● TensorFlow

Google’s open-source platform TensorFlow is perhaps the most popular tool for Machine Learning and Deep Learning. TensorFlow is JavaScript-based and comes equipped with a wide range of tools and community resources that facilitate easy training and deploying ML/DL models. Read more about top deep learning software tools. While the core tool allows you to build and deploy models on browsers, you can use TensorFlow Lite to deploy models on mobile or embedded devices. Also, if you wish to train, build, and deploy ML/DL models in large production environments, TensorFlow Extended serves the purpose.

What you need to know:

Although there are numerous experimental interfaces available in JavaScript, C++, C #, Java, Go, and Julia, Python is the most preferred programming language for working with TensorFlow. Read why python is so popular with developers? Apart from running and deploying models on powerful computing clusters, TensorFlow can also run models on mobile platforms (iOS and Android). TensorFlow demands extensive coding, and it operates with a static computation graph. So, you will first need to define the graph and then run the calculations. In case of any changes in the model architecture, you will have to re-train the model.

The TensorFlow Advantage:

● TensorFlow is best suited for developing DL models and experimenting with Deep Learning architectures.
● It is used for data integration functions, including inputting graphs, SQL tables, and images together.

● PyTorch

PyTorch is an open-source Deep Learning framework developed by Facebook. It is based on the Torch library and was designed with one primary aim – to expedite the entire process from research prototyping to production deployment. What’s interesting about PyTorch is that it has a C++ frontend atop a Python interface. While the frontend serves as the core ground for model development, the torch.distributed” backend promotes scalable distributed training and performance optimization in both research and production.

What you need to know:

● PyTorch allows you to use standard debuggers like PDB or PyCharm.
● It operates with a dynamically updated graph, meaning that you can make the necessary changes to the model architecture during the training process itself.

The PyTorch Advantage:

● It is excellent for training, building, deploying small projects and prototypes.
● It is extensively used for Deep Learning applications like natural language processing and computer vision.

● Keras

Another open-source Deep Learning framework on our list is Keras. This nifty tool can run on top of TensorFlow, Theano, Microsoft Cognitive Toolkit, and PlaidML. The USP of Keras is its speed – it comes with built-in support for data parallelism, and hence, it can process massive volumes of data while accelerating the training time for models. As it is written in Python, it is incredibly easy-to-use and extensible.

What you need to know:

● While Keras performs brilliantly for high-level computations, low-level computation isn’t its strong suit. For low-level computations, Keras uses a different library called “backend.”
● When it comes to prototyping, Keras has limitations. If you wish to build large DL models in Keras, you will have to make do with single-line functions. This aspect renders Keras much less configurable.

The Keras Advantage:

● It is excellent for beginners who have just started their journey in this field. It allows for easy learning and prototyping simple concepts.
● It promotes fast experimentation with deep neural networks.
● It helps to write readable and precise code.

● Sonnet

Developed by DeepMind, Sonnet is a high-level library designed for building complex neural network structures in TensorFlow. As you can guess, this Deep Learning framework is built on top of TensorFlow. Sonnet aims to develop and create the primary Python objects corresponding to a specific part of a neural network. These objects are then independently connected to the computational TensorFlow graph. This process of independently creating Python objects and linking them to a graph helps to simplify the design of high-level architectures.

What you need to know:

● Sonnet offers a simple yet powerful programming model built around a single concept – “snt.Module.” These modules are essentially self-contained and decoupled from one another.
● Although Sonnet ships with many predefined modules like snt.Linear, snt.Conv2D, snt.BatchNorm, along with some predefined networks of modules (for example, snt.nets.MLP), users can build their own modules.

The Sonnet Advantage:

● Sonnet allows you to write modules that can declare other submodules internally or can pass to other modules during the construction process.
● Since Sonnet is explicitly designed to work with TensorFlow, you can easily access its underlying details, including Tensors and variable_scopes.
● The models created with Sonnet can be integrated with raw TF code and also those written in other high-level libraries.