Deep learning has become a buzzword in the IT segment for backing numerous innovations. The advanced analytics software suite Statistica quotes that the market for Deep Learning applications is touted to reach a whopping $80 million, and that’s just in the United States!
What I admire about it are its applications which can be extended to different spheres such as sales, operations, marketing, customer services, classification of images, sound recognition, and a lot more. Are you intrigued yet? If yes, read on to discover some fascinating deep learning applications that will leave you baffled for sure.
Deep learning projects can prove to be a boon to our society. This is true in the case of a research study done by Harvard scientists. They employed Deep Learning to teach a computer to perform viscoelastic computations, which are used in predicting earthquakes. Before the use of Machine Learning and Deep Learning, these computations proved to very intensive. However, Deep learning applications have improved the calculation time by 50,000 percent! Timing is extremely crucial for making earthquake calculations and, eventually when it comes to saving lives.
Although automatic machine translation has been in use for a long time; it is only after Deep Learning applications offer top results in two segments: Automatic translation of Images and Automatic translation of texts. Automatic Machine Translation involves the automatic translation of a sentence, phrase, or words into another language. Using Deep Learning techniques, text translation can be done without any need for pre-processing of any sequence. A pretty famous example of this is Google Translate, which uses Deep Learning to provide accurate translations.
This allows the algorithm to understand the dependency between the words and their mapping to a different language. This is done with the aid of stacked networks of recurrent neural networks. The identification of images in these systems is made using the convolutional neural network. Upon identification, these can be converted into text, translated, and recreated with the text that has been translated. This process is referred to as instant visual translation.
Image colorization is a problem that is associated with adding color to images that are black and white. Earlier, this task was done manually by human effort, as it was pretty complicated to accomplish with machines. However, Deep Learning has resulted in the possibility of coloring the images with the use of devices. This cannot be described as anything less than an impressive feat. The capability of high quality and large convolutional neural networks are employed during this process. These neural networks are trained from ImageNet.
Deep Learning machines are pretty much advanced. These cannot just speak but can also comprehend what you are speaking. The LipNet system is a classic example of this system developed by scientists at Oxford University utilizing neural networks. LipNet has become the first system in the world which can recognize not just words, but as well as lip-speech and whole sentences. A video sequence is processed by the system dividing it into layers or fragments. I must say the results obtained were nothing less than impressive. This technology provides qualitative impetus for developing medical technologies. Check out this awesome video with LipNet in action predicting the outcomes accurately.
Many companies are working on self-driving cars, from Tech companies to Car manufacturers. Companies trying to build these kinds of driver-assistance services need to make the computer understand how to take over critical parts of driving with the aid of digital sensor systems. For this to happen, companies need to train algorithms with large chunks of data.
This is quite an interesting task. During this process, a large chunk of text is learned, and the new text is generated character-by-character or word-by-word. The model developed can learn how to punctuate, spell, and even capture the style of the text in the chunk of data. Large recurrent neural networks are utilized for establishing the relationship between items in the sequence of input strings for text generation. Great success has been achieved with the LSTM recurrent neural networks, which rely on a character-based model. You can read this blog post by Andrej Karpathy on recurrent neural networks and the success he had in generating automatic text.
There are numerous Deep Learning projects which are trying to tap the benefits of the Deep Learning process. Artificial Intelligence is reshaping the field of medicine and life sciences. Innovations in the area can help with population health management and precision medicine. Decision support tools, quantitative imaging, computer-aided diagnosis and computer-aided the detection will play a massive role in the years to come.
Voice search is one of the most popular applications of Deep Learning. Tech giants have made significant investments in this field. You can find nearly every single smartphone today equipped with voice-activated assistants. Apple’s Siri has been on the market since 2011. Google Assistant is available on Android devices. The latest addition is the intelligence assistance offered by Microsoft Cortana.
In this Deep Learning project, the system needs to synthesize sounds for matching a silent video. This system can then be trained to utilize thousands of examples of videos with sounds of a drumstick, striking different surfaces for creating sounds. This model associates the video frame with a database of sounds that have been pre-recorded. This project uses both Long short-term memory [LSTM] recurrent networks and Convolutional neural networks.
With this application, handwriting examples or samples are fed into a machine for generating new handwriting for a given phrase or word. The handwriting is provided as a sequence of coordinates used by the pen when the samples are created. The relationship between the letters and the pen movement can be learned to generate automatic patterns. You can try out this fun Interactive Handwriting application where any sentence you type can be developed into a random handwriting style.
The automatic the captioning project involves generating a caption from an image by describing the contents of the picture. Deep learning applications were explored mainly during the year 2014 for the impressive results that they could offer. The algorithms could leverage top models for detecting and classifying objects in the photographs. Upon detection of objects in pictures, the labels need to be generated. Once this is done, the tags need to be transformed into a coherent description. These systems involve the use of large convolutional neural networks for detecting objects in photographs. These are then converted into labels using LSTM.
This is another key area which has been transformed by Deep Learning projects. Both advertisers and publishers have applied deep learning to increase the relevancy of their ads. This, in turn, boosts the return on investments of the campaigns being launched for advertising. Deep learning permits publishers and ad networks to leverage the content for creating targeted display advertising.
Spotting invasive brain cancer cells during the surgery is difficult as per observations by a team of French researchers. This may be due to the effects of lighting in the rooms where surgery is taking place. The team of scientists figured out that Raman spectroscopy, in conjunction with neural networks, can help in easier detection of cancer cells. This project helps to match advanced image recognition process as well as classification of screening apparatus and different kinds of cancer.
Phenomenal success has been noted in the futures markets since its inception over the past decade. This success has been attributed to the leveraging the futures for the market participants. This Deep Learning study involves analyzing trading strategy, which offers benefits using the Capital Asset Pricing Model. This study consists of the application of trading rules which have been developed using spot market prices. For the analysis, historical data of the daily cost of twenty stocks are employed from ten markets.
Deep Learning Projects have found massive applications in numerous sectors. These are evident from the deep learning applications, as mentioned above. Deep learning projects can simplify a lot of tasks that cannot be done manually. With a little data training and appropriate strategy, the applications have the potential to offer revolutionary benefits.
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