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What is deep learning?
Deep learning has been an incredibly impactful subset of machine learning. One thing that sets deep learning apart from other methods is its ability to handle complex, unstructured data such as images, audio, and natural language. Machine learning engineers who specialize in deep learning are constantly training models and refining techniques to improve accuracy and performance, making it a rapidly evolving field. Ultimately, the goal of deep learning is to create models that can learn and adapt to new data, much like humans do.
The idea behind deep learning is to create algorithms that process data in the same way humans do. The more data they process, the smarter they become and the better they are at making decisions without being told what to do.
In recent years, deep learning has made significant progress in many areas such as image recognition, speech recognition, natural language processing and translation.
The history of deep neural networks
Deep Learning contains many different methods, such as: Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Deep Belief Networks (DBNs). However, its humble beginnings lie in the simple model of a neuron by McCulloch and Pitts, first devised in in 1943, and then the simple one-layer perceptron.
It’s only until recently (that is in the last 10-15 years) that advances in computational power, and the data deluge allowed the creation and training of more complicated neural networks.
This has led us to the current era, where deep neural networks are used for all sorts of applications. For example, virtual assistants like Siri are based upon deep learning, in order to understand commands. It’s also very likely that in the near future autonomous vehicles will be purely deep learning driven.
That’s why I thought it would be a good idea to dedicate a whole episode to that. I hope you enjoy it! You can find the episode on anchor.fm and youtube below.