Top 6 Machine Learning Frameworks for AI and Deep Learning
Explore top machine learning frameworks for AI and deep learning, including TensorFlow, PyTorch, Keras, and more.
Learning has always been an integral part of development. From early civilization, humans have learned new skills to adapt to and transform their environment.
According to the UNESCO publication, education is crucial for transforming the future. But as the world advances in tech, it becomes more clear how machine learning (ML) also plays a vital role in advancing a better future.
Empowering machine models and algorithms to self-learn improves their ability to make more accurate decisions and predictions. In turn, these machines can power emerging technologies like self-driving cars, robotics, and medical diagnostics.
The first step to building these machine models is to master relevant programming frameworks. Follow this tutorial to learn more about the top machine learning frameworks for AI and deep learning.
Top machine learning frameworks
Machine learning frameworks are interfaces or libraries that enable developers to build ML models faster and easier without dealing with underlying ML algorithms. We cover some top frameworks developers use for deep learning and AI development projects.
1. TensorFlow
TensorFlow is an end-to-end open-source machine learning library. Researchers at Google Brain Team originally developed the library for internal deep learning and machine learning research.
In 2017, Google launched TensorFlow 1.0 and made it open for public use. The library was characterized by its 58x speed and scalability. It contained application programming interfaces (APIs) for Go and Java, which aided machine learning for mobile devices. However, the drawback was that version 1.0 was low-level and had a cumbersome workflow.
Retaining its versatility and improving on simplicity and beginner-friendliness, Google launched TensorFlow 2.0. Google incorporated Keras into TensorFlow, giving it access to high-level APIs for building and training machine learning models. After building, the library’s flexibility allowed developers to deploy their models in various ecosystems, including web browsers, mobile devices, and larger data systems.
TensorFlow also provides a rich set of tools and pipelines to develop and train several deep neural networks simultaneously. Common use cases of TensorFlow for beginners are building and training high-performance ML applications for image classifiers and recommender systems for social platforms. It’s particularly useful for implementing convolutional neural networks (CNNs) and working with language models.
Features
- Comes with the TensorFlow.js library that allows you to deploy machine learning models directly to your web browser and run in a JavaScript environment like Node.js
- With TensorFlow Lite, developers can deploy the framework on iOS, Android, internet of things (IoT) devices, and ML models on cloud infrastructures, like Microsoft Azure and Amazon Web Services (AWS)
- Provides eager execution, which processes tensors and smaller code units and provides output immediately (instead of waiting to complete the entire model)
- Supports CUDA architectures for GPU acceleration
Pros
- Natively supports training acceleration with graphics processing units (GPUs) and tensor processing units (TPUs), which are natively unavailable with other frameworks like scikit-learn
- Can operate as a back-end framework to Keras when dealing with a high-level environment and can function independently for low-level computations
- Library has vast functionalities and high performance when dealing with large datasets
- Supports various object-oriented languages like Python, Java, JavaScript, and C++
- Excellent for gradient computation and flow control in neural networks
Cons
- Despite requiring smaller storage space, TensorFlow takes more time to train machine learning models compared to PyTorch and Theano
- Only supports NVIDIA and Python programming languages for accelerating models on GPUs
- Unlike Keras and Theano, which can use any of Python’s debugging tools, TensorFlow uses only a special debugging tool
2. Keras
Keras is a high-level framework that provides an interface for machine learning and deep learning processes. Keras was created to achieve faster experimentation. The framework can simultaneously run models on your system’s CPU) and GPU, accelerating the training process.
Since Keras was written in Python, it inherited simple syntax and modular programming, making it beginner-friendly. Keras is simple, providing numerous easy-to-use APIs and reducing the number of codes users need to complete various machine learning tasks.
Besides its simple APIs, Keras shows excellent flexibility and can run on top of TensorFlow and other open-source frameworks like Microsoft CNTK and Theano. Companies like Uber, Netflix, and Square use Keras frameworks to create deep learning models for their mobile apps.
Features
- Keras is modular, containing pre-built functions, variables, and modules that make computing codes easier when building deep learning applications and models
- Allows you to export models to JavaScript to run them directly in the browser for various Android and iOS devices
- Has numerous prelabeled datasets, allowing users to access and train easier
Pros
- An excellent choice for projects where researchers want to quickly build, train, and deploy models
- Great for fast prototyping and can support various operating systems, including Linux, macOS, and Windows
- When compared to TensorFlow, Keras has more debugging functionalities and toolkits
- Has a vast community of developers and robust documentation and tutorial materials that allow beginners to soft pedal into deep learning
Cons
- Although Keras can provide interfaces for TensorFlow and Theano, it can’t stand alone—it needs a back-end framework to function
- Relative to TensorFlow, Theano, and PyTorch, Keras is slow and requires more time to process algorithms and train deep learning models
- Keras often give errors in low-level environments, like when handling some complex mathematical computations
3. PyTorch
PyTorch is a deep learning framework developed from Facebook AI research in 2018. The framework was built using Python and Torch, a scientific computing framework based on the Lua programming language.
PyTorch enables research in high-performance and low-latency applications. The library helps build and train neural networks and deep learning algorithms end to end. Being a low-level framework, PyTorch is excellent for handling numerical computations, making it a choice program for mathematicians and statisticians looking for a superior environment to run deep learning algorithms.
PyTorch provides a rich collection of tools, making it useful for machine learning, deep learning research, prototype building, computer vision, and natural language processing (NLP). Data scientists and engineers worldwide use PyTorch in various industries, including automotive, health care, finance, and more.
Features
- Allows data parallelism, meaning users can distribute computational operations between several CPUs and GPUs, making them process data simultaneously
- Back-end framework but works with C++ for front-end interfacing and Python for its architecture
- Has solid compatibility with NumPy, making it an excellent library for converting NumPy objects to tensors
Pros
- Eases debugging processes thanks to its Python IDE and other debugging tools
- Can optimize models in between deploying or training a model with PyTorch
- Has a number of resources to create an ecosystem for accelerating artificial intelligence (AI) research and development processes
- Supports several libraries and APIs that extend its capabilities, like having the Open Neural Network Exchange support for exporting models faster
- Is Pythonic, meaning it follows the syntax and convention of the Python programming language and is easy to learn
Cons
- Not as extensive as TensorFlow and doesn’t cover end-to-end model building to deployment functions
- Has limited features for creating a graph for deep learning models, making users connect to external frameworks for more extensive visualization
4. Microsoft NLTK
Microsoft Natural Language Toolkit (NLTK) is a comprehensive NLP toolkit developed by Steven Bird and Edward Loper. The library was created to advance research in machine learning, NLP, and other related fields like computational linguistics and information retrieval.
The framework provides numerous algorithms for natural language text processing tasks like text classification, tagging, stemming, and lemmatization. As a Python-based library, NLTK is open-source and provides extensive documentation that guides you on using it for various machine learning and data science tasks.
The framework is available on Windows, Linux, and macOS operating systems. ML developers use the framework to build chat boxes and identify sentiments in reviews and natural language.
Features
- Offers a wide range of tools for text processing, including tokenization, part-of-speech tagging, Named Entity Recognition, and parsing
- Has several trained models and data corpora that aid text processing
Pros
- Supports integration with various lexical resources like WordNet to aid language processing
- Offers flexibility with language as it provides compatibility with numerous languages, including English, Spanish, French, German, and Chinese
- Operates with the Apache 2.0 license, which allows commercial application of the framework
- Codes are simple and easy to apply, making the library an excellent choice for practical or research-intensive learning
Cons
- Doesn;t break text into sentences for semantic analysis but only runs syntactic analysis
- Has a relatively slow performance when implementing models compared to natural language frameworks like SpaCy
5. Scikit-learn
Scikit-learn started as a Google Summer of Code project in 2007. Later on, several other developers worked on the project to produce a more extensive framework, which was released to the public in 2010.
Scikit-learn builds on several Python libraries like NumPy, matplotlib, Pandas, and SciPy. The machine learning framework provides various classification, regression, and clustering algorithms.
Further, it has a comprehensive toolkit for predicting future events based on analyzed data. Companies like Spotify and J.P. Morgan use this framework for various ML applications, including recommender systems, prediction models, and data clustering software.
Feature
- Contains an extensive collection of supervised learning algorithms, including models for linear regression and decision trees
- Has numerous helper functions, like the hyperparameter and data preprocessing tools
- Dimensionality reduction feature allows you to reduce the number of attributes in a dataset using visualization and summarization methods
Pros
- Easy to use
- Can define and compare machine learning algorithms and processes
- Has extensive documentation that guides users through how to use the library
- Versatile and relevant for various processes, like creating neuroimages and predicting customer behavior
- Has a vast community and numerous authors—as a result, it gets frequent updates
Cons
- Simple abstraction may tempt beginners to skip ML foundations
- Not suitable for string processing
6. Caffe
Caffe is a deep learning framework that can be used for machine learning. The library is particularly common for image classification, speech recognition, and computer vision.
Caffee operates by analyzing data in two steps: the training phase and the prediction phase. For example, when classifying images, the algorithm trains to identify images by learning from prelabeled images and predicts unlabeled images in the next phase.
Caffe benefits include its ease of use, flexibility, and ability to run on multiple GPUs. Caffe also has a large community of users and developers, which can be helpful when you’re trying to learn more about machine learning or deep learning.
Features
- Integrates with Apache Spark
- Expressive architecture encouraging application and innovation
- Extensible code fosters active development
- Fast, well-tested C++ code for CPU and GPU computation
- Pre-built models and use cases available through the Caffe Model Zoo
Pros
- Excellent performance for image processing and CNNs
- Good for deploying deep learning models in production environments
- Strong community support and a rich ecosystem of models and extensions
Cons
- Primarily focused on computer vision tasks, which may limit its versatility for other types of machine learning problems
- Can be complex to set up and configure, especially for beginners
- Less flexible for defining custom layer types compared to some newer frameworks
Machine learning framework FAQ
Machine learning is an in-demand field in the tech industry today, growing in relevance across IT fields like robotics, big data, and software development.
In that light, this section aims to answer some common and crucial questions about machine learning frameworks.
What makes a good machine learning framework?
Various ML frameworks exist today, so choosing the right one for your project can be difficult. You should consider the type of computational graph the framework uses.
A computational graph allows you to represent your computations as a graph, which makes it easy to visualize and understand the workflow as you run your algorithms. Frameworks that use computational graphs (like PyTorch) allow for running parallel computations, speeding up training.
There are two main types of machine learning algorithms based on their computational graphs.
- Static algorithms. Static frameworks learn from prelabeled data that doesn’t change. They’re typically easier to use and require less code to get started. They also tend to be more efficient since the graph is compiled ahead of time. However, static frameworks aren’t as flexible as dynamic frameworks. TensorFlow is a typical example of a framework that uses static graphs.
- Dynamic algorithms. Dynamic frameworks learn from data that’s constantly changing. Since they allow you to run each code line, these frameworks generate graphs in alternate layers and can be easier to debug. Such features make them essential for building and training deep learning processes and neural network models. An example of a framework that uses dynamic graphs is the PyTorch library.
Which is the best framework for machine learning?
This question has no one-size-fits-all answer, as the best framework for machine learning will depend on your specific needs and goals. However, a few popular frameworks are widely used in the field of machine learning, such as TensorFlow, PyTorch, and Keras.
Each framework has its advantages and disadvantages. For example, PyTorch is more suited for research than TensorFlow since it’s easier to learn and debug. On the other hand, TensorFlow has larger datasets and is a better option for the production and deployment of models.
Evaluate each option to determine the best fit for your project. Here are some tips for selecting the right ML framework:
- Consider the project requirements. Before starting a ML project, point out the scope behind the project. Like the example cited earlier, PyTorch is a better research choice than TensorFlow. Besides the scope, determine what type of project you’ll run, the programming language you’ll use to develop your AI models, and the compatible frameworks. While Python is a popular choice, developers may also use languages like Java, C++, R, or Scala.
- Think about your scaling and deployment needs. When training models, scaling refers to the volume of data the model can process at a time and the speed required to achieve its results. During deployment, scalability refers to how many people can use the model simultaneously. Typically, you’ll develop and run your models in different environments using large-scale distributed networks. Consider which frameworks support both types of scaling. Examples include TensorFlow and PyTorch.
What are the 3 types of learning in machine learning?
Machine learning includes three types of learning: supervised, unsupervised, and reinforcement.
- Supervised learning is where the machine is given a set of training data and can learn and generalize from that data. For example, if you want a model to recognize an orange, you’ll input several images of different oranges so the machine can learn different patterns from the images to make predictions. Supervised learning is often used in making sales forecasts, customer churn prediction, and sentiment analysis models.
- Unsupervised learning describes a condition where machines process data without receiving specific instructions. The machine has to learn from the data itself and try to find patterns. In practice, unsupervised learning is applied when creating models that detect anomalies in datasets. For example, the financial forensics industry uses unsupervised models to detect fraudulent transactions. The health industry also uses unsupervised learning models for cancer research to detect abnormal cell growth.
- Reinforcement learning occurs when the machine is given a goal to achieve and allowed to learn from its own experiences, trying different actions to see which ones result in the best outcomes. A typical application is self-driving cars. The model analyzes the current position of the vehicle and the objects around it before deciding which direction to move. As it makes those decisions, the model notes which action produced the most effective results in specific situations. Another example is recommender systems, where the algorithms recommend random music or videos and learn how you interact with each recommendation.
Get help from machine learning experts
Machine learning is revolutionizing the way we do business. A recent report from IBM indicates a fast-growing adoption of AI solutions among businesses—with a 42% rate in 2024.
Despite the growing relevance of AI, the report highlights challenges businesses face, including limited skilled workers, lack of tools to develop models, and project and data complexity.
Thankfully, Upwork provides a platform that allows you to outsource your projects to skilled ML developers. Whether you want to leverage neural network solutions for video game development or build deep learning models to automate data analysis processes, you can visit Upwork to hire machine learning experts.
These professionals will assess your company’s needs or projects and determine which framework fits best. Access Upwork’s talent pool by creating an account as a client and searching for experts using the search tool.
Likewise, if you’re an independent professional—from a software developer to an AI engineer—create an account and complete your profile to get started and find jobs on Upwork.
Upwork is not affiliated with and does not sponsor or endorse any of the tools or services discussed in this article. These tools and services are provided only as potential options, and each reader and company should take the time needed to adequately analyze and determine the tools or services that would best fit their specific needs and situation.