Top 8 Most Popular Machine Learning / Artificial Intelligence Software in 2023

Artificial Intelligence (AI) and Machine Learning (ML) have altered how businesses function and how people live. As the field grows and evolves, so does the number of software libraries and development tools available to enable AI and ML development.

If you’ve ever considered constructing your own GPT model to replace ChatGPT or give OpenAI a run for their money, this post will cover some of the most popular AI and ML software libraries and tools (including the one used by OpenAI).

I’ll also provide some examples of how these tools and libraries are typically put to work, though please note that this is by no means an exhaustive list of all possible applications for any given tool or library. Most of them can be applied to any situation, but there are a few that stand out as being especially well-suited to a specific use.

1. TensorFlow

When it comes to dataflow and differentiable programming, the open-source software package TensorFlow is hard to beat. It was introduced in 2015 and was created by the Google Brain team. Specifically, TensorFlow is put to use in deep learning and machine learning programs, including neural networks.

When it comes to developing and deploying machine learning models, TensorFlow is a versatile and effective platform. It’s useful for both academic and industrial research because of the variety of hardware it supports.

Among TensorFlow’s many powerful capabilities is automated differentiation, which facilitates the computation of gradients for very involved mathematical expressions. In this way, improving model performance is straightforward. In addition to its ease of use, TensorFlow’s pre-built model library makes it quick and simple for developers to begin working with machine learning.

Use cases:

1. Image classification: The Waymo self-driving car project uses TensorFlow for object detection and classification. For more details, check their data set. Also, additional tutorials for image classification as well as its use with Google Cloud are available.
2. Natural language processing: TensorFlow provides NLP tutorials with code and explanations for several NLP tasks, including text classification and sentiment analysis. You can find these tutorials on the TensorFlow website.
3. Speech recognition: The TensorFlow Speech Recognition Challenge provides a dataset and code for building speech recognition models using TensorFlow. You can find more information about the challenge on the TensorFlow website.
4. AlphaGo: Created by Google’s DeepMind, this computer program is credited with taking down the current Go world champion. Visit DeepMind’s website for additional details about AlphaGo. A newer and stronger version of AlphaGo called AlphaGo Zero was trained using TensorFlow. Check this article from Oracle for more information.

Airbnb, Airbus, ARM, and Intel are just a few of the firms that use TensorFlow, which is recognized as the most popular AI framework.

2. PyTorch

To put it simply, PyTorch is an open-source machine learning library written in Python that is based on the Torch framework. It was first launched in 2016 after being developed by Facebook’s artificial intelligence research team. The fields of computer vision, NLP, and generative models all make use of PyTorch. The platform is user-friendly and adaptable, making it ideal for developing and training ML models.

PyTorch’s dynamic computation graph is one of its main selling points since it makes model-building more adaptable and efficient. As an added bonus, PyTorch offers a model-centric high-level API that makes it simpler for programmers to get into the world of machine learning.

Use Cases:

1. OpenAI: OpenAI has used the PyTorch library to create multiple state-of-the-art AI models and has contributed to the library’s development as a whole. In order to learn more about OpenAI and how it employs PyTorch, you may visit their website.
2. Computer vision: Many computer vision tasks, such as picture classification and object recognition, have code and explanations available in the PyTorch tutorials. These guides are available on the PyTorch website.
3. Natural language processing: The PyTorch NLP tutorials provide code and explanations for several NLP tasks, including text classification and sentiment analysis. You can find these tutorials on the PyTorch website.
4. Generative models: The PyTorch tutorials also provide code and explanations for building Generative Adversarial Networks (GANs) for tasks such as image generation. You can find these tutorials on the PyTorch website.

Companies like OpenAI, Facebook, Nvidia, Tesla, and Uber are just a few that are employing the famous PyTorch framework.

3. Keras

Keras is a free and open-source library that offers a Python API for artificial neural networks. By its creation, the authors hoped to facilitate developers’ entry into the field of deep learning. Keras’s intuitive interface, modular structure, and scalability make it ideal for rapid prototyping.

Keras’s simplicity and user-friendliness are two of its most appealing qualities. In order to make deep learning models accessible to more developers, it provides a high-level API for doing so. As an additional benefit, Keras’s modular design makes it simple for programmers to add new features to their models.

Use cases:

1. Image classification: It has been demonstrated that Keras can be utilized for picture classification tasks like object detection. For instance, one can get a guide on how to create an image classification model with Keras on the Keras blog.
2. Natural language processing: Natural language processing activities like text categorization and sentiment analysis are also possible with Keras. The Keras blog, for instance, has an in-depth guide on creating a text classification model with Keras.
3. Healthcare: Keras has been put to use in the medical field for purposes including disease diagnosis from medical photos. For creating a pneumonia classification model in Keras, for instance, a dataset and code are provided in the publication “Chest X-Ray Pictures (Pneumonia).“

Kera is the most used deep learning framework on Kaggle.

4. scikit-learn

scikit-learn is a Python machine learning toolkit that facilitates access to effective and user-friendly tools for data mining and analysis. It was created so that more programmers could use machine learning tools. Classification, regression, clustering, and dimensionality reduction are just some of the many uses for scikit-learn.

Scikit-learn’s ease of use and effectiveness are two of its most appealing qualities. It makes it simple for developers to get started with machine learning by providing a variety of pre-built models and algorithms for common tasks. Data pre-processing and feature extraction tools are also included in scikit-learn, giving it a comprehensive solution for ML.

Use cases:

1. Customer segmentation: scikit-learn has been used for customer segmentation tasks, such as grouping customers into similar groups based on their behavior or characteristics. Here’s a tutorial to achieve this.
2. Fraud detection: scikit-learn has also been used for fraud detection tasks, such as identifying fraudulent transactions in financial data. Here are more details on the subject.
3. Recommender systems: scikit-learn has been used for building recommender systems, such as suggesting items to users based on their behavior or preferences. Here’s a tutorial to achieve this:

Bonus: More about recommender systems in general.

Spotify, J.P. Morgan, Evernote, and Booking.com are just a few of the companies that have adopted and benefited from it. Microsoft and BNP Paribas Cardif are also backing up the project.

5. XGBoost

XGBoost, short for “eXtreme Gradient Boosting,” is a toolkit for distributed gradient boosting that has been tuned for speed, adaptability, and portability. As of its 2014 debut, it had been created by Tianqi Chen. Tree-based model building with XGBoost is common in Kaggle tournaments.

XGBoost’s speed is one of its most appealing qualities. It is optimized for speed and scalability, making it suitable for use with massive data sets. In addition to being a robust machine learning solution, XGBoost offers a variety of tools for model tuning and optimization.

Use cases:

1. Sales forecasting: XGBoost has been used for sales forecasting tasks, such as predicting future sales for a company.
2. Customer churn prediction: XGBoost has also been used for customer churn prediction tasks, such as identifying customers who are likely to stop using a company’s services.
3. Stock price prediction: XGBoost has been used for stock price prediction tasks, such as predicting future stock prices for a company. There are many articles and papers on the internet about XGBoost and its use in Stock price predictions (like this or this), or if you want just a code example, you can check this series or this snippet.

XGBoost is sponsored by companies such as Nvidia and Intel.

6. LightGBM

Tree-based learning techniques are at the heart of the LightGBM gradient boosting system. Microsoft created it, and it came out in 2016. LightGBM is efficient and scalable, and it can process massive amounts of data.

LightGBM’s lightning quick performance is a selling point. It’s optimized for speed, giving it a viable option for use with large datasets, and it’s been widely used. LightGBM is an effective machine learning solution because of the wide variety of tools it offers for model refining and optimization.

Use cases:

1. Credit risk prediction: LightGBM has been used for credit risk prediction tasks, such as predicting the likelihood of a loan default. For more information, check this article or this research paper.
2. Fraud detection: LightGBM has also been used for fraud detection tasks, such as identifying fraudulent transactions in financial data. Check out this paper on the subject.
3. Sales forecasting: LightGBM has been used for sales forecasting tasks, such as predicting future sales for a company. For more details, check this article.

7. FastAI

FastAI was developed as a deep learning package that utilized the PyTorch framework. The main reason it was created was to help more programmers use deep learning. Vision, text, tabular, and collab (collaborative filtering) models are all supported by FastAI’s high-level APIs.

FastAI’s simplicity and user-friendliness are two of its most appealing qualities. In order to make deep learning models accessible to more developers, it provides a high-level API for doing so. FastAI is a comprehensive deep learning solution since it offers a wide variety of pre-built models and tools for common deep learning tasks.

Image categorization, NLP, and generative models are just a few of the many applications of FastAI. Some high-profile projects have used FastAI, including research into deep learning models for driverless vehicles.

Use cases:

1. Computer vision: FastAI has been used for computer vision tasks, such as image classification and object detection. For example, the FastAI website provides a tutorial for building an image classification model using FastAI.
2. Natural language processing: FastAI has also been used for natural language processing tasks, such as text classification and sentiment analysis. For example, the FastAI website provides a tutorial for building a text classification model using FastAI.
3. Tabular data: FastAI has been used for tasks involving tabular data, such as regression and binary classification. For example, the FastAI website provides a tutorial for building a regression model for tabular data using FastAI.

8. MATLAB

MATLAB is a platform and language for numerical computation. Produced by MathWorks, it first appeared in 1984. Classification, regression, clustering, and deep learning are just few of the machine learning tools available in MATLAB. Due to its user-friendliness, it finds widespread application in academic and scientific settings.

MATLAB’s accessibility is a major selling point. With its high-level application programming interface (API), machine learning may be used by a greater variety of programmers. MATLAB is a comprehensive solution for machine learning since it includes not only a wide variety of algorithms, but also pre-processing and visualization capabilities for data.

Use cases:

1. Signal processing: MATLAB has been widely used for signal processing tasks, such as filtering and transforming signals. For example, the MathWorks website provides a product dedicated to this.
2. Control systems: MATLAB has also been used for control systems tasks, such as designing and simulating control systems. For example, the MathWorks website provides a product dedicated to this.
3. Image processing: MATLAB has been used for image processing tasks, such as processing and analyzing images. For example, the MathWorks website provides a product dedicated to this.

Tips and guidelines for using AI/ML software

Here are some guidelines to follow when working with AI/ML tools in general, regardless of which ones you end up picking:

1. Complexity: AI/ML algorithms and models can be difficult to understand, even for seasoned software engineers and data scientists. As a result, it may become more challenging to debug and optimize AI/ML systems, and there may be a greater chance of errors occurring.
2. Data quality: the quality of the data is essential for the success of artificial intelligence and machine learning algorithms. The AI/ML system will be erroneous if trained on noisy, skewed, or otherwise defective data. It might be difficult and time-consuming to clean and preprocess data.
3. Computational resources: computing resources, as some AI/ML algorithms can be quite memory and processing power hungry. This can make it more expensive to run AI/ML systems on the cloud and make it harder to run AI/ML models on small devices like smartphones.
4. Overfitting: Overfitting is the failure of an AI or ML model to generalize to data it has never seen because it has been overly adapted to the training data. This can make the AI/ML system more vulnerable to adversarial attacks and lead to poor performance on real-world data.
5. Bias: AI/ML systems can amplify and generalize unjust or discriminating outcomes that exist in the training data. Facial recognition algorithms that have been improperly trained, for instance, by using biased data, can misidentify members of particular minority groups.

There are, however, a few more broad considerations that I will try to discuss in a future piece if there is enough interest.

Conclusion

These are only a small selection of the many available AI and ML software libraries and tools.

There are advantages and disadvantages to each of these tools; determining which is appropriate for a given work requires considering the task’s unique parameters. New tools and technologies are likely to emerge as the area of AI and ML continues to evolve; as a result, it will be necessary for developers to be abreast of the most recent advancements in the field.

Common questions about AI & ML development

What scientists mean by “Artificial Intelligence” (AI) is when robots can do tasks that normally need human intelligence and judgment. Artificial intelligence (AI) is the study and creation of algorithms and computer programs capable of doing activities that traditionally require human intellect, such as voice recognition, decision making, learning from experience, and solving complicated problems. Several people classify AI as either narrow or weak, which is programmed to carry out a limited range of activities, or general or strong, which can carry out any intellectual work a person can.

Most of the artificial intelligence systems in use today belong to the narrow AI category, whereas strong AI is still in the theoretical stage. Healthcare, finance, transportation, and industry are just a few of the many industries that might benefit from AI. Machine learning, natural language processing, and computer vision are only some of the most prevalent forms of AI utilized in these kinds of programs. Self-driving vehicles, virtual assistants, and robots are just some of the intelligent systems that have benefited from the use of AI.

Machine learning explained – In machine learning, computers are taught to acquire new skills from data without being given any specific instructions. To rephrase, machine learning techniques let computers get better at doing a task the more data they are exposed to. Data preparation, model training, and model assessment are the three key phases of a conventional machine learning process. To guarantee the data is fit for use in the model, it must be gathered, cleansed, and preprocessed in the data preparation step. During model training, the machine learning algorithm is provided with the prepared data and taught to draw conclusions or make decisions based on those conclusions or judgments. Lastly, in the model assessment stage, the trained model is put through its paces using a new set of data to see how well it performed.

Supervised learning, unsupervised learning, and reinforcement learning are only few of the machine learning algorithms available. Supervised learning involves teaching a machine to spot patterns and make predictions by providing it with a collection of labeled data. Machines may learn to recognize patterns and correlations in unlabeled data through unsupervised learning. Once the system receives input from its surroundings, it is able to make better judgments in the process of reinforcement learning. Image and audio recognition, natural language processing, fraud detection, recommender systems, predictive maintenance, and many more areas may all benefit from machine learning.

Machine learning (ML) is a subset of artificial intelligence (AI), although the two are not interchangeable.

The term “artificial intelligence” (AI) describes the development of computer systems that can mimic human intelligence and decision-making. Artificial intelligence (AI) is the study and creation of algorithms and computer programs capable of doing activities that traditionally require human intellect, such as voice recognition, decision making, learning from experience, and solving complicated problems. Nevertheless, machine learning is a subfield of AI concerned with the study and creation of methods that will allow computers to learn from data without being explicitly taught.

To rephrase, machine learning techniques let computers get better at doing a task the more data they are exposed to. Hence, while machine learning is a methodology that helps bring about AI, AI itself comprises a far wider spectrum of methods and approaches. Rule-based systems, expert systems, genetic algorithms, NLP, and other forms of AI are all possible. In essence, artificial intelligence (AI) is a larger concept that includes machine learning and other approaches, whereas machine learning is a specific technique inside AI.

Advances in Machine Learning and Artificial Intelligence

Programming algorithms and computer programs to carry out intelligent activities, spot patterns, and make judgments based on data constitutes artificial intelligence (AI) and machine learning (ML) development.

AI and ML development follows a standard multi-stage methodology that includes the following:

Identify the issue:

The first step in developing an AI or ML system is to precisely identify the issue that needs fixing. The first step is to define the system’s intended function and the data it will process. Inputting Data: It is important that the data used to train and evaluate the AI or ML model be properly prepared. In order to guarantee the data is reliable, full, and applicable, it must be cleaned, transformed, and pre-processed. Selection of Models: There is a wide variety of AI and ML algorithms to pick from, and the one that works best is going to be different for every problem and set of data.

To create the AI or ML model, the selected algorithm will be implemented. To train an AI or ML model, one must use this prepped data. Now the algorithm is able to learn from the data it has been given and generate predictions based on those inputs. When the model has been trained, it is put through a series of tests on new data to determine how well it performs. This analysis ensures that the model is robust and can be used to generate predictions on new data.

Deployment:

Putting the model into production once it has been built and tested. Putting the concept into action means incorporating it into the workflows and infrastructure of the company. AI and ML models need to be constantly checked and updated to guarantee they produce reliable results. To keep the model performing well over time, it may require retraining with fresh data, algorithm updates, or other adjustments.

Programming, data analysis, and domain-specific expertise are only few of the many abilities necessary for developing AI and ML systems. Libraries, frameworks, and cloud-based services are just a few examples of the various tools and platforms available to make the creation of AI and ML solutions easier for developers.