensorFlow because both are working on different platforms such as Keras on high-level which operating with the abstraction of layers and models and TensorFlow is on low-level that implemented the mathematical operations like generalized matrix-matrix multiplication and convolution operations.
MXNet
MXNet is a type of Deep Learning platform that can be implemented on various kinds of devices. The advantages of MXNet included fast problem-solving ability, supporting the multiple GPU’s, and clean and easily maintainable code (Sharama, 2019).
Key Features:
• It has been supported by various languages such as JavaScript, Julia, R, Python, Go, Scala, and even Pearl.
• The Deep Learning platform is working effectively on multiple GPU’s and many machines.
Gluon
Gluon is another type of Deep Learning platform that can be used to create both simple and sophisticated models. It brings out the training algorithm and deep learning model that improves the flexibility without compromising on efficiency in the operations.
Key Features:
• Basically, it is a flexible interface for prototyping, learning, and designing deep learning models without losing the learning speed.
• It is working based on MXNet that simplifies the creation of deep learning models.
• As similar to the PyTorch, Gluon is operating on a dynamic graph which is an alternative to Keras for distributed computing (Techlabs, 2019).
Swift
When you’re considering the programming, you might hear about Swift which can be used for app development for iOS and macOS. With the integration of programming language, Swift for TensorFlow will become powerful algorithms than before. Let’s say, if you’re running a program, not able to find the typo-errors and crash-down the program, you can opt for Swift to know any line of code that running correctly.
Key Features
• As Swift has been built upon TensorFlow, it allows accessing the transparent access to the low-level TensorFlow operators.