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4 cited papers · March 29, 2026 · Powered by Researchly AI
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TL;DR
Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) form a powerful technology stack that is transforming industries worldwide. describe…
Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) form a powerful technology stack that is transforming industries worldwide.12Deshpande & Sharma (2025)1describe AI as a transformative force, with ML and its subset DL at its core, covering fundamental concepts from supervised learning to complex deep neural networks.22
Diagram
ML and DL are core technologies driving significant innovation in healthcare, finance, agriculture, manufacturing, and transportation.
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ADVANCING ARTIFICIAL INTELLIGENCE: AN IN-DEPTH LOOK AT MACHINE LEARNING AND DEEP LEARNING ARCHITECTURES, METHODOLOGIES, APPLICATIONS, AND FUTURE TRENDSDr. Tanay Deshpande, Dr. Kavita Sharma2025International Journal of Intelligent Data and Machine Learning
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Machine Learning and Deep Learning Paradigms: From Techniques to Practical Applications and Research FrontiersKamran Razzaq, M. Shah2025Comput.
View - Machine Learning (ML) — A field that teaches computers to solve problems by analyzing data and creating analytic models automatically, encompassing supervised, unsupervised, semi-supervised, and reinforcement learning techniques.
- Deep Learning (DL) — A sophisticated subfield of ML that utilizes neural networks to simulate AI, often surpassing traditional ML models especially in image classification and complex cognitive tasks.
- Convolutional Neural Network (CNN) — A key deep learning architecture widely used in computer vision tasks such as object detection, face recognition, and image classification. Voulodimos et al. (2018)
- Recurrent Neural Network (RNN) / LSTM — Neural network architectures designed for sequential data, including Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU), applied in NLP and speech recognition. Alom et al. (2019)
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Exploring AI Techniques for Yoga Image Classification: A Focus on Machine Learning and Deep LearningAnjali Duggal, Satish Kumar et al.20252025 International Conference on Intelligent Systems and Computational Networks (ICISCN)
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Machine Learning and Deep Learning Paradigms: From Techniques to Practical Applications and Research FrontiersKamran Razzaq, M. Shah2025Comput.
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ADVANCING ARTIFICIAL INTELLIGENCE: AN IN-DEPTH LOOK AT MACHINE LEARNING AND DEEP LEARNING ARCHITECTURES, METHODOLOGIES, APPLICATIONS, AND FUTURE TRENDSDr. Tanay Deshpande, Dr. Kavita Sharma2025International Journal of Intelligent Data and Machine Learning
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Diagram
AI └── Machine Learning (ML) ├── Supervised Learning ├── Unsupervised Learning ├── Semi-Supervised Learning └── Reinforcement Learning └── Deep Learning (DL) ├── Deep Neural Networks (DNN) ├── Convolutional Neural Networks (CNN) │ └── Applications: Image Classification, Object Detection ├── Recurrent Neural Networks (RNN / LSTM / GRU) │ └── Applications: NLP, Speech Recognition ├── Autoencoders (AE) ├── Deep Belief Networks (DBN) └── Generative Adversarial Networks (GAN)
Table
| Feature | Traditional ML | Deep Learning (DL) |
|---|---|---|
| Data Handling | Works well with structured/small data | Excels with large, complex datasets |
| Feature Engineering | Manual feature extraction required | Learns features automatically |
| Performance | Good baseline results | Matches or beats human performance on complex tasks |
| Key Algorithms | SVM, Decision Trees, Regression | CNN, RNN, LSTM, GAN |
Experimental results show state-of-the-art performance using deep learning compared to traditional ML in image processing, computer vision, speech recognition, machine translation, medical imaging, medical information processing, robotics and control, bioinformatics, natural language processing, and cybersecurity.1
Alom et al. (2019)
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Review of deep learning: concepts, CNN architectures, challenges, applications, future directionsLaith Alzubaidi, Jinglan Zhang et al.2021Journal Of Big Data
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- Emerging challenges include ethical considerations, the need for Explainable AI (XAI), and the complexity of deploying ML/DL solutions from data acquisition to production.
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ADVANCING ARTIFICIAL INTELLIGENCE: AN IN-DEPTH LOOK AT MACHINE LEARNING AND DEEP LEARNING ARCHITECTURES, METHODOLOGIES, APPLICATIONS, AND FUTURE TRENDSDr. Tanay Deshpande, Dr. Kavita Sharma2025International Journal of Intelligent Data and Machine Learning
View - ML encompasses supervised, unsupervised, semi-supervised, and reinforcement learning as its core paradigms.
- Deep Learning is a subfield of ML that uses neural networks and consistently outperforms traditional ML on complex tasks.
- CNNs are the dominant architecture for computer vision tasks such as object detection and face recognition.
- RNNs, LSTMs, and GRUs are the go-to architectures for sequential and language-based tasks.
- Future directions in AIML include hybrid models, generative AI, federated learning, and quantum machine learning.
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Machine Learning and Deep Learning Paradigms: From Techniques to Practical Applications and Research FrontiersKamran Razzaq, M. Shah2025Comput.
View 2
ADVANCING ARTIFICIAL INTELLIGENCE: AN IN-DEPTH LOOK AT MACHINE LEARNING AND DEEP LEARNING ARCHITECTURES, METHODOLOGIES, APPLICATIONS, AND FUTURE TRENDSDr. Tanay Deshpande, Dr. Kavita Sharma2025International Journal of Intelligent Data and Machine Learning
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Exploring AI Techniques for Yoga Image Classification: A Focus on Machine Learning and Deep LearningAnjali Duggal, Satish Kumar et al.20252025 International Conference on Intelligent Systems and Computational Networks (ICISCN)
View 4
Review of deep learning: concepts, CNN architectures, challenges, applications, future directionsLaith Alzubaidi, Jinglan Zhang et al.2021Journal Of Big Data
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- "Best Python libraries for machine learning beginners — scikit-learn vs TensorFlow vs PyTorch"
- "Convolutional Neural Networks for image classification — step-by-step tutorial"
- "Supervised vs unsupervised vs reinforcement learning — differences and use cases"
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