Whenever we talk about artificial intelligence, the two terms machine learning and deep learning always become a part of the conversation. Both seem similar to many and are even used interchangeably, but in reality, they are distinct approaches to solving problems with AI. We will differentiate the two based on common parameters and at the end is the example of ChatGPT explaining how this OpenAI tool uses machine learning and deep learning.
But first, get your basics right.
Machine learning is like teaching a computer to recognize patterns or make predictions by feeding it examples of data. It involves algorithms that are designed to analyze and learn from data, and then use that knowledge to make decisions or predictions. Machine learning techniques often rely on feature engineering, which involves manually selecting relevant features or attributes from the data.
It is a subset of machine learning that involves artificial neural networks, the same as neural networks in the human brain. These networks have multiple interconnected nodes called neurons that create a neural network. Algorithms are designed to automatically learn and represent data features by stacking multiple layers of these nodes.
Think of machine learning as using pre-defined rules or formulas to analyze data, while deep learning is like allowing the computer to learn its own rules or formulas from the data. But there differ in many more areas.
Machine learning algorithms typically require less computation and have shorter execution times compared to deep learning algorithms. Machine learning models are relatively simpler and involve fewer layers and parameters, making them faster to train and deploy. On the other hand, deep learning models are more complex, with multiple layers and a large number of parameters, which requires significant computational resources and time for training.
Machine learning algorithms are less dependent on specialized hardware and ML models can often be trained and deployed on standard CPUs, while deep learning models often require specialized hardware such as Graphics Processing Units (GPUs) or Tensor Processing Units (TPUs) to achieve optimal performance due to their computational requirements.
Feature engineering is the process of selecting relevant features or variables from the data to train a machine-learning model. In machine learning, feature engineering plays a crucial role as the performance of the model depends on the quality of the features selected. On the other hand, deep learning algorithms automatically learn relevant features from the raw data without explicit feature engineering. Deep learning models can automatically extract high-level features from the data, which can lead to better performance in certain tasks.
Machine learning typically follows a rule-based or statistical approach where models are trained on labeled data and use predefined rules or statistical techniques to make predictions or decisions. On the other hand, deep learning follows a neural network-based approach where models are trained to learn hierarchical representations of data through multiple layers of interconnected neurons. Deep learning models are capable of learning complex patterns and representations from data, which makes them well-suited for tasks such as image and speech recognition.
Machine learning algorithms provide insights into how the model arrived at a particular decision or prediction, which makes it easier to interpret and explain the model's behavior. Deep learning models, on the other hand, are often referred to as "black boxes" as they learn complex representations that are difficult to interpret, making it challenging to explain the reasoning behind their predictions or decisions.
Both machine learning and deep learning can be applied to various types of data, including structured and unstructured data. However, machine learning models are commonly used for structured data, where the data is organized in a tabular format with predefined features and labels. Deep learning models, on the other hand, are well-suited for unstructured data such as images, speech, and text, where the data does not have a predefined structure and requires the model to learn relevant features from raw data.
Machine learning algorithms are generally less data-dependent compared to deep learning algorithms. Machine learning models can often achieve good performance with relatively small amounts of data, whereas deep learning models typically require large amounts of data for training to learn complex patterns effectively. Deep learning models thrive on big data as they have a higher capacity to learn from vast amounts of data, which can lead to better performance.
Machine learning is suitable for a wide range of applications, including customer segmentation, fraud detection, recommendation systems, and sentiment analysis. It is often used when the problem and data are relatively simple and do not require complex representations. Deep learning, on the other hand, is well-suited for tasks that require complex and hierarchical representations from unstructured data, such as image recognition, speech recognition, natural language processing, and autonomous driving. Deep learning excels in tasks where traditional machine learning approaches may not be as effective due to the complexity and richness of the data.
So, which is better - deep learning or machine learning?
The answer depends on the specific problem you are trying to solve and the nature of your data. Machine learning is generally preferred when the problem is relatively simple, data is limited, and interpretability is important. Machine learning models are often easier to understand and explain, making them suitable for applications where transparency and interpretability are critical, such as in legal, healthcare, or finance domains.
Deep learning is best to be utilized in areas dealing with complex data and tasks such as computer vision, audio processing, speech recognition, etc. These areas require high-level representations thus deep learning is best suited for them.
However, the two give brilliant results when working together. A practical example of it is ChatGpt. Let's take a closer look at how ChatGPT, the language model utilizes both machine learning (ML) and deep learning (DL) techniques in its operation.
ChatGPT uses machine learning algorithms to generate responses based on patterns learned from vast amounts of text data. During its training phase, ML algorithms are used to process and analyze large datasets containing text from various sources, such as books, articles, websites, and social media. The ML algorithms learn from this data and extract patterns, such as word frequencies, sentence structures, and contextual relationships between words, to understand language patterns and correlations.
During inference, when you interact with ChatGPT by providing input prompts, the ML algorithms analyze your prompt and generate responses based on the patterns learned from the training data. The ML algorithms consider various factors, such as the likelihood of certain words or phrases occurring based on their frequency in the training data, to generate a relevant and coherent response.
In addition to machine learning, ChatGPT also employs deep learning techniques, specifically a type of DL model called a transformer neural network. Transformers are a class of DL models that are designed to process sequential data, such as text, and capture long-range dependencies between words. Transformers use self-attention mechanisms to weigh the importance of different words in a sentence based on their context, enabling them to understand the meaning and context of words about each other.
The deep learning components of ChatGPT, particularly the transformer neural network, allow it to understand complex language patterns, generate contextually relevant responses, and adapt to different writing styles and prompts. The self-attention mechanism of the transformer model enables ChatGPT to capture long-range dependencies and consider the context of the entire input prompt, allowing for more coherent and contextually relevant responses.
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