What is Machine Learning?

Machine Learning (ML) is a subset of artificial intelligence (AI) that enables computers to learn from data and make decisions without being explicitly programmed. By identifying patterns and correlations in data, ML models can perform tasks such as prediction, classification, and optimization. For instance, Netflix uses machine learning to recommend shows and movies based on a user’s viewing history. ML has revolutionized fields like healthcare, finance, e-commerce, and robotics by automating complex decision-making processes and enabling systems to adapt to new information.

The fundamental idea of machine learning is that machines can improve their performance over time by learning from data. Instead of hardcoding specific rules, ML algorithms create models that adjust themselves to improve accuracy and efficiency through experience.

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What is Deep Learning?

Deep Learning (DL) is a specialized subset of machine learning inspired by the structure and functioning of the human brain. It uses artificial neural networks (ANNs) with multiple layers (hence the term “deep”) to process and analyze large volumes of complex data. Deep learning is particularly effective for tasks such as image and speech recognition, natural language processing, and autonomous driving.

Compared to traditional machine learning, which often relies on manual feature engineering, deep learning models can automatically extract features from raw data. For example, while a machine learning model may require human-defined rules to analyze images, a deep learning model like a convolutional neural network (CNN) identifies edges, textures, and shapes autonomously. This ability to learn hierarchical representations of data makes deep learning uniquely powerful for handling unstructured data such as images, videos, and text.

However, deep learning typically requires more computational resources and large datasets for effective training, which distinguishes it from traditional machine learning models that perform well on smaller datasets.

Deep Learning (DL) is a specialized subset of machine learning inspired by the structure and functioning of the human brain. It uses artificial neural networks (ANNs) with multiple layers (hence the term “deep”) to process and analyze large volumes of complex data. Deep learning is particularly effective for tasks such as image and speech recognition, natural language processing, and autonomous driving.

Deep learning models, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), can automatically extract features from raw data without manual intervention. This ability to learn hierarchical representations of data makes deep learning a powerful tool in domains requiring intricate analysis.

While machine learning models often rely on structured data, deep learning excels with unstructured data, such as images, videos, and text. However, deep learning typically requires more computational resources and large datasets for effective training.

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Types of Machine Learning

Machine learning is broadly classified into three main types, each addressing distinct kinds of problems and datasets. These classifications are significant because they determine how a model learns from data, whether it’s by mapping inputs to known outputs, discovering hidden patterns, or adapting through interaction. By understanding these types, practitioners can select the most appropriate approach for tasks such as predicting outcomes, segmenting data, or automating decision-making.

1. Supervised Learning

• In supervised learning, the model is trained on a labeled dataset, where each input has a corresponding output. The algorithm learns to map inputs to outputs by minimizing the error between predicted and actual outcomes.
• Examples: Linear regression, logistic regression, support vector machines (SVM), and neural networks.
• Applications: Fraud detection, spam filtering, and predicting housing prices.

1. Unsupervised Learning

• Unsupervised learning deals with unlabeled data. The model identifies patterns, structures, or clusters in the data without explicit supervision.
• Examples: K-means clustering, hierarchical clustering, and principal component analysis (PCA).
• Applications: Customer segmentation, anomaly detection, and recommendation systems.

1. Reinforcement Learning

• In reinforcement learning, an agent learns to make decisions by interacting with an environment. The agent receives rewards or penalties based on its actions and optimizes its strategy to maximize cumulative rewards.
• Examples: Q-learning, deep Q-networks (DQN), and policy gradient methods.
• Applications: Game playing (e.g., AlphaGo), robotic control, and autonomous vehicles.

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some examples of new models & innovations in machine learning and deep learning from 2024 and 2025

Deep Learning Innovations:

1. Quantum Convolutional Neural Networks (QCNNs)
   • Combines quantum computing principles with CNNs for efficient high-dimensional data processing.
   • Applications: Quantum data analysis, image recognition.
2. Dissipative Quantum Neural Networks (DQNNs)
   • Uses quantum perceptrons in a dissipative framework for robust quantum environment learning.
   • Applications: Supervised learning with quantum datasets.
3. Diffusion Models
   • Enhanced capabilities: Improved for high-quality image/video generation, text-to-image, and multimodal tasks.
   • Applications: 3D point clouds, creative industries, and reinforcement learning integration.
   • Examples: Stable Diffusion, DALL·E 3.
4. OpenAI’s o1 and o3 Models
   • o1 Model: Focuses on human-like reasoning, improving multi-step inference for tasks like coding and logical problem-solving.
   • o3 Model: Enhances o1’s reasoning capabilities and introduces smaller, application-specific versions.
5. Nvidia’s Cosmos AI Models
   • Designed for generating 3D models and training robots for better interaction with the physical world.
   • Applications: Humanoid robots, industrial automation, and self-driving cars.
6. Generative AI Models
   • Integration with Reinforcement Learning from Human Feedback (RLHF) for better alignment with user intent.
   • Examples: DALL·E 3 with RLHF for more accessible and user-friendly generative tasks.
7. Vision Transformers (ViTs)
   • Advanced transformer-based architectures for image recognition, competing with traditional CNNs.
8. FlashAttention-2 and Sparse Transformers
   • Optimized transformer algorithms improving memory efficiency, speed, and scalability for large-scale models.
9. Liquid Neural Networks
   • Inspired by biological nervous systems, they adapt dynamically over time, offering greater transparency and reduced power consumption.
   • Applications: Fraud detection, self-driving cars, genetic data analysis.

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Machine Learning Innovations:

1. Federated Learning (2025)
   • Enhanced for scalability and privacy in decentralized environments.
   • Applications: Healthcare, finance, and IoT.
2. Explainable AI (XAI)
   • New tools for interpretability in decision-critical applications like healthcare and law.
3. Reinforcement Learning
   • Diffusion World Models: Predict future states in RL environments, reducing long-term prediction errors.
   • Integration with diffusion models for improved sample efficiency and exploration.
4. Amazon’s Nova AI Models
   • Includes models like Nova Micro, Nova Lite, and Nova Pro optimized for speed, cost, and multimodal capabilities.
   • Specialized models: Nova Canvas (image generation) and Nova Reel (video generation).
5. AlphaProof by Google DeepMind
   • Combines AlphaZero and LLMs to solve complex mathematical proofs.
   • Achievements: Solved International Math Olympiad problems in 2024.
6. Nvidia’s DLSS 4 with Multi Frame Generation
   • Uses deep learning to generate multiple frames per traditionally rendered frame, improving gaming and visualization.
7. Autonomous Agents (2025)
   • AI agents that operate independently, using advanced reasoning to handle dynamic environments.
   • Applications: Personal assistants, automated trading, and logistics.

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Machine Learning vs. Deep Learnin:

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Feature	Machine Learning	Deep Learning
Definition	A broad field of AI that includes various algorithms, such as shallow neural networks, decision trees, and SVMs.	A specialized subset of ML focusing on deep neural networks with many layers.
Neural Networks	May use shallow neural networks (1-2 layers) or other algorithms (e.g., decision trees, SVMs).	Employs neural networks with multiple layers (hence “deep”) that process data hierarchically.
Scale and Depth of Networks	Typically limited to shallow or simple networks, or no networks at all.	Uses deep architectures with many layers, enabling the learning of complex patterns and hierarchical representations.
Feature Engineering	Requires manual design and extraction of features (e.g., selecting specific attributes or creating transformations).	Automatically learns and extracts features from raw data during training.
Data Requirement	Works well with small to medium-sized datasets.	Requires large datasets to generalize effectively and avoid overfitting.
Computational Resources	Relatively lower requirements; can often run on standard CPUs.	Requires significant computational power, often leveraging GPUs or TPUs.
Training Time	Generally faster to train.	Can take significantly longer, especially for complex architectures.
Interpretability	Easier to interpret and debug (e.g., decision trees, linear regression).	Often considered a “black box,” but explainable AI (XAI) tools are emerging.
Applications	Commonly used for structured data, such as tabular data in finance, healthcare, and e-commerce.	Excels with unstructured data, such as images, audio, video, and text.
Real-Time Suitability	Suitable for many real-time applications due to lower computational demands.	Challenging for real-time applications without specialized hardware due to high computational requirements.

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Machine learning and deep learning are complementary technologies driving innovation in artificial intelligence. The emergence of new paradigms in 2025 highlights the field’s dynamism and the potential for addressing real-world challenges with increasingly sophisticated tools. in table, Some techniques examples for machine learning are shown.

Table 1. Some techniques examples for machine learning

In another hand, Integrating Machine Learning & Deep learning models into iOS apps is crucial. In the next day article, we will explore frameworks for iOS developers, including the new MLX framework optimized for Apple Silicon, offering superior performance and efficiency. For more details, visit the MLX documentation.

For further information on machine learning frameworks for iOS apps, check out this article.

To see an example of an iOS app with machine learning & deep learning integration, try the our INGOAMPT apps.