Top 10 AI Development Tools, Frameworks for 2024

This blog is written with an objective to keep you updated with the top AI Development Tools for the upcoming year. Also; I have covered other aspects like their features and few basic concepts for those who are new with Artificial Intelligence.

Technology is intervening in every aspect of our lives. Be it shopping, eating, traveling or even reading; our everyday activities are getting transformed to a whole new extent. Artificial intelligence is one such technology which has introduced revolution for the humankind.

The most basic and the most frequently asked question.

What is Artificial Intelligence?

Artificial Intelligence is how a digital computer system can demonstrate behaviors that are associated with human intelligence like reasoning, learning, problem-solving, manipulation, motion, and knowledge representation.

Along with that; developers are also working on making a machine creative and socially intelligent. In simple terms; expecting a robot to understand our emotions and respond to us logically.

What are the Applications of Artificial Intelligence?

Today; Artificial Intelligence is almost everywhere. From Siri and Alexa to Fraud and Spam detection we are using Artificial Intelligence in almost every aspect of our lives.

Here is the list of well-known areas where Artificial Intelligence is transforming lives:

1. Optical character recognition.
2. Handwriting recognition.
3. Speech recognition.
4. Face recognition.
5. Artificial creativity.
6. Computer vision, virtual reality, and image processing.
7. Photo and video manipulation.

Examples of Artificial Intelligence

Have you heard of the benefits of AI for the insurance industry?

With tools like AI-enabled Insurance CRM solutions, insurance industry is revolutionizing at a quick speed. Not only insurance; there are a lot of other industries like Manufacturing, Health care, Construction, Retail, Business Intelligence, City Planning, Education, Fashion and many more.

List of examples of Artificial Intelligence in Our Daily Life:

1. Spam Filters(Emails, Calls, Messages, SMS)
2. Smart Email Categorisation.
3. Voice To Text Features.
4. Smart Personal Assistants, Such As Siri, Cortana And Google Now.
5. Automated Responders And Online Customer Support.
6. Process Automation.
7. Sales And Business Forecasting.
8. Security Surveillance.

In this blog today; we are trying to cover everything about the best AI development tools. We have included a few frequently asked questions as well. For each framework, we have explained it’s top features which can help you to compare which is the best.

Let us begin our journey of top AI Development Frameworks.

Top #10 Artificial Intelligence Frameworks of 2024

List of 10 best AI Development Tools, Frameworks for 2024

1. Scikit-learn
2. TensorFlow
3. Caffe
4. Theano
5. Keras
6. MXNET
7. Microsoft CNTK
8. Auto ML
9. Accord.NET
10. Torch/ PyTorch

Scikit-learn

Scikit-learn uses a consistent interface in Python to provide learning algorithms. These algorithms can be supervised or unsupervised. It is one of the most powerful Open Source AI Development Tools Frameworks.

At present, it is distributed under different Linux distributions. The main objective of Scikit-learn is to encourage commercial and academic use.

There is a stack which needs to be installed before using Scikit-learn, it includes:

1. IPython: For enhanced interactive console
2. SciPy: The fundamental library for scientific computing
3. Sympy: Symbolic mathematics
4. NumPy: The Base n-dimensional array package
5. Pandas: Data structures and analysis
6. Matplotlib: Comprehensive 2D/3D plotting

Features of Scikit-learn

• Cross-Validation: Scikit-learn allows the developers to estimate the performance of supervised models for the unseen data.
• Clustering: This feature is mainly for the unsupervised classification. Data like Kmeans which is unlabeled can be grouped.
• Feature selection: To build supervised models, it is easy to identify meaningful attributes.
• Feature extraction: To identify the text and image data attributes.
• Datasets: to generate and test datasets that have a specific property. Later on, investigating their model behavior.
• Dimensionality reduction: To reduce the number of required attributes in data for visualization, feature selection, and summarization.
• Supervised model: a huge array which is not limited to discriminate analysis, lazy methods, generalized linear models, neural networks, support vector machines, and naive byes.

TensorFlow

One of the Open Source AI Development Tool for Everyone.

TensorFlow is a popular machine learning framework. It is known to carry out high-performance numerical computations. It has excellent architecture support which is beneficial for easy deployment of computations across mobile phones, desktops, a cluster of servers, etc.

Why TensorFlow is Popular among Devs?

The major reason for the popularity of TensorFlow is its regular updates in the ecosystem. Every issue faced by researchers and developers is addressed in the updates. Overall, TensorFlow is a whole ecosystem which can provide a solution for real-world problems, when it comes to artificial intelligence.

Features of TensorFlow

• Robust Production: TensorFlow serves a direct path for the production. Despite language or platform selection, with TensorFlow anyone can deploy and train its model smoothly.
• Model building: With multiple abstractions with TensorFlow, you can choose the right one as per your requirements. It has high-level Keras API to build and train the models. This makes getting started with TensorFlow easy.
• Supports powerful experiments: For research and development; TensorFlow supports the creation of complex technologies with its flexibility and control over the best features. Besides; It also supports experimentation with models and powerful add-on libraries.
• Distributed training: TensorFlow has supported exploring of the two of the major distributed methods.
  • Which are:
    1. To reduce the training time; TensorFlow allows to distribute the training time of the neural network model over multiple kinds of servers.
    2. To search for good hyperparameters; TensorFlow allows running multiple experiments parallelly over different servers.
• Accessible syntax: TensorFlow provides syntax which is accessible and readable easily. This leads to easy ways to program any resource.
• More network control: With higher control over the network; TensorFlow allows developers to experiment and understand how the implementations occur in the network for any operation.

CAFFE 2 – AI Development Framework Tool

Caffe is another popular framework in the field of Artificial Intelligence. With speed, expression and modularity in mind; Caffe is built to support developers and researchers. Caffe stands for Convolutional Architecture for Fast Feature Embedding; which is written in C++ with an interface of python.

Features of Caffe

• Active development: With its extensible code; Caffe helps in the active development process. In its first year; developers showed an exceptional interest in Caffe which lead to adding other significant changes. With the contribution of researchers and developers, Caffe is serving state of the art models and code.
• Speed: For scenarios that include industry deployment and research experiments, speed is an asset. Hence; Caffe becomes popular. Caffe can process more than 60M images per day. That means; 1 ms/image for interference and 4 ms/image for learning purposes. As per Berkeley’s vision; Caffe is one of the fastest known convent implementations present today.
• Expressive architecture: Caffe supports innovation and application with the help of its expressive architecture. Without any need for hard coding; models and optimizations can be defined by configuration only.
• Huge community: Caffe has a community of developers and researchers around the world on GitHub and Caffe users hub. The community has helped multiple startup prototypes, academic research projects, as well as large scale industrial processes.

Theano – AI Development Tools

Theano is a python library that helps in defining, optimizing and evaluating mathematical expressions. It also supports expressions with multi-dimensional arrays as well.

Since 2007; Theano has been empowering intensive scientific investigations that are huge in numbers and computations. It can combine computer algebra systems with the aspects of the compiler.

In scenarios where multiple evaluations are each evaluated at least once; Theano can reduce the compilation/ analysis overhead. Even after it provides symbolic features like automatic differentiation.

Features of Theano

• Transparent usage of GPU: With GPU; Theano can perform computations that are data-intensive at 140 times faster speed as compared to any CPU.
• Dynamic generation of C code: Theano can evaluate expressions at a faster rate.
• Integration with NumPy: Theano has tight integration with NumPy; arrays called numpy.ndarrays are used in the Theano complied functions.
• Symbolic differentiation: Theano can compute derivatives of one or many inputs for any function.

Keras

Did You Know that Keras can run on the top of Theano and Tensorflow. It is a neural networks API that is high-level when compared to its competitors. The major objective behind Keras was to enable faster experimentation.

Keras can run smoothly on both; CPU & GPU. It supports both the network types; convolutional and recurrent also the combination of both.

Features of Keras

• Ease of extensibility: Developers can add new modules easily as new functions or classes. Also; the existing modules have ample examples for the support. The complete expressiveness which comes from the ease to build new modules; makes Keras most loved for advanced research.
• User-friendliness: The best part of Keras is; it is designed for humans. Unlike others; which are designed for the machines. One of its goals is to make the user experience the best features by keeping it at the front and center.
• Less cognitive load: Keras reduces the cognitive load with simple and consistent APIs. It provides actionable and clear feedback on any type of user error. Keras minimizes the number of actions by a user required for any common use case.
• Modularity:
  • What is a model?
  • A model is a sequence of fully configurable and standalone modules that can work together with as few restrictions as possible. Hence; a developer can combine optimizer, neural layers, initialization schemes, cost functions, activation functions, and regularization schemes to build new models.
• Blessing of Python: Models in Keras are written in Python code. Hence; it is easier to debug, is compact and provides ease of extensibility.

MXNET

MXNET is another one of the top open-source deep learning frameworks. It is powerful enough to support industry verticals like transportation, healthcare, and manufacturing. MXNET is flexible, ultra-scalable and lean. Hence; it supports multiple languages and a flexible programming model. Also; it allows model-training faster.

Features of MXNET

• Multiple language bindings: MXNET supports Julia, Scala, Clojure, C ++, Perl, R and Java along with the deep integration into Python.
• Hybrid front end: To provide speed and flexibility; a hybrid front end transitions smoothly between Gluon’s eager imperative mode and symbolic mode.
• Distributed Training: MXNet allows you to make the most out of your hardware, whether it is multi-GPU or multi-host training with near-linear scaling efficiency.
• Scalability: MXNET is built by keeping scalability in mind. It provides support for multi-machine and multi GPU training.
• TVM Support: MXNET has TVM support which helps developers to run the program on a whole host of new devices.
• Tools and Libraries: MXNET has a whole ecosystem of tools and libraries which enables the use of cases in NLP, computer vision and time series.

Microsoft CNTK

CNTK is a deep learning library released by Microsoft. As per Microsoft; CNTK is a unified deep-learning toolkit. It uses a directed graph to describe neural networks as the steps of computational series.

CNTK stands for Computational Network ToolKit. Without any regard to the location of GPUs; CNTK allows parallelization across multiple GPUs and multiple machines. Along with that; it implements backpropagation and stochastic gradient descent(SGD).

Features of CNTK

• Performance: CNTK can achieve the best performance on the production systems and benchmark tasks.
• Efficiency: CNTK is efficient to train production systems quickly.
• Flexible: it is open to try out new ideas and support multiple tasks like text, image, and speech.

Google AutoML

AutoML is Google’s product. It was co-opted officially in May 2017 for the neural network architecture search of Google. AutoML is one of the most powerful and flexible Artificial Intelligence frameworks.

Features of AutoML

• Quick and easy deployment: Auto ML has an easy graphical user interface that helps the developers to evaluate, improve, train and deploy models that are based on their data. They can build their own custom machine learning models in just a few minutes.
• Smoothly train machine learning models: With Google’s neural architecture search technology and transfer learning; Auto ML helps developers to train models as per the requirements of any business.
• Best performance: Auto ML leverages Google research technology to make the machine learning models perform faster. Along with that, it provides more accurate predictions than any other framework.

Accord.NET

Accord.NET framework is a .NET based machine learning framework that has numerous ready-to-use libraries majorly for image and audio processing. Accord.NET is preferred for commercial use due to its ability for developing production-grade computer vision along with statistics applications and signal processing.

Features of Accord.NET

• Classification: With Support Vector Machines, Logistic Regression, Decision Trees Accord.NET is helping the developers to solve the most common problem.
• Clustering: Accord.NET app applies a clustering algorithm in arbitrary data including videos, audios, images and data tables.
• Hypothesis tests: Accord.NET has 35 plus statistical hypothesis tests which include non-parametric tests like Kolmogorov-Smirnov test and the Sign Test for the Median along with one way and two way ANOVA tests.
• Transformation of Audio signals: Accord.NET parse, load, filter, save and transform audio signals like applying audio processing filters in frequency as well as spatial domain.

And our last but not the least framework in the list of top AI-driven development tools is Torch.

Torch

Torch is a scientific computing framework as well as a scripting language. It is entirely based on Lua programming language.

Torch can serve the entire range of algorithms for deep machine learning. By leveraging the scripting language LuaJT and an underlying C implementation Torch is winning the race.

Features of Torch

• GPU support which is fast as well as efficient.
• Strong N-dimensional array.
• Multiple routines for slicing, indexing and transposing.
• Best interface to C via LuaJT.
• Energy-based models as well as the neural network.
• Numerix optimization routines.
• Linear algebra routines.
• Huge ecosystem for community-driven package.

FAQs about AI Development Frameworks & Tools

Now that we are well aware of the top artificial intelligence frameworks, let us have a look at a few common questions and their answers.

Over to You!

Artificial Intelligence in the future and present. With projects like Google’s brain, there is no doubt that AI can transform our daily life.

Top business companies are adopting this technology to improve their operations, serve better experience to their consumers and win the battle with their competitors.

What are your plans? When do you want to leverage the benefits of Artificial Intelligence for your company?

If you are looking for top software development company to transform your idea to reality or to integrate any existing AI application to your business then you are at the right place.

At Samarpan Infotech; we have AI experts. With an intense knowledge and skills, our team is transforming several businesses and ideas around the world.

Confused about how to implement AI in your business or industry? Talk to our expert business analysts. Our team of business analysts will help you understand the importance of AI for your business. As well as you can discuss the business model and revenue model of your project as well. Get in touch today.

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