Taha Douaji, Machine Learning Engineer
7 mins
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August 12, 2024
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Source: freepik.com
AI solutions are transforming modern businesses by enhancing personalisation and decision-making. Powering this change are advanced machine learning and deep learning algorithms. They analyse various datasets and identify patterns otherwise impossible for humans to comprehend.
These technologies enable companies to automate complex tasks and improve customer experiences. For example, machine learning algorithms drive personalised recommendations on streaming and e-commerce platforms. On the other hand, deep learning powers natural language processing (NLP) in voice assistants like Siri and Alexa. By leveraging ML and DL, businesses can uncover insights from data, predict trends, and make informed decisions.
Despite their overlapping roles, AI, ML, and DL are distinct. This blog aims to demystify these terms, explaining their differences and unique contributions to business applications.
Here's what you will learn:
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AI is a broad field focused on creating systems that mimic human intelligence to perform specific tasks. These include reasoning, problem-solving, perception, and understanding human language.
It encompasses wide-ranging techniques and methodologies that range from simple rule-based systems to advanced algorithms that can process large datasets.
Machine Learning (ML) and Deep Learning (DL) are subsets of AI that can process large and complex datasets.
Machine Learning: It is a method of data analysis that automates the building of analytical models. It involves algorithms that learn from and make predictions or decisions based on data. ML is commonly used for recommendation systems, fraud detection, and predictive analytics.
Deep Learning: It involves neural networks with many layers (hence "deep") that can automatically learn representations from data. Deep learning models excel in handling large, complex datasets. They are used in applications like image and speech recognition, natural language processing, and autonomous vehicles. DL models are powerful due to their ability to learn directly from raw data without manual feature extraction, unlike traditional machine learning.
Machine learning is a subset of artificial intelligence that focuses on developing algorithms that enable AI systems to learn from data. ML helps these systems mimic human intelligence to make critical decisions. Its fundamental concept involves using datasets to train models and help them identify patterns and make predictions.
A training method based on supervision, it used labelled datasets to train ML models. In the labelled datasets, the inputs are already mapped to the outputs. As a result, the model learns to map inputs to outputs. These models are used for classification (e.g., spam detection and email filtering) and regression (e.g., predicting house prices, market trends, and weather forecasts).
In this method, the model is trained on unlabelled data. It learns to find patterns and relationships within the dataset. The primary goal of unsupervised learning is to group or categorise the unsorted dataset according to similarities, patterns, and differences. The ML model learns to find hidden patterns from the input dataset. Examples include recommendation systems, anomaly detection, and data extraction.
A method that teaches ML models based on a feedback process. There are no labelled datasets in this method and the model learns through trial and error, much like humans. The goal of this process is to help the model earn cumulative rewards. It gets rewarded for each correct action and gets penalised for incorrect action. Examples of its usage include game theory, autonomous vehicles, robotics, etc.
Deep learning is a subset of machine learning that uses multilayered neural networks to model complex patterns in large datasets. Unlike traditional machine learning, deep learning can automatically learn features from raw data. It is a powerful tool for tasks like image and speech recognition.
At the core of deep learning are neural networks. They are computational models inspired by the human brain's structure. A neural network consists of interconnected nodes (neurons) organised into layers: input, hidden, and output layers.
While supervised learning models require structured, labelled input data for accurate outputs, deep learning models are trained via unsupervised learning.
Some examples of deep learning models:
Convolutional neural networks or CNNs are used in image recognition and processing. They are designed to identify objects even in obscured or distorted images. CNNs use convolutional layers to scan through images, capturing patterns and details like edges, textures, and shapes.
Recurrent neural networks or RNNs are used for sequential data processing, time-series prediction, and natural language processing. They are good at recognising data's sequential characteristics and using patterns to predict the next likely scenario.
These models are usually used for NLP tasks, such as translation, text summarisation, and question answering. They use a self-attention mechanism to weigh the importance of different words in a sentence. This allows them to capture context and relationships more effectively than traditional RNNs.
From a business perspective, choosing between traditional machine learning and deep learning depends on the type of data they need to process.
Consider traditional machine learning if you have limited data. Traditional ML algorithms can deliver reliable results with smaller datasets, making them more cost-effective and faster to deploy. For instance, a retail company that wants to predict customer churn can train an ML model on small and structured datasets like purchase history, customer service interactions, and demographic information.
ML models are suitable for quick implementation of a solution. They require shorter training periods as compared to deep learning models. This makes them quicker to develop, train, and deploy, thus allowing you to address business challenges or capture opportunities.
Traditional machine learning is ideal if you you want to minimize infrastructure costs. These ML algorithms can often be executed with standard CPUs and require less computational power as compared to deep learning models.
Traditional ML models are ideal when a business needs clear and interpretable insights. Techniques like decision trees or logistic regression provide understandable outputs, critical in sectors like finance or healthcare, where transparency and explainability are crucial.
Traditional ML models help with well-defined, straightforward problems, such as predictive maintenance, customer segmentation, or basic classification tasks. ML is effective for problems where the relationships between inputs and outputs are relatively simple.
Deep learning algorithms thrive on abundant data. Unlike machine learning algorithms, they are suitable for businesses that need to analyse large and complex data sets. DL models have the ability to discover intricate patterns, makes them ideal for complex problems where the relationship between inputs and output is not straightforward. For instance, identifying fraudulent patterns in financial transactions.
Adanvaced capabilities like image recognition, natural language processing, or autonomous systems demand deep learning models. DL's ability to model highly complex functions makes it the right choice for sophisticated AI applications. Leveraging deep learning capabilities also help businesses to unlock new opportunities in innovation.
Deep learning models can support businesses that aim to automate tasks that are traditionally human-driven and require deep analysis. Examples include medical image diagnosis, sentiment analysis, or high-level decision-making. DL can handle the complexity and variability in these tasks far more effectively than traditional ML.
Deep learning is the answer if your business is prepared to make a long-term investment in AI. While DL requires more time, data, and computational resources, the potential for automation, innovation, and scaling can offer significant returns over time.
Use Case: A retail company wants to segment customers to personalise marketing campaigns based on their behaviour
Choice: Machine Learning
How: The company uses machine learning algorithms like k-means clustering to analyse customer data and group similar customers together. This allows for targeted marketing strategies, increasing customer engagement and conversion rates.
Use Case: Developing self-driving cars
Choice: Deep learning
How: CNNs in deep learning help self-driving cars process vast amounts of data from cameras, LIDAR, radar, and other sensors. These models enable the vehicle to recognise objects and understand their surroundings. These inputs help with braking, accelerating, or turning.
Use Case: Personalised treatments
Choice: Machine learning
How: Machine learning algorithms help analyse historical medical records and outcomes to develop personalised treatment plans. This leads to data-driven recommendations for personalised medication or treatment.
Use case: Intelligent document processing
Choice: Deep learning
How: Deep learning models can automate the extraction and analysis of information from various financial documents. These include invoices, contracts, and bank statements. By applying techniques like optical character recognition (OCR) and natural language processing (NLP), they can accurately extract relevant data from complex documents.
For instance, our document processing platform, Pedant processes invoices, cheques, and delivery notes. It is based on a hybrid model trained on varied data types.
Knowing the distinctions and overlaps between ML and DL helps adopt the right approach to specific problems. Both ML and DL are powerful, but their applications vary based on the nature and complexity of data. Machine learning excels in scenarios where data is limited while DL is designed for complex problems with vast large datasets.
Understanding these distinctions helps businesses make informed decisions. Whether leveraging ML for quick, data-driven decisions or deploying DL for advanced applications, the right choice can significantly impact operational efficiency, customer experiences, and overall business success.
