ML 1.7 Deep Learning - The Heart of Machine Learning !

Deep learning is a bit like a puzzle for many people. Some wonder if it was its type of machine learning, while others think it is just a clever trick. To make sense of it all, let's embark on a journey to see how deep learning fits into the big picture of machine learning.

The confusion between the terms "Deep Learning," "Machine Learning," and "Artificial Intelligence" often arises due to the evolving and overlapping nature of these fields, as well as the way they are presented in popular media and discussions.

People often use these terms interchangeably, but they have distinct meanings. Deep learning is a subset of machine learning, and machine learning is subset of Artificial Intelligence.

• Artificial Intelligence (AI): AI is the broadest field encompassing the development of computer systems that can perform tasks that typically require human intelligence, such as problem-solving, understanding natural language, recognizing patterns, and making decisions.
• Machine Learning (ML): Machine learning is a subset of AI. It focuses on the development of algorithms and statistical models that allow computer systems to improve their performance on a specific task through experience, often with the use of data.
• Deep Learning (DL): Deep learning is a subset of machine learning. It specifically revolves around artificial neural networks, which are inspired by the structure and function of the human brain.

Deep learning was like a superstar within machine learning, known for using special computer brains with many layers. These brains were called "deep neural networks," and they were the secret sauce behind deep learning's magic.

Deep learning is both a type of machine learning and a set of techniques used to automatically learn data representations through these deep neural networks. It can be applied to various learning pattern/models, including supervised, unsupervised, and reinforcement learning.

• In Supervised Deep Learning, deep neural networks are used for tasks where labeled data is available, and the primary goal is to make predictions or classifications.
• In Unsupervised Deep Learning, deep neural networks are applied to tasks that involve discovering patterns or representations in unlabeled data.
• In Reinforcement Learning, deep reinforcement learning (DRL) utilizes deep neural networks to approximate value functions or policy functions for sequential decision-making.?

In simple words,

• Supervised Learning: The teacher that helps computers learn with labeled examples.
• Unsupervised Learning: The explorer that uncovers hidden patterns in data.
• Reinforcement Learning: The decision-maker that learns from rewards and punishments.

All of them teamed up with deep learning to solve real-world challenges, like recognizing objects, making sense of the environment, and driving safely.

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In the end, we realized that deep learning was not just a type of machine learning or a fancy trick. It was a versatile technique that could be used in different ways to teach computers about the world. It was like the key that unlocked the potential of artificial intelligence.

So, as our journey comes to a close, remember that deep learning is the bridge that connects machines to the world, making them learn, adapt, and perform tasks with human-like skills. And in the vast world of machine learning, it's the ally that helps us solve puzzles and uncover the mysteries of technology.

In summary, supervised, unsupervised, and reinforcement learning are learning pattern/models that can benefit from deep learning techniques when dealing with complex data. In essence, deep learning is neither an isolated type of machine learning nor a singular approach; it is a versatile technique that can be applied across various machine learning paradigms. It thrives in supervised settings by automating feature extraction, complements unsupervised learning by uncovering very complicated data representations, and empowers reinforcement learning for making complex decisions.

One of the top-notch free courses on deep learning can be found at MIT 6.S191: Introduction to Deep Learning