5 Deep Learning Frameworks You Need To Know in 2021

Deep Learning with visually detected objects

Deep learning frameworks are used in the creation of deep and machine learning models. The frameworks offer tried and tested foundations for designing and training deep neural networks by simplifying machine learning algorithms.

These frameworks include interfaces, libraries, and tools that allow programmers to develop deep and machine learning models more efficiently than coding them from scratch. They provide concise ways for defining models using pre-built and optimized functions.

Deep Learning Frameworks

In addition to speeding up the process of creating machine or deep learning algorithms, the frameworks offer accurate and research-backed ways to do it, making the end product far more accurate than if one was to build the entirety of the model themselves.

This article will focus on the five most important deep learning frameworks in 2021:

  1. Tensorflow
  2. Keras
  3. PyTorch
  4. MxNet
  5. Chainer.


Tensorflow is an open-source, cost-free software library for machine learning and one of the most popular deep learning frameworks. It is coded almost entirely using Python. Developed by Google, it is specifically optimized for training and inference of neural networks. Deep learning inference is the process by which trained deep neural network models are used to make predictions about previously untested data.

Tensorflow allows developers to produce large-scale neural networks with many layers using data flow graphs. Hence, Tensorflow supports these large numerical computations by accepting data in the form of multidimensional arrays that host generalized vectors and matrices, called tensors.


Keras is another very popular open-source software library. The deep learning framework provides a Python interface for developing artificial neural networks. Keras acts as an interface for the Tensorflow library. It has been accredited as an easy-to-use, simplistic interface.

Keras is particularly useful because it can scale to large clusters of GPUs or entire TPU pods. The functional API can handle models with non-linear topology, shared layers, and even multiple inputs or outputs. Keras was developed with the intention to enable fast experimentation for bulky models and prioritizes the developer experience, which is why the platform is so intuitive.


PyTorch is a Python library that supports building deep learning projects such as computer vision and natural language processing. There are two main features of PyTorch: Tensor computing (such as NumPy) with strong acceleration via GPU, and deep neural networks built on top of a tape-based automatic differentiation system, which numerically evaluates the derivative of a function specified by a computer program.

PyTorch also includes the Optim and nn module. The Optim module, torch.optim, uses different optimization algorithms used in neural networks. The nn module, or PyTorch autograd, lets you define computational graphs and also make gradients; this is useful because raw autograd can be low-level. Pytorch’s advantages over other deep learning frameworks include a short learning curve and data parallelism, where computational work is distributed among multiple CPU or GPU cores.


Apache MxNet is an open-source deep learning framework designed to train and deploy deep neural networks. A distinguishing feature of MxNet when compared to other frameworks is its scalability (the measure of a system’s ability to increase or decrease in performance).

MxNet is also especially known for its ability to be flexible with multiple languages, unlike frameworks like Keras that only support one language. Among the languages it supports include C++, Python, Julia, Matlab, JavaScript, Go, R, and many more. However, it is not as widely used as other DL frameworks, which leaves it with a smaller open source community.


Chainer is a deep learning framework built on top of the NumPy and CuPy libraries. Chainer is the first framework ever to implement a “define-by-run” approach, contrary to the more popular “define-and-run” approach.

The “define-and-run” scheme first defines and fixes a network, and the user continually feeds it with small batches of training data. However, the “define-by-run” means that Chainer stores the history of computation in a network rather than the actual programming logic.

Following Chainer, other frameworks such as PyTorch and TensorFlow now implement the “define-by-run” approach. Chainer also supports Nvidia CUDA computation, which has several advantages over traditional general-purpose computation on GPUs using graphics APIs.

What’s Next

Deep learning frameworks are widely used and implemented, and include many more than the five discussed in this article. Other increasingly popular DL frameworks that were not mentioned above include Sonnet, Gluon, DL4J, and more. To read more about deep learning, we suggest the following articles

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