A brief introduction of DL and why it is important
Deep learning (also known as deep structured learning or hierarchical learning) is part of a broader family of machine learning methods based on learning data representations, as opposed to task-specific algorithms. Learning can be supervised, semi-supervised or unsupervised.
Deep learning architectures such as deep neural networks, deep belief networks and recurrent neural networks have been applied to fields including computer vision, speech recognition, natural language processing, audio recognition, social network filtering, machine translation, bioinformatics, drug design, medical image analysis, material inspection and board game programs, where they have produced results comparable to and in some cases superior to human experts.
Companies building these types of driver-assistance services, as well as full-blown self-driving cars like Google’s, need to teach a computer how to take over key parts (or all) of driving using digital sensor systems instead of a human’s senses. To do that companies generally start out by training algorithms using a large amount of data.
You can think of it how a child learns through constant experiences and replication. These new services could provide unexpected business models for companies.
Breast or Skin-Cancer diagnostics? Mobile and Monitoring Apps? or prediction and personalised medicine on the basis of Biobank-data? AI is completely reshaping life sciences, medicine, and healthcare as an industry. Innovations in AI are advancing the future of precision medicine and population health management in unbelievable ways. Computer-aided detection, quantitative imaging, decision support tools and computer-aided diagnosis will play a big role in years to come.
One of the most popular usage areas of deep learning is voice search & voice-activated intelligent assistants. With the big tech giants have already made significant investments in this area, voice-activated assistants can be found on nearly every smartphone. Apple’s Siri is on the market since October 2011. Google Now, the voice-activated assistant for Android, was launched less than a year after Siri. The newest of the voice-activated intelligent assistants is Microsoft Cortana.
In this task, the system must synthesize sounds to match a silent video. The system is trained using 1000 examples of video with sound of a drumstick striking different surfaces and creating different sounds. A deep learning model associates the video frames with a database of pre-rerecorded sounds in order to select a sound to play that best matches what is happening in the scene.
The system was then evaluated using a turing-test like a setup where humans had to determine which video had the real or the fake (synthesized) sounds.
This uses application of both convolutional neural networks and Long short-term memory (LSTM) recurrent neural networks (RNN).