As an entrepreneur or aspiring CEO, you probably have dreams of launching your startup or scaling up your business. To do this, you need the right technology combined with good deep learning and machine learning frameworks.
The framework that you choose can make or break your project deployment. It can also impact the pace at which your business develops.
Over the years, Google’s TensorFlow has earned a reputation for being one of the best platforms for deep neural networks and machine learning.
TensorFlow is an open-source library used for a wide range of tasks including numerical computation, application building, serving predictions, and acquiring data.
It also makes it easy to train models, run image recognition, machine translation, PDE-based simulations, word embeddings, natural language processing, and large-scale production prediction.
Despite being one of the bigwigs in the machine learning field, TensorFlow may not be right for you. The programming language you use and the kind of neural networks you intend to develop might not pair perfectly with TensorFlow.
Additionally, the purpose and character of the project or the number of tools you’ll need may put TensorFlow out of your comfort zone. As a result, it’s important to consider other alternatives that won’t carve a huge hole in your business’ account.
Finding the perfect alternative to TensorFlow could be a tough exercise. But lucky for you, I’ve done all the heavy lifting.
Below are the best TensorFlow alternatives you can use. I’ve highlighted the unique features, benefits, and use cases of each one to help you select the best deep learning solution for your particular needs.
Best TensorFlow Alternatives
1. DataRobot
DataRobot is an enterprise-level machine learning platform that uses algorithms to analyze and understand various machine learning models to help with informed decision-making.
It makes it easy for businesses to conduct data analysis and build advanced AI-powered applications.
The software houses a comprehensive library of open source ML techniques and algorithms. DataRobot leverages an automated time series model to make predictions about a series of data based on past trends and data obtained from testing.
This way, you’ll be able to make more accurate forecasts for marketing, lead scoring, product demand, revenue, inventory, financial analytics, and more.
DataRobot is available as a fully managed service, on your preferred cloud platform, or as an on-premise tool.
Thanks to its Paxata feature, your organization can shape, explore, and combine different data sets for Enterprise AI and transform them into data primed for AI and ML applications.
For those who prefer to write their own code and build custom models, DataRobot incorporates Zepl—a self-service, cloud-native notebook. With Zepl, diverse teams will also be able to collaborate seamlessly, boosting efficiency and productivity along the way.
Unlike TensorFlow which has a higher learning curve for beginners, DataRobot features an intuitive interface. This makes it easy to use without technical assistance.
DataRobot offers AI assistance to users during the course of data preparation exercises like cleaning, profiling, and clustering. This in turn decreases the amount of time required to shape and prepare data.
All the screens also come loaded with descriptions about all models and data points so that even people with zero data-analysis experience can understand.
In addition, DataRobot provides tailored artificial intelligence and machine learning training to help businesses make the most of the platform.
2. PyTorch
If having a deep learning framework that offers brief training, flexibility, and debugging capabilities are important to you, you’ll find PyTorch to be a better alternative to TensorFlow.
PyTorch was developed by Facebook and is written in the Lua programming language. It’s a machine learning library, scripting language, and computing framework rolled into one.
It enables developers to carry out Tensor computations with impressive GPU speed. PyTorch was specifically built to accelerate processes from research to production and deployment.
PyTorch utilizes array-like sensors to convert parameters, model outputs, and inputs into code. With TorchScript and TorchServe functions, it provides end-to-end workflows for pretty much all major platforms.
It can be used to build and train your deep learning models rapidly. This makes it the preferred framework of choice for many researchers.
PyTorch has a clear and straightforward modeling process. It features tons of retrained modular parts and models that are ready to use and relatively simple to combine.
What sets PyTorch apart from other deep learning solutions is its reverse-mode auto-differentiation technique. This allows you to make alterations to the behavior of your neural network with little to no overhead.
But with traditional techniques, you’ll have to start from scratch every time you need to modify an existing neural network. PyTorch’s technique speeds up development cycles and gives users more flexibility.
PyTorch also features ready-made optimizer and loss functions, built-in transformations, and simplified data loading mechanisms. The program is available for Linux, Android, iOS.
The platform offers support for distributed training and popular debugging tools such as the PyCharm debugger, Pdb, and Ipdb. It also utilizes model servers, direct embedding, and direct microservices to support model serving.
PyTorch outshines TensorFlow when it comes to data parallelism because it uses native support to achieve asynchronous execution via Python. Meanwhile, TensorFlow requires you to optimize and program each operation manually on specific devices for distributed training.
Compared to TensorFlow, PyTorch has an easy learning curve. Both beginners and folks with machine learning experience can get the hang of the platform in a short while.
3. Keras
Keras is a neural network library. Like TensorFlow, it’s open-source and based on the Python programming language.
It offers support for convolutions networks and can be used for quick and easy research prototyping. Keras is a powerful API designed to expedite experimentation and it doesn’t require as much coding as TensorFlow.
There are many use cases where Keras can come in handy, this includes speech recognition, translation, voice identification, and image recognition amongst others.
This deep learning framework offers advanced extensibility and incredible practices for cognition load reduction. With Keras, you can build new modules like optimizers, cost functions, ad neural layers, and more.
These modules are defined by the Python code and are pretty easy to add. The Keras framework features a continuous interface for building simple layer linear stacks using outputs and inputs.
Its minimalistic API can aid in crafting deep learning models from the ground up and creating sophisticated graphs of layers. The deep learning models and layers built with Keras are portable and lightweight.
Keras gives you access to scalable and customizable modules. It has a simple and intuitive architecture that makes navigation a breeze even for newbies.
If you’re just starting your deep learning journey, Keras can provide an easy gateway into the field and help you prototype painless concepts.
Other attractive features that make Keras worth considering include its smartphone support which allows you to use the platform on your iOS or Android device.
Furthermore, Keras can be run on GPU, Spark, TPU, and CPU especially when combined with CUDA. It also supports combining two networks.
4. Caffe & Caffe 2
Caffe is a deep learning framework based on C++ with an elegant front-end that’s built on Python. It’s an excellent program for developers and data scientists who want to train models without having to author extra lines of code.
The framework offers support for feedforward networks and convolutional neural networks. Caffe is useful for refining existing networks and conducting image processing.
Caffe2 was developed by Yangqing Jia, a Facebook developer, to serve as a more efficient successor to Caffe. It’s designed for large-scale and mobile deployments in production environments.
Caffe2 is a scalable production-ready framework. Facebook has leveraged it to ship to over one billion phones ranging across six generations of Android and eight generations of iPhone CPU architecture.
The framework is fast and lightweight with server-optimized inference. Caffe2 integrates seamlessly with other frameworks like PyTorch.
It also provides pre-trained models for creating demo applications, which leads to more efficient development processes. Caffe2 features a stylish, easy-to-navigate interface that will help you carry out deep learning tasks easily and quickly.
5. Scikit-learn
Developed by David Cournapeau and released in 2007, Scikit-learn is an open-source machine learning library based on Python and C++. It’s built on top of NumPy, Matplotlib, and SciPy.
Scikit-learn houses many regression, clustering, and classification algorithms including Random Forest, K-means clustering, Support Vector Machines, Gradient Boosting, and more.
With Scikit-learn you can also easily implement reprocessing and modal selection for enhanced data visualization and customer segmentation. The platform can also be used for feature extraction, dimensionality reduction, feature selection, grid search, and matrix factorization.
Scikit-learn features a handful of built-in datasets like the diabetes dataset, iris data set, and house prices dataset. Their core function is to help you easily understand and execute ML models on them.
The Scikit-learn framework supports unsupervised and supervised machine learning. It’s designed to specifically handle numerical data stored in SciPy sparse metrics or NumPy arrays.
It features a number of models and algorithms known as estimators. In addition, it offers transformation, model fitting, data preprocessing, selection, and evaluation capabilities.
If you want a machine learning library that allows you to accurately evaluate prediction performance, Scikit-learn is up to the task. Another powerful tool that the platform provides is Decision Tree, which enables you to make decisions and predict the output.
This feature is particularly useful when you’re working with dependent variables that don’t have a sequential relationship with the independent variable.
Scikit-learn’s tool suite also provides support for a variety of other tasks like the construction of workflow pipelines that blend estimators and data transformer objects. Like TensorFlow, Scikit-learn is open source and available for commercial use.
6. Jupyter Notebook
Another TensorFlow alternative that’s worth considering if you’re in the market for a machine learning framework is Jupyter Notebook. It’s an open-source web application that enables collaboration between researchers, data scientists, mathematicians, data engineers, and other users.
Jupyter Notebook supports Python, R, and Julia programming languages and provides modular kernels for more than forty other languages. This multi-language support gives it an edge over TensorFlow which only supports a single language—Python.
The open-source platform functions as a computational notebook tool for the creation, editing, and sharing of code, images, explanatory text, and other data. You can use Jupyter to implement data visualizations, software code, and comments.
It can also be used to add computations and rich media renditions of computation output to a document. This document, called a notebook, can then be easily distributed and revised by members of your team.
In this way, notebooks can moonlight as a thorough computational record of every interaction and collaboration between your team members.
Jupyter Notebook is ideal for developers who want to write little bits of code and build large modules. It’s also a great tool of choice for handling small integration tasks, which makes it a good fit for small and medium-sized businesses.
The platform is equipped to create accurate graphical charts easily and quickly. Jupyter Notebook is also much easier to learn than TensorFlow, even for programming newbies.
Jupyter leverages containers like Docker to streamline deployment and software installation processes. It also offers good technical and community support to deal with any challenges you might encounter while using the tool.
Other uses for Jupyter Notebook include numerical simulation, data cleaning & transformation, statistical modeling, and exploratory data analysis. The notebooks you create with Jupyter can easily be shared via GitHub, email, Jupyter Notebook Viewer, and Dropbox.
7. MXNet
This deep learning framework was created by Apache in collaboration with Intel and the open-source community. MXNet supports a variety of languages such as Julia, R, Python, Javascript, C++, Perl, and more.
It is beloved by developers and companies across the globe because of its scalability. MXNet is most commonly used for forecasting, handwriting & speech recognition, and natural language processing.
When it comes to running deep learning algorithms, MXNet is efficient, quick, and flexible. You can run the MXNet framework on any device.
It offers high-powered AI and comprehensive GPU support with the inclusion of multiple GPU modes. MXNet gives you an extensive tool ecosystem and an abundant library to help you create and train models for cross-validation, time series, and natural language processing.
MXNet makes the training and deploying of deep neural networks an uncomplicated affair. The framework is lightweight and can easily be scaled to multiple GPUs and different machines with optimized computation and rapid context switching.
The program recently introduced HOROVOD, the open-source machine learning framework developed by Uber into the MXNet system. The Gluon API feature allows users to transition from symbolic and imperative mode seamlessly.
It also allows you to serve models and provides good support for multiple programming languages. TensorFlow on the other hand only supports the Python programming language.
With MXNet, you’ll be able to perform both symbolic and imperative programming. This will enable you or your developer team to select their preferred programming technique when creating deep learning models.
The MXNet framework comes with support for advanced deep learning models like long short-term memory networks (LSTMs) and convolutional neural networks (CNNs).
Whether you’re planning a big industrial or small-scale project, MXNet is up to the task.
Also Read: Best Open-source Tools For Web Developers
8. Torch
This TensorFlow alternative is an open-source machine learning framework that offers support for a variety of machine learning algorithms. Torch is based on LuaJIT, a scripting language that’s known for being quick and simple to use.
It is licensed under BSD and it supports implementation for C and CUDA programming languages. Torch is one of the oldest and most popular deep learning libraries around.
It lets you efficiently analyze, define, and optimize mathematical expressions that include multidimensional arrays and matrix Matrix calculations. The library is tightly integrated with NumPy and supports the use of GPUs.
Other amazing features that Torch offers include symbolic differentiation, self-verification, stability & speed operations, as well as extensive unit testing.
Torch also offers functions for numeric optimization routines, energy-based models, linear algebra routines, neural networks, and more. Although both Torch and TensorFlow provide trained models and parallel execution, Torch has a faster execution speed.
Since it’s a cross-platform tool, Torch can run smoothly on Graphics Processing Units (GPUs) and Central Processing Units (CPUs). The framework is also available on Linux, Windows, MacOS, Android, and iOS.
Torch performs computations super fast and can be used to train deep learning algorithms. It can also be used for signal processing, computer vision, audio, networking, image, and video.
Another feature that puts it ahead of many of its peers is its provision of a vast array of deep algorithms. It also provides a versatile N-dimensional array.
Compared to TensorFlow, the Torch framework is extremely easy to use so you can dive into your projects instead of spending too much time learning the ropes of the platform.
There are plenty of tutorials and sample codes available on Torch to guide you through the installation and setup process.
9. CNTK
Microsoft Cognitive Toolkit or CNTK is a sturdy deep learning framework built for handling large datasets. It offers support for multiple languages including Python, Java, C++, and C#.
You can train your models in one language and use them from another. This feature is particularly useful for organizations that require people specializing in different languages to collaborate together on projects.
CNTK enables you to train for handwriting, image, and voice recognition efficiently for both recurrent and convolutional neural networks. It comes with plenty of optimized components.
The open-source framework brings scalability and improved performance to the table. In the area of resource usage, it’s more lightweight than TensorFlow.
Thanks to CNTK’s API abstraction, you can easily train deep/arbitrary neural networks without consuming a lot of resources. Also, CNTK delivers abstractions for learning and computing algorithms.
It comes with scalable built-in data readers that are designed to deal with various data types. This includes speech, image, and text so you don’t have to go through the trouble of writing your own code for data reading.
CNTK can be easily integrated with Azure Cloud, Apache Spark, and a host of other tools and platforms. It’s also best suited for 64-bit Windows and Linux build or deployment environments.
Other positive aspects of the CNTK framework include clear and comprehensive documentation and directed graph visualization. In addition, users enjoy extensive technical support from the Microsoft Team, as well as user base and community support.
10. DL4J
Deeplearning4j is an open-source TensorFlow alternative and deep learning library. The fundamental computations for DL4J are based on CUDA, C, and C++ languages.
DL4J is powered by ND4J, the platform’s unique numerical computing library. It makes use of Hadoop and Apache Spark to implement AI within business environments and accelerate model training.
These models and algorithms can then be used on distributed central and graphic processing units. Like TensorFlow, DL4J supports data parallelism.
However, neural networks in DL4J are trained in parallel through repetitive reduction via clusters. DL4J features executions of deep belief net, word2vec, GloVe, deep autoencoder, tensor network, recursive neural network, stacked denoising autoencoder, and Boltzmann machine.
You can use DL4J to construct deep neural nets and form layers from shallow nets. This gives you the freedom to merge sequence-to-sequence autoencoders, recurrent/convolutional networks, and variational autoencoders.
DL4J is written for Java, with support for any Java Virtual Machine (JVM) language including Kotlin, Scala, and Clojure. It also underpins various kinds of neural networks such as LTSM, CNN, RNTN, and RNN.
There’s a lot of potential use cases for DL4J, specifically in the fields of fraud detection, image recognition, text mining, and natural language processing. If you’re working in any of these areas, you should definitely give this framework a closer look.
DL4J is capable of processing large amounts of data at a faster rate than TensorFlow. It’s also incredibly flexible and effective, with robust documentation.
Final Words
Now that you’re familiar with some of the best TensorFlow alternatives on the market, you’re equipped to make an informed decision about which deep learning framework is right for you.
As you’ve seen above, each framework brings something different to the table. So it comes down to which features are most important to you based on your project needs and situation.
All the platforms considered in this list are great options, but PyTorch has an edge over all the others. This is because it’s able to handle large datasets without compromising on speed or performance.
It’s also very easy to learn, even for beginners, and possesses excellent debugging capabilities. Additionally, PyTorch is compatible with different platforms and infrastructures.
