The Artificial Intelligence (AI) sector is growing with algorithms. Developing to meet and even exceed human capabilities. One awesome example is Deep Learning (DL). An emerging machine learning subfield that can continue to evolve on its own. Without the need for continued programming. Also, 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 have appropriate deep learning frameworks. Since each framework serves a specific purpose. In smooth and quick business development or efficient delivery. Finding the perfect fit is important and necessary.
 
However, deep learning is the key to performing tasks of a higher level of complexity and logical thinking. Building and deploying them proves to be quite a difficult challenge. Especially, for data scientists and data engineers worldwide. Today, we have many frameworks that enable us to develop tools that can offer a better level of precision. Furthermore, simplifying the challenging programming. Each platform has various benefits.

Deep Learning Frameworks

 
Here, we look at some of the top Best deep learning frameworks. Like TensorFlow, Keras, Pytorch, etc to give you a better understanding of which framework can fit. Or be useful in solving your business tasks.

Deep Learning Frameworks Pros & Cons

TensorFlow

TensorFlow, deliver by Google and written in C++ and Python. It is 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. Therefore, we don’t need any other proof of its greatness. TensorFlow is ideal for advanced projects such as building neural networks with multilayers. Also, It’s used for voice and image recognition. Moreover, 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. Firstly, It is user-friendly, scalable, and extensible. Secondly, 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, CNTK, and Theano backends

PyTorch

PyTorch is new but is becoming increasingly popular. It is also open-source, built primarily by Facebook. However, it is famous for its usability, versatility, and customizability. PyTorch has a clean architectural model. So, this 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

Caffe

Caffe is a deep learning platform. Available in frameworks such as C, C++, Python, MATLAB, and the CLI. Certainly, It is well known for its transposability, speed. More on, applicability in Convolution Neural Networks (CNN) modeling. However, the biggest advantage of using the C++ library of Caffe (which comes with a Python interface) is accessing available networks from the ‘Caffe Model Zoo’ deep net repository. Those are pre-trained and are using instantly. Whether modeling CNNs or solving issues related to image processing. This is your perfect library.

Pros:

  • The biggest selling point is its speed, measured at 1 ms/image for inference and 4 ms/image for learning
  • Offers server optimized inference
  • Fast, scalable, and lightweight

Cons:

  • No support for fine granularity network layers

MXNet

MXNet (pronounced mix-net) is a deep learning platform. It is available through Python, R, C++, and Julia. It shares higher efficiency, productivity, and versatility. Also, MXNet’s strength is that it allows the user to code in several programming languages (Python, C++, R, Julia, and Scala, to name a few). To clarify, you can train your models of deep learning with whatever language you’re comfortable in without having to learn anything new. Most importantly, Amazon uses this framework as its reference library for deep learning.

Pros:

  • Can scale and work with many GPUs
  • Long Short-Term Memory (LTSM) networks with both RNN and CNN
  • A high-performance imperative API
  • Easy Model serving

Cons:

  • Much smaller community
  • Less popular among researchers

Sonnet

A high-level library designed specifically to build complex neural network structures in Tensorflow. As you probably have guessed by now. This particular deep learning framework is actually built on top of TensorFlow. This means that it works in collaboration with it and builds upon the foundation offered by TensorFlow.
 
Sonnet works intending to develop and create primary Python objects. These objects correspond to specific parts of a neural network. In other words, it acclimatizes itself to individual situations and provides a rather specific workload.
 
Sonnet offers a rather simple but supremely powerful programming model that’s built around a single concept i.e. “sent. Module”. /this is basically a self-contained model that is decoupled from one another. Although Sonnet does provide predefined modules. It brings with it the capacity for users to build their own modules and work accordingly.

Pros:

  • Allows you to write your own modules that can declare other submodules internally. Or pass them onto other modules during the construction process.
  • Since it works explicitly with TensorFlow. You have the opportunity to access its particular underlying details.
  • The models created with Sonnet can actually be integrated with raw TF code. Which can also then be carried forward into other high-level libraries.

Cons:

  • A rather basic framework
  • Does not have its own set foundation and is reliant on TensorFlow for its operations.

Gluon

A rather recent addition to the world of DL frameworks. Gluon is an open-source deep-learning interface. It is brilliant for beginners to develop machine learning models with ease. With its straightforward and concise API for defining ML/DL models through the usage of an assortment of pre-built and optimized neural network components. It offers a relatively quicker achievement of models.
 
Gluon also offers its users to define neural networks using simple, clear, and concise code. It comes with a range of plug-and-play neural network building blocks. These blocks include predefined layers, optimizers, and initializers. Which in turn helps to eliminate any underlying complicated implementation details.
 
With Gluon, you have the flexibility to develop according to your needs. Without there being any compromise on the performance. Moreover, with its dynamic neural network definition. You also have the ability to build it on the go with any structure you’d like.

Pros

  • A versatile tool for machine learning beginners. Since it allows users to define and manipulate models.
  • Rather easy to prototype and experiment with its neural network models.

Cons

  • Too basic.
  • Lack of sophisticated features for advanced development needs.

DL4J

A distributed deep-learning library written for Java and JVM (Java Virtual Machine). It is compatible with several different languages and underlying computations.
 
Its use of Apache Spark and Hadoop makes it easier for it to expedite the model training process. Incorporate AI into the business environment. With ease to use on distributed CPUs and GPUs.

Pros

  • Can compose deep neural nets from rather shallow nets, which creates “layers”
  • Flexibility when using a combination of encoders.

Cons

  • Reliant on a lot of cross-integration for optimum performance.

Chainer

Another open-source Deep learning framework that’s written in Python. Chainer is in fact the first deep learning framework to introduce the define-by-run approach. With this, you start by defining fixed connections. Between a variety of mathematical operations in the network before running the actual training computation.
 
This enables developers to test out their operations beforehand. Also, make any necessary tweaks to the operating mathematics.

Pros

  • Highly intuitive and flexible.
  • Ease of debugging. It can suspend training computation. Inspect data flows on code present in particular networks.

ONNX

A brainchild of Microsoft and Facebook. The Open Neural Network Exchange (ONNX) is a diverse ecosystem. It has been designed specifically for the development and presentation of ML and DL models. It includes separate definitions of extensible computation graph models. Along with definitions of built-in operators and standard data types.
 
ONNX simplifies the transferring model process between different means of working with AI. This enables developers to train models in one framework. Then, transfer it to another one for any inference.

Pros

  • Easier to access hardware optimizations.
  • Allows users to develop in their preferred frameworks.

Conclusion

Given above are some of the best deep learning frameworks available in the market. So, compare your product’s requirements with the pros and cons of deep learning frameworks. Moreover, you can easily see which one you should be using for your project.
 
Researchers have come to some form of conclusion. On what really is the ‘best’ deep learning framework on offer at this point TensorFlow.
 
While PyTorch might also be right up there. TensorFlow just edges it due to the following reasons:
 
1) TensorFlow is more mature. Owing to its long presence in the industry. Also, many changes/developments have taken place within it over the years.
 
2) It supports more programming languages. Therefore, making it a more holistic framework
 
3) Gained wide popularity in the market itself
 
4) The community support on offer at TensorFlow is beyond comparison with anyone else
 
5) It is intertwined with several supporting technologies. So it makes the entire integration that much easier
 
However, this is not to say that TensorFlow is the right fit for everyone. What we must understand is that there is no one “perfect” framework. Because each works differently. Provides differing advantages based on the specific development tasks that the user wants to achieve.
 
Therefore, the decision is truly up to you at the end of the day. just think about what you’re hoping to achieve. How advanced your data truly is before you make the jump to deciding which framework to use.

Look for Major Types Of Machine Learning Models