Deep Learning is one of the hottest tech topics at the moment, and could potentially be the answer to hundreds of questions that were previously deemed unsolvable. Deep learning enables a machine to learn just like the way humans do i.e. learn by example.
We have all heard that phrase ‘Data is the new oil’ and if data is oil, deep learning is the refinery that turns crude oil into useful finished products. Deep learning models are trained by using a massive set of labeled data and neural network architectures that contain many layers. If correctly programmed deep learning models can achieve an incredibly high level of accuracy, sometimes exceeding human-level performance.
How Deep learning was developed?
Deep learning is a subset of Machine learning, which is again a subset of Artificial Intelligence, so initially it was thought that machine learning was the answer to out A.I needs, but there are a lot of problems that ML cannot solve and hence we had to find something that would, and this is how Deep learning came into the picture.
Why is Deep learning so useful right now?
Even though deep learning was around since the 1980s, there are two key reasons it has become useful recently:
- Deep learning requires huge amounts of labeled data. For eg., A driverless car would require millions of images and thousands of hours of video to figure where to drive and what and who to avoid.
- Deep learning entails large computing power. High-performance GPUs and Cloud computing have substantially reduced the training time of DL models.
How Deep Learning Works?
Deep learning models use neural network architectures, and that is why deep learning models are referred to as deep neural networks. The term “deep” refers to the many hidden layers in the neural network. Generally, neural networks contain just 2-3 hidden layers, while deep networks can contain more than 100 layers.
Deep learning models are trained using huge sets of categorized data and neural network architectures that acquire features directly from the data without the need for manual feature extraction. A popular type of deep neural networks is the convolutional neural networks (CNN).
Here are the three most common ways people use deep learning to perform object classification are:
- Training from Scratch
- Transfer Learning
- Feature Extraction
So, what is the difference between DL and ML?
Deep Learning is a sub-part of ML, but why do we need deep learning? The reason is ML algorithms can’t process higher dimension or higher number of observations data.
Another problem with ML is that one has to fine-tune the number of parameters. While, in case of deep learning, a neural network can decide on its own about the key features. Essentially, deep learning imitates the way our brain tends to learn from previous experience.
Where can Deep Learning be used?
Here is a short list of tasks that deep learning can perform in real-life situations:
- Identify faces
- Read handwritten digits and texts
- Recognize speech and translate
- Play computer games
- Control self-driving cars
Credit: Google News