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Top 10 Deep Learning Frameworks in 2023 You Can’t Ignore

As the popularity of Machine Learning (ML) continues to solidify in the industry, with it is rising another innovative area of study in Data Science – Deep Learning (DL). 

Deep Learning is a sub-branch of Machine Learning. The unique aspect of Deep Learning is the accuracy and efficiency it brings to the table – when trained with a vast amount of data, Deep Learning systems can match (and even exceed) the cognitive powers of the human brain. 

Naturally, Data Scientists working on this advanced field of learning got busy to develop a host of intuitive frameworks for Deep Learning. These Deep Learning frameworks can either be an interface or a library/tool that helps Data Scientists and ML Developers to build Deep Learning models much more conveniently. The best part about Deep Learning frameworks is that you need not get into the intricacies of the underlying ML/DL algorithms – that is taken care of by the Deep Learning frameworks.

Now, let’s look at some of the most popular and extensively used Deep Learning frameworks and their unique features!

Also, check out our free NLP online course 

Top Deep Learning Frameworks

1. 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. This is a great deep learning framework

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.

2. 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. This is one of the best deep learning frameworks you can use. 

How it is different from Tensorflow? Read Pytorch vs Tensorflow.

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. 

3. 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. This is a great Deep Learning framework

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:

  1. It is excellent for beginners who have just started their journey in this field. It allows for easy learning and prototyping simple concepts.
  2. It promotes fast experimentation with deep neural networks. 
  3. It helps to write readable and precise code.Read: Deep Learning Career Path

4. 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. This is one of the best Deep Learning frameworks you can use. 

 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.

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5. MXNet

MXNet is an open-source Deep Learning framework designed to train and deploy deep neural networks. Since it is highly scalable, it promotes fast model training. Apart from flaunting a flexible programming model, it also supports multiple programming languages, including C++, Python, Julia, Matlab, JavaScript, Go, R, Scala, Perl, and Wolfram. This is a great Deep Learning platform which can be of great use to you!

What you need to know:

  • MXNet is portable and can scale to multiple GPUs as well as various machines.
  • It is a lean, flexible, and scalable Deep Learning framework with support for state-of-the-art DL models such as convolutional neural networks (CNNs) and long short-term memory networks (LSTMs).

The MXNet advantage:

  • It supports multiple GPUs along with fast context switching and optimized computation.
  • It supports both imperative and symbolic programming, thereby allowing developers to choose their desired programming approach to building deep learning models.

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6. Swift for TensorFlow

Swift for TensorFlow is a next-generation platform that combines the power of TensorFlow with that of the Swift programming language. Since it is specifically designed for Machine Learning, Swift for TensorFlow incorporates all the latest research in ML, differentiable programming, compilers, systems design, and much more. Although the project is at a nascent stage, it is open to anyone who’s interested in experimenting with it. Another great Deep Learning platform for you to use. 

 

What you need to know:

  • When it comes to differentiable programming, it gets first-class auto-diff support in Swift for TensorFlow. So, you can make derivatives of any function or even custom data structures differentiable within minutes.
  • It includes a sophisticated toolchain to help enhance the productivity of users. You can run Swift interactively in a Jupyter notebook and obtain helpful autocomplete suggestions to further explore the massive API surface of a next-gen Deep Learning framework.

The Swift for TensorFlow advantage: 

  • Swift’s powerful Python integration makes migration extremely easy. By integrating directly with Python, a general-purpose programming language, Swift for TensorFlow allows users to express powerful algorithms conveniently and seamlessly.
  • It is a wonderful choice if dynamic languages are not suited for your projects. Being a statically typed language, Swift depicts any error in the code upfront, so that you can take a proactive approach and correct it before running the code.

7. Gluon

A very recent addition to the list of Deep Learning frameworks, Gluon is an open-source Deep Learning interface that helps developers to build machine learning models easily and quickly. It offers a straightforward and concise API for defining ML/DL models by using an assortment of pre-built and optimized neural network components.

Gluon allows users to define neural networks using simple, clear, and concise code. It comes with a complete range of plug-and-play neural network building blocks, including predefined layers, optimizers, and initializers. These help to eliminate many of the underlying complicated implementation details. 

What you need to know:

  • It is based on MXNet and provides a neat API that simplifies the creation of DL models.
  • It juxtaposes the training algorithm and neural network model, thereby imparting flexibility to the development process, without compromising on the performance. This training method is known as the Gluon trainer method.
  • Gluon allows users to opt for a dynamic neural network definition which means that you can build it on the go using any structure you want and with Python’s native control flow.

The Gluon advantage:

  • Since Gluon allows users to define and manipulate ML/DL models just like any other data structure, it is a versatile tool for beginners who are new to Machine Learning.
  • Thanks to Gluon’s high flexibility quotient, it is straightforward to prototype and experiment with neural network models. 

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8. DL4J

Deeplearning4J (DL4J) is a distributed Deep Learning library written for Java and JVM (Java Virtual Machine). Hence, it is compatible with any JVM language like Scala, Clojure, and Kotlin. In DL4J, the underlying computations are written in C, C++ and Cuda.

The platform uses both Apache Spark and Hadoop – this helps expedite model training and to incorporate AI within business environments for use on distributed CPUs and GPUs. In fact, on multiple-GPUs, it can equal Caffe in performance. 

What you need to know:

  • It is powered by its unique open-source numerical computing library, ND4J.
  • In DL4J, neural networks are trained in parallel via iterative reduce through clusters.
  • It incorporates implementations of the restricted Boltzmann machine, deep belief net, deep autoencoder, recursive neural tensor network, stacked denoising autoencoder, word2vec, doc2vec, and GloVe.

The DL4J advantage:

With DL4J, you can compose deep neural nets from shallow nets, each of which forms a “layer.” This provides the flexibility that lets users combine variational autoencoders, sequence-to-sequence autoencoders, convolutional nets or recurrent nets as required in a distributed, production-grade framework that works with Spark and Hadoop.

9. ONNX

The Open Neural Network Exchange or ONNX project is the brainchild of Microsoft and Facebook. It is an open ecosystem designed for the development and presentation of ML and DL models. It includes the definition of an extensible computation graph model along with definitions of built-in operators and standard data types. ONNX simplifies the process of transferring models between different means of working with AI – you can train models in one framework and transfer it to another for inference. 

What you need to know:

  • ONNX was designed as an intelligent system for switching between different ML frameworks such as PyTorch and Caffe2. 
  • ONNX models are currently supported in Caffe2, Microsoft Cognitive Toolkit, MXNet, and PyTorch. You will also find connectors for several other standard libraries and frameworks.

The DL4J advantage: 

  • With ONNX, it becomes easier to access hardware optimizations. You can use ONNX-compatible runtimes and libraries that can maximize performance across hardware systems.
  • ONNX allows users to develop in their preferred framework with the chosen inference engine, without worrying about downstream inferencing implications.

10. Chainer

Chainer is an open-source Deep Learning framework written in Python on top of NumPy and CuPy libraries. It the first Deep Learning framework to introduce the define-by-run approach. In this approach, you first need to define the fixed connections between mathematical operations (for instance, matrix multiplication and nonlinear activations) in the network. Then you run the actual training computation.

Learning these deep learning frameworks will enable you to face deep learning interviews with proficiency and even differentiate which one of the following is not a deep learning framework.

What you need to know:

Chainer has four extension libraries – ChainerMN, ChainerRL, ChainerCV, and ChainerUI. With ChainerMN, Chainer can be used on multiple GPUs and deliver a super-fast performance, as compared to other Deep Learning frameworks like MXNet and CNTK.

The Chainer advantage:

  • Chainer is highly intuitive and flexible. In the define-by-run approach, you can use a programming language’s native constructs like “if” statements and “for loops” to describe control flows. This flexibility comes in handy while implementing recurrent neural networks.
  • Another significant advantage of Chainer is that it offers ease of debugging. In the define-by-run approach, you can suspend the training computation with the language’s built-in debugger and inspect the data that flows on the code of a particular network.

Why Do You Need Deep Learning?

We know that Deep Learning is a part of Machine Learning and has the ability to scale a business. This creates a huge potential for companies trying to use technology to get high-performance results. A study suggests that the Deep Learning industry, which will be driven by data mining, analytics, and customisation can contribute more than $93 billion by 2028. 

  • Automation of the Features

Without further human input, deep learning algorithms may create new features from a small collection of features present in the training dataset. Deep learning can therefore handle challenging jobs that frequently involve substantial engineering.

Businesses will benefit from quicker technology or application deployments that provide higher accuracy.

  • Is great for large amounts of data

The capacity of deep learning to process unstructured data is one of its main appeals. When you take into account that the great majority of company data is unstructured, this becomes very pertinent in a commercial environment. Some of the most popular data forms used by organisations include text, graphics, and speech. Because unstructured data cannot be fully analysed by traditional ML algorithms, this treasure of knowledge is frequently underutilised. Deep learning has the most promise for influence in this area.

Businesses may improve practically all functions, from sales and marketing to finance, by training deep learning networks with large amounts of data and suitable categorisation.

  • Has good self-learning ability

Deep neural systems include numerous layers, which makes it possible for models to do more demanding tasks and learn more complex properties. In tasks involving unstructured datasets and machine perception (i.e., the capacity to comprehend inputs like pictures, audio, and video as a person would), it outperforms machine learning.

This is because deep learning algorithms may eventually learn from their own mistakes. It has the ability to check the accuracy of its results and make the required corrections. Although, the correctness of output must be determined by humans in variable degrees for traditional machine learning models.

  • Cost Effective

Deep learning models can be expensive to train, but once they are, they can help firms reduce wasteful spending. An incorrect forecast or a defective product has significant financial consequences in sectors including manufacturing, consultancy, and even retail. Deep learning model training expenses are frequently outweighed by its benefits.

In order to drastically reduce error margins across sectors and verticals, deep learning algorithms can take into account variation among learning characteristics. This is especially evident when you evaluate the shortcomings of deep learning algorithms with those of the traditional machine learning paradigm.

  • Helps in scaling the business

Due to its capacity to analyse enormous volumes of data and carry out numerous calculations in a time- and cost-efficient way, deep learning is extremely scalable. Modularity and portability as well as productivity are directly impacted. 

For instance, you may execute your deep neural network in the cloud using Google Cloud’s Automated network prediction. In order to expand group prediction, you may use Google’s cloud infrastructure in addition to the improved model organisation. Automatically adjusting the number of units being used based on request traffic, this then increases efficiency.

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Wrapping Up

So, now that you have a detailed idea of all the major Deep learning frameworks out there, you can make an informed decision and choose the one that suits your project best and know which one of the following is not a deep learning framework for your demanded use or implementation.

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What are the challenges of configuring neural networks?

Since there are no clear rules for building a network for a specific situation, this is the case. We can't calculate the best model type or configuration for a dataset analytically. Copying the setup of another network for a comparable problem is a shortcut for configuring a neural network on a problem. However, because model configurations are not transportable between issues, this method rarely yields good results. You are also likely to work on predictive modelling challenges that are very different from those addressed in the literature.

What are the problems with regards to poor performance of a deep learning model?

When it comes to bad performance of a deep learning neural network model, there are three categories of issues that are simple to diagnose. Learning issues present themselves in a model that is unable to successfully learn a training dataset, or that makes slow progress or performs poorly when training the dataset. Generalization issues reveal themselves in a model that overfits the dataset and performs poorly on the holdout dataset. Prediction issues reveal themselves in the stochastic training procedure, which has a significant impact on the final model, resulting in a high degree of variability in behavior and performance.

How can the variance in the performance of the final model be reduced?

By including bias, the variation in the final model's performance can be minimized. Combining the predictions from numerous models is the most typical approach to incorporate bias into the final model. Ensemble learning is the term for this. Ensemble learning can improve predictive performance in addition to reducing the variance of a final model's performance. Each contributing model must have skill, which means that the models must produce predictions that are better than random, while the prediction errors between the models must have a low correlation.

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