In today’s tech world, we often hear buzzwords like Deep Learning, Machine Learning, and Artificial Intelligence (AI). But what exactly do they mean, and where should we focus? It’s a big question.
To understand, let’s start with the basics: definitions, approaches, data needs, computing power, and real-world uses of Deep Learning and Machine Learning. While they’re both part of AI, they have different methods and goals.
In my last post, I mentioned my upcoming exploration of these topics, aiming to clarify the differences between Deep Learning and Machine Learning as I transition from a Global MBA background. Join me as we simplify these complex concepts together.
First, let’s start with their basic definitions:
Machine Learning :
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- Machine learning is a subset of AI that focuses on algorithms and statistical models that enable computers to learn and improve on a specific task without being explicitly programmed.
- It encompasses a variety of techniques such as supervised learning, unsupervised learning, reinforcement learning, and more.
Deep Learning :
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- Deep learning is a specific subset of machine learning that utilizes artificial neural networks with multiple layers (hence we call it deep) to learn from large amounts of data.
- Deep learning algorithms attempt to mimic the workings of the human brain’s neural networks, enabling computers to identify patterns and make decisions with minimal human intervention.
The Takeaway from this definition is that both Machine learning and Deep learning are related. Deep learning is a subset of machine learning.
Let’s look at the approach they follow.
Machine Learning:
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- In Machine learning, feature extraction and engineering are typically performed manually by human experts. Experts select and craft features that they believe are relevant and informative for the task at hand. These features are then used as input to machine learning algorithms.
- The algorithm learns to make predictions or decisions based on these engineered features, which are often derived from knowledge and expertise.
Deep Learning:
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- Deep learning algorithms automatically learn hierarchical representations of data through the layers of neural networks. Instead of relying on manually engineered features, deep learning models directly process raw data inputs, such as images, text, or audio.
- Each layer in the neural network learns increasingly abstract features from the raw data. This automated feature extraction process requires less manual intervention in feature engineering, as the system can learn to extract relevant features directly from the data itself.
The Takeaway from both these approaches is that machine learning relies on manual feature engineering by experts, and deep learning automates this process by learning hierarchical representations from the data. This clearly shows that automation can lead to more efficient and effective models, especially for tasks involving large, complex datasets.
Now let’s take a look at Data requirements,
Machine Learning :
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- Machine learning algorithms often require curated datasets with well-defined features. The quality of features greatly influences the performance of the model.
- Data preprocessing and feature engineering play a crucial role in ML pipelines to ensure that the input data is suitable for the chosen algorithm.
Deep Learning:
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- Deep learning models thrive on large volumes of raw data. They can automatically learn complex features directly from the raw data, reducing the need for extensive feature engineering.
- Data learning algorithms benefit from massive datasets, as they require substantial amounts of data to efficiently train the parameters of deep neural networks.
Now let’s take a look at the Computational requirements
Machine Learning :
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- Traditional Machine learning algorithms usually require less computational power compared to deep learning models. They can often run efficiently on standard hardware configurations.
- Training Machine learning models typically involves optimizing parameters through techniques like gradient descent or evolutionary algorithms.
Deep Learning:
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- Deep learning models are computationally intensive, especially during training. Training deep neural networks often requires special hardware like GPUs or TPUs to accelerate computations.
- Deep learning models often involve millions or even billions of parameters, and training them may take significant time and computational resources.
Finally, let’s check on the applications
Machine Learning :
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- Machine learning techniques are widely used in various domains, including finance, healthcare, marketing, and recommendation systems.
- Applications include credit scoring, fraud detection, customer segmentation, and personalized recommendations.
Deep Learning:
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- Deep learning has revolutionized fields like computer vision, natural language processing, and speech recognition.
- Applications include image classification, object detection, machine translation, sentiment analysis, and virtual assistants.
In summary, both machine learning and deep learning are subfields of artificial intelligence, they differ in their approaches, data requirements, computational requirements, and applications.
Machine learning relies on manually engineered features and is suitable for tasks with structured data and well defined features. Deep learning, on the other hand, automates feature extraction and is highly effective for tasks involving unstructured data, such as images, text, and audio. Depending on the problem domain and available resources, practitioners can choose the most appropriate to build intelligent systems.