Artificial Intelligence

AI software list 2024


Do you know about AI software?

AI software list 2024 – Artificial Intelligence (AI) refers to the development of computer systems that can perform tasks that are typically require human intelligence. This includes learning from experience (machine learning), understanding natural language, recognizing patterns, and solving complex problems. AI systems use algorithms to analyze data, make decisions, and improve their performance over time. Applications range from virtual assistants and image recognition to autonomous vehicles and medical diagnosis. AI aims to mimic human cognitive functions, enabling machines to adapt and excel in diverse tasks, revolutionizing industries and daily life by enhancing efficiency, innovation, and decision-making capabilities.

some popular lists of AI software:-

  • TensorFlow
  • PyTorch
  • Scikit-learn
  • Keras
  • Microsoft Cognitive Toolkit (CNTK)
  • Apache MXNet
  • OpenCV
  • NLTK (Natural Language Toolkit)
  • Spacy
  • Pandas
  • Apache Spark MLlib
  • IBM Watson Studio
  • Google Cloud AI Platform
  • TensorFlow
  • PyTorch
  • Scikit-learn
  • Keras
  • Microsoft Cognitive Toolkit (CNTK)
  • Apache MXNet
  • OpenCV
  • NLTK (Natural Language Toolkit)
  • Spacy
  • Pandas
  • Apache Spark MLlib
  • IBM Watson Studio
  • Google Cloud AI Platform



TensorFlow:  – This AI developed by Google. It is an open-source machine learning framework widely used for deep learning applications. This software is a library for numerical computation for using data flow graphs. It is primarily used to build and train the machine learning models

Here is the some list of Ai tools of tensorflow are following below:-

1.Tensorflow Lite:- This software is designed for mobile and embedded devices. It gives permission to on-device machine learning, allowing models to run locally on smartphones, IoT devices, and other edge devices.

2. TensorFlow Extended (TFX):– This software provides an end-to-end platform for deploying the production-ready machine learning models. This software facilitates the entire machine learning lifecycle, from data preparation to model deployment.

3. TensorFlow.js:– This software is a JavaScript library that enables training and deployment of machine learning models directly in the browser or on Node.js. this  is useful for tasks like client-side image recognition.

4. TensorBoard:– This software is a visualization toolkit for TensorFlow. It helps users understand, debug, and optimize TensorFlow programs by providing a suite of web applications for inspecting and understanding training runs.

5. TensorFlow Hub:– It is a repository of pre-trained machine learning models. It allows developers to easily reuse and share machine learning components.

6. TensorFlow Model Optimization Toolkit:– This toolkit provides tools for optimizing and fine-tuning machine learning models, including techniques like quantization and pruning to make models more efficient for deployment.

7. TensorFlow Data Validation (TFDV):– It provides a library for exploring and validating machine learning data. It helps identify and understand anomalies and distribution skew in datasets.

8. TensorFlow Probability:– This software provides a library for probabilistic reasoning and statistical analysis. It also gives tools for building probabilistic models using TensorFlow.

9. TensorFlow Federated (TFF):– It gives framework for machine learning and other computations on decentralized data. It also enables  the training models across multiple devices without exchanging raw data.

10. TensorFlow AutoML:– It will provides tools for automating the process of selecting and optimizing machine learning models. It makes it easier for users without extensive machine learning expertise.



PyTorch: – It is an open-source machine learning library developed by Facebook’s AI Research lab it also known for its dynamic computational graph, for making more intuitive for researchers.

Here is a following some notable AI models and projects that have been implemented using PyTorch:

1. ResNet (Residual Networks):– This software provides a deep neural network architecture that introduced residual learning, enabling the training of very deep networks.

2. BERT (Bidirectional Encoder Representations from Transformers):– It is a pre-trained natural language processing model that has achieved state-of-the-art results in various NLP tasks.

3. GPT (Generative Pre-trained Transformer) Models:– This will includes models like GPT-2 and GPT-3, which are large-scale language models capable of generating human-like text.

4. PyTorch Lightning:– It provides a lightweight PyTorch wrapper for high-performance AI research. It will simplifies the training process and includes best practices for reproducibility and scalability.

5. Fastai:– This software provides a high-level deep learning library built on top of PyTorch that simplifies the training process and provides easy-to-use APIs for common tasks.

6. Detectron2:– It is a flexible object detection library built on PyTorch. It will provides implementations of state-of-the-art object detection algorithms.

7. CycleGAN (Cycle-Consistent Generative Adversarial Network):– This software provides a model for image-to-image translation without paired training examples. It has been used for style transfer and other creative applications.

8. DenseNet (Densely Connected Convolutional Networks):– It is a neural network architecture that encourages feature reuse by connecting each layer to every other layer in a feed-forward fashion.

9. VGGNet:– It is a convolutional neural network architecture known for its simplicity and effectiveness. It has different variants like VGG16 and VGG19.

10. Pix2Pix:– It provides a model for image-to-image translation that learns a mapping from an input image to an output image.

11. WaveNet:– It is a deep generative model for generating high-quality raw audio waveforms. It has been used in text-to-speech applications.

12. DCGAN (Deep Convolutional Generative Adversarial Network):– It is a model that uses a generative adversarial network for generating realistic images.

13. YOLO (You Only Look Once):– It is an object detection algorithm that can detect and classify objects in real-time.

14. Torchvision:– It gives the image and video datasets library for PyTorch, including popular datasets like ImageNet.

15. OpenNMT-py:– This software provides an open-source neural machine translation framework built on PyTorch.



 Scikit-learn:– It’s a simple and efficient AI tool for data analysis and modeling. It provides a wide range of machine learning algorithms for classification, regression, clustering, and more.

Here is the following some common machine learning algorithms and techniques that can be implemented using Scikit-learn are as follows:

1. Supervised Learning:

  • Linear Regression
  • Support Vector Machines (SVM)
  •  Decision Trees
  •  Random Forests
  •  k-Nearest Neighbors (kNN)
  •  Naive Bayes
  • Gradient Boosting (e.g., AdaBoost, GradientBoostingClassifier)

2. Unsupervised Learning:

  • K-Means Clustering
  • Hierarchical Clustering
  • Principal Component Analysis (PCA)
  • Independent Component Analysis (ICA)
  • t-Distributed Stochastic Neighbor Embedding (t-SNE)

3. Model Selection and Evaluation:

  • Cross-Validation
  • Grid Search
  • Metrics (e.g., accuracy, precision, recall, F1-score)

4. Preprocessing:

  • Standardization and Scaling
  •    Imputation of Missing Values
  •    Feature Extraction (e.g., text and image feature extraction)

 5. Ensemble Methods:

  •    VotingClassifier
  •   Bagging (e.g., BaggingClassifier)
  •   Boosting (e.g., AdaBoost, GradientBoostingClassifier)

6. Dimensionality Reduction:

  •    Principal Component Analysis (PCA)
  •   Truncated Singular Value Decomposition (TruncatedSVD)

7. Neural Network Support:

  • Multi-layer Perceptron (MLP) using `MLPClassifier` and `MLPRegressor`

  8. Text Analysis:

  •    Text Vectorization (e.g., `CountVectorizer`, `TfidfVectorizer`)
  •    Text Classification (e.g., Naive Bayes for text classification)



 Keras:- This AI is a deep learning API written in Python. It can be designed to be a user-friendly, modular, and extensible. If you’re looking for names associated with AI or machine learning in general, then you might be interested in popular models, algorithms, or frameworks.

Here are the some following key terms and names related to AI, particularly in the context of neural networks and deep learning:

1. Neural Networks:

  •  Perceptron
  •  Multilayer Perceptron (MLP)
  •  Convolutional Neural Network (CNN)
  •  Recurrent Neural Network (RNN)
  •  Long Short-Term Memory (LSTM)
  •  Gated Recurrent Unit (GRU)

 2. Deep Learning Frameworks:

  • TensorFlow
  •    PyTorch
  •    Keras (now integrated into TensorFlow)
  •    Caffe
  •    Theano

  3. Pre-trained Models:

  • AlexNet
  •  VGG16, VGG19
  •  ResNet
  •  Inception (GoogLeNet)
  •  BERT (for natural language processing)
  •  Geoffrey Hinton
  •  Yann LeCun
  •  Yoshua Bengio
  •  Andrew Ng

AI-related Terms:

  • Machine Learning
  •    Natural Language Processing (NLP)
  •    Computer Vision
  •    Reinforcement Learning
  •    Transfer Learning


Microsoft Cognitive Toolkit (CNTK)

Microsoft Cognitive Toolkit (CNTK)

. Microsoft Cognitive Toolkit (CNTK):– This AI software developed by Microsoft. It supports learning algorithms in areas like image and speech recognition. If you’re looking for specific AI applications or projects built with CNTK, you might be find there names of models or projects that developed by the community or researchers.

Here are the some examples might be given below:-

1. ResNet (Residual Networks):– It is a type of neural network architecture designed to facilitate the training of very deep networks.

2. AlexNet:– It is a convolutional neural network architecture that won the ImageNet Large Scale Visual Recognition Challenge in 2012.

3. LSTM (Long Short-Term Memory):– It is a type of recurrent neural network architecture designed for sequence learning and prediction.

4. ImageNet:– It provides an image database used in large-scale visual recognition challenges, often used to benchmark the performance of deep learning models.

Apache MXNet

Apache MXNet

Apache MXNet:- This AI software supports symbolic and imperative programming.

Here are the some following AI-related projects and tools under the Apache MXNet umbrella:

1. Apache MXNet (incubating):– It gives a core deep learning framework, developed to provide efficient and flexible support for neural networks.

2. GluonCV:– This software provides a deep learning toolkit for computer vision tasks. It provides pre-trained models, data loaders, and utilities to work with computer vision applications.

3. GluonNLP:– This software has a natural language processing toolkit which built on top of Apache MXNet’s Gluon API. It can offers pre-trained models and tools for working with NLP tasks.

4. GluonTS:-This software is a toolkit for time series forecasting using deep learning. It will includes pre-built models and tools for working with time series data.

5. Model Zoo:– This software is a collection of pre-trained deep learning models. It will covers a wide range of tasks, including image classification, object detection, machine translation, and more.

6. MXBoard:– this software is a set of utilities for logging and visualizing training metrics during the training of deep learning models.

7. MXNet Model Server:– this software has a flexible, high-performance serving system for machine learning models designed for production deployment.

Open CV


 OpenCV:– OpenCV (Open Source Computer Vision Library) is a AI library of programming functions mainly aimed at real-time computer vision for making a work easier.

Here are following projects and applications that involve the use of OpenCV in AI contexts are as follows:

1. OpenCV AI Kit (OAK):– This software AI Kit is a set of hardware and software tools developed by OpenCV it is to provide an AI-powered platform for computer vision applications. It can includes a depth-sensing camera and can also be used for various AI tasks.

2. YOLO (You Only Look Once):– This software is a popular object detection algorithm, and OpenCV can be used to implement YOLO for the real-time object detection in images and video streams.

3. Face Recognition with OpenCV:– It provides an often used for face recognition tasks. By combining OpenCV with machine learning models, we can build systems that recognize faces in images or videos.

4. Gesture Recognition:– OpenCV software can be employed for tracking and recognizing gestures. This can be often used in human-computer interaction applications.

5. Image Segmentation:– OpenCV software can also provides tools for image segmentation, and this can be combined with AI techniques for more advanced segmentation tasks.

6. Lane Detection in Autonomous Vehicles:-it is commonly used for detecting lanes in the context of autonomous vehicles. Machine learning models can be integrated to enhance the accuracy of lane detection.

NLTK (Natural Language Toolkit)

NLTK (Natural Language Toolkit)

NLTK (Natural Language Toolkit):-This AI platform is used for building Python programs to work with human language data. It provides easy-to-use interfaces to work with linguistic data.

If you were specifically looking for named entity recognition (NER) tools or models within the Natural Language Toolkit (NLTK), then NLTK itself doesn’t provide pre-trained models for named entity recognition, So, the conclusion is NLTK is a powerful library that can be used in combination with other tools or models for NER.

so here is a basic example of how you might use spaCy for NER:


import spacy

# Load spaCy NER model

nlp = spacy.load(“en_core_web_sm”)

# Sample text

text = “Apple Inc. is planning to open a new store in New York City.”

# Process the text with spaCy

doc = nlp(text)

# Extract named entities

for ent in doc.ents:

    print(f”{ent.text}: {ent.label_}”)


In this example, “en_core_web_sm” is a small English language model provided by spaCy, and it can performs named entity recognition on the given text.

Remember to install spaCy first by running:


pip install spacy


Additionally, if you are interested in using machine learning models for NER, you might to explore other libraries like Stanford NER, Flair etc.



 Spacy:– This AI platform is designed specifically for production use and it is fast and efficient

So, here’s an example of how you can use spaCy for named entity recognition:


import spacy

# Load spaCy NER model

nlp = spacy.load(“en_core_web_sm”)

# Sample text

text = “Apple is planning to open a new store in New York City.”

# Process the text with spaCy

doc = nlp(text)

# Extract named entities

for ent in doc.ents:

    print(f”{ent.text}: {ent.label_}”)


In this example, “en_core_web_sm” is the small English language model for spaCy, and it recognizes entities like “Apple Inc.” as an organization and “New York City” as a location.

If you need a more accurate or comprehensive model, spaCy also provides larger models like “en_core_web_md” (medium) and “en_core_web_lg” (large), which offers more features and better performance, but they require more resources.

Keep it in mind that the field of NLP is dynamic, and the new models may have been introduced since my last update. Always refer to the spaCy documentation for the latest information on available models and features:



Pandas:– It is a powerful AI library for data manipulation and analysis in Python. It is often used in AI projects for handling and preparing data. While it’s not specifically an “AI” library, it is widely used in AI and machine learning projects for handling and preprocessing data.

 a brief example of how Pandas might be used in the context of machine learning:-


import pandas as pd

from sklearn.model_selection import train_test_split

from sklearn.ensemble import RandomForestClassifier

from sklearn.metrics import accuracy_score

# Load a dataset (for example, a CSV file)

df = pd.read_csv(‘your_dataset.csv’)

# Explore the dataset


# Separate features and target variable

X = df.drop(‘target_variable’, axis=1)

y = df[‘target_variable’]

# Split the dataset into training and testing sets

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Initialize a machine learning model (for example, a RandomForestClassifier)

model = RandomForestClassifier()

# Train the model, y_train)

# Make predictions on the test set

predictions = model.predict(X_test)

# Evaluate the model

accuracy = accuracy_score(y_test, predictions)

print(f”Accuracy: {accuracy}”)


In this above coding examples Pandas is used to load and explore a dataset, and scikit-learn is used for machine learning tasks. Mainly Pandas provides functionality to manipulate, clean, and preprocess the data before feeding it into a machine learning model.

So, Remember to install Pandas and scikit-learn if you haven’t already:


pip install pandas scikit-learn


While Pandas itself it’s not an AI tool, it is an essential part of the AI and data science ecosystem for handling and processing data efficiently.

Apache Spark MLlib

Apache Spark MLlib

Apache Spark MLlib:– This AI it is a machine learning library included with Apache Spark, a fast and general-purpose cluster computing system. MLlib provides a set of high-level APIs built on the top of Spark, making it’s easy to use for scalable machine learning tasks. Here are the some common machine learning algorithms and components available in Apache Spark MLlib like:-

1. Linear Regression:– Used to predicting a continuous variable.

2. Logistic Regression:– Used for binary classification problems.

3. Decision Trees:– for both classification and regression tasks.

4. Random Forest:– Ensemble the learning method that combines multiple decision trees.

5. Gradient-Boosted Trees (GBT):– Another ensemble method for classification and regression.

6. Naive Bayes:– A probabilistic classifier based on Bayes’ theorem.

7. Support Vector Machines (SVM):– Used for classification and regression tasks.

8. K-Means:– A clustering algorithm for grouping data points into k clusters.

9. Principal Component Analysis (PCA):– Used for dimensionality reduction.

10. Collaborative Filtering:– Techniques for building recommendation systems.

11. Word2Vec:– An algorithm for learning distributed representations of words.

12. Tf-idf:– Term frequency-inverse document frequency for feature extraction from text data.

13. FPGrowth:– A parallel FP-growth algorithm for mining frequent itemsets.

14. ALS (Alternating Least Squares):– Matrix factorization algorithm used for collaborative filtering.

15. Feature Transformers:– Various transformers for feature engineering, such as Tokenizer, StopWordsRemover, and VectorAssembler.

16. Pipeline:– A tool for building, tuning, and deploying machine learning workflows.

17. Cross-Validation:– Methods for model selection and hyperparameter tuning.

Here’s a very simple example of using Spark MLlib for linear regression are as follows:


from pyspark.sql import SparkSession

from import LinearRegression

from import VectorAssembler

# Create a Spark session

spark = SparkSession.builder.appName(“LinearRegressionExample”).getOrCreate()

# Load data

data =“your_data.csv”, header=True, inferSchema=True)

# Prepare features and target variable

feature_cols = [“feature1”, “feature2”, …]

assembler = VectorAssembler(inputCols=feature_cols, outputCol=”features”)

data = assembler.transform(data)

# Split the data into training and test sets

train_data, test_data = data.randomSplit([0.8, 0.2], seed=123)

# Initialize and train the linear regression model

lr = LinearRegression(featuresCol=”features”, labelCol=”target”)

lr_model =

# Make predictions on the test set

predictions = lr_model.transform(test_data)

# Show the predictions“features”, “target”, “prediction”).show()

“`– This AI platform is used for data analysis, and it includes capabilities for data science, machine learning, and AI. is a company that provides an open-source machine learning platform called H2O, as well as other products and services related to artificial intelligence. some key components and products associated with are as follows:

1. H2O-3:– This is a machine learning platform that includes a wide range of machine learning algorithms for classification, regression, clustering, and more. It is designed to be scalable and it can be used for big data analytics.

2. H2O Driverless AI:– It is an automated machine learning (AutoML) platform that automates the process of building and deploying machine learning models. This platform designed to make it easier for users with varying levels of expertise to create and deploy models.

3. H2O Sparkling Water:– The Integration of H2O with Apache Spark, combining the capabilities of both platforms for the distribution of  machine learning on big data.

4. H2O Wave:– This software is stack for building, deploying, and managing AI apps, including web applications and dashboards.

5. H2O Q:– This product designed for business analysts and data scientists for collaborative and interactive exploration of data, including drag-and-drop functionality.

6. H2O AI Hybrid Cloud:– This cloud-based offering that it provides machine learning capabilities on various cloud platforms.

7. Blue:– An initiative by to accelerate innovation in artificial intelligence and machine learning. is known for its commitment to open-source technologies and making machine learning accessible to a broader audience through user-friendly interfaces and automation.

IBM Watson Studio

IBM Watson Studio

IBM Watson Studio:-This AI software is a cloud-based platform for data science and machine learning. It provides  the tools for data exploration, model development, and deployment.

This software provides a collaborative environment for data scientists, developers, and domain experts to work together on various AI projects. Within IBM Watson Studio, we can use a variety of tools and services for different AI-related tasks. There are some key components and capabilities are as follows:

1. Watson Studio Notebooks:– Jupyter Notebooks integrated into Watson Studio, allowing you to create, edit, and run Python and R notebooks for data exploration, analysis, and model development.

2. Watson Machine Learning:– A service within Watson Studio that enables you to train, deploy, and manage machine learning models. It supports the popular machine learning frameworks like TensorFlow, PyTorch, and scikit-learn.

3. AutoAI:– It is an automated machine learning tool that helps to accelerate the model-building process automatically by selecting algorithms, features, and hyperparameters based on your data.

4. Watson Knowledge Catalog:– A data catalog that helps to discover, curate, and share data assets across your organization. It aids in understanding and preparing data for AI and analytics.

5. Data Refinery:– This tool is used for cleaning, shaping, and preparing data without the need for coding. It provides a visual interface for data wrangling.

6. Modeler Flows:– Drag-and-drop the interface for building and deploying machine learning models. It includes a variety of pre-built nodes for data preparation, modeling, and evaluation.

7. Experiments:– It allows you to create, manage, and compare multiple machine learning experiments to find the best-performing models.

8. Watson OpenScale:– This kid of tool is used for managing and monitoring machine learning models in production. It helps to ensure model fairness, explainability, and transparency.

9. Deep Learning as a Service:– It supports a deep learning frameworks like TensorFlow and PyTorch, allowing you to create and train deep learning models.

10. Watson Studio Desktop:– A desktop client for Watson Studio that allows you to work on projects locally and then sync with the cloud.

Google Cloud AI Platform

Google Cloud AI Platform

Google Cloud AI Platform:– This AI platform is a set of tools for building, training, and deploying machine learning models on Google Cloud.

1. AI Platform Training:– Service for training and tuning machine learning models using distributed training on GCP.

2. AI Platform Prediction:– A service for serving predictions from trained machine learning models.

3. AI Platform Notebooks:– Managed Jupyter Notebooks for data exploration, analysis, and machine learning.

4. AI Platform Pipelines:– This service is for building, deploying, and managing machine learning workflows.

5. AutoML on AI Platform:– AutoML capabilities for building custom machine learning models without extensive knowledge of machine learning.

6. TensorFlow Enterprise on AI Platform:– It is integrated with AI Platform, providing a reliable and scalable TensorFlow experience.

7. Explainable AI (XAI):– This features is used for understanding and interpreting machine learning models.

8. Vertex AI (Unified):– Google Cloud’s AI platform that unifies various AI services, including AI Platform Training and Prediction, AutoML, and more.

9. AI Hub:– This platform is used for discovering, sharing, and deploying AI components and workflows.

10. Deep Learning Containers:– Pre-configured Docker containers with popular deep learning frameworks, compatible with AI Platform.

11. AI Platform Vizier:– It provides a service for hyperparameter tuning and optimization.

12. BigQuery ML:– service that allows the users to create and execute machine learning models directly in BigQuery.

13. Cloud Dataflow:– It is a fully managed service for stream and batch processing, useful for ETL tasks in machine learning pipelines.

14. Cloud Storage:– It is a storage service that is commonly used for storing datasets and model artifacts.

15. Cloud TPU (Tensor Processing Unit):– Specialized hardware accelerators for machine learning workloads.

16. Cloud AutoML:– It provides a pre-trained models and the ability to train custom models with minimal effort.

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About the author

Pooja Rastogi

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