The coolest branch of machine learning is probably deep learning (Deeplearning) and reinforcement learning (Reinforcement learning).
Deep Learning
Deep learning is an algorithm that models the implicit distribution of data in machine learning through multi-layer representations. In other words, deep learning algorithms automatically extract low-level or high-level features required for classification. Therefore, deep learning can better represent the characteristics of data. Due to the model’s hierarchy and numerous parameters, its capacity is sufficient, allowing deep learning models to represent large-scale data. This is particularly effective for challenging problems such as images and speech, where features are not obvious, as deep learning can achieve better results with large-scale training data. Moreover, since deep learning combines features and classifiers into a single framework, learning features from data reduces the enormous workload of manual feature extraction. Thus, not only can the performance be improved, but it is also very convenient to apply. As a result, deep learning has made significant advancements in image recognition and speech recognition.
Reinforcement Learning
Reinforcement learning is essentially a process of continuous decision-making, characterized by the absence of labeled data, providing only a reward function. This reward function determines the outcome of the current state (e.g., “good” or “bad”). Mathematically, it is still a Markov decision process. The ultimate goal of reinforcement learning is to optimize the expected overall reward function in the decision-making process.
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Applications
Using a neural network that resembles the web-like structure of the human brain, deep learning can solve many practical problems. For example, Google’s image search, Facebook’s face recognition, Skype’s real-time translation, and Twitter’s detection of explicit language. Reinforcement learning takes deep learning a step further; once you establish a deep learning network for playing games, you can use reinforcement learning to let it compete against itself and evolve.