Deep Learning is a subdivision of machine learning, under the category of artificial intelligence. It’s based on a fixed set of algorithms that strives to model advanced level abstractions in data. In a simple model, you would be having two sets of neurons, where if the input layer receives any input, it transmits a revamped version of input to the next layer. However, in a Deep network, there exists a web of many layers between input and output, compelling the algorithm to rely on multiple processing layers, made of numerous layers and non-linear transformations.
No wonder, Deep Learning has triggered a revolution in the machine learning realm. Interesting works are being carried on in this field. Innovative technology is modifying speech recognition, object detection, visual object recognition and other sectors, like genomics and drug discovery. And, yes, we are excited about all the new good things that’s happening around!!
For more detailed analysis, scroll below:
About Deep Learning Architecture
- Generative deep architectures are created to characterize high-order correlation attributes of visible data for all sorts of pattern analysis as well as synthetic purposes.
- Discriminative deep architectures are specialized in offering discriminative power for pattern classification, mostly by showcasing posterior distribution of classes subject to visible data.
- Hybrid deep architectures are designed for discrimination but are aided with results of generative architectures through better optimization as well as regularization.
A Few Applications of Deep Learning
Colorization of BW Images
Deep learning has the ability to recreate an image with the addition of color. The cutting edge technology uses the objects and the entire context within a picture for coloring the whole image, quite similar to a human approach. For this, extensive supervised layers and convulational neural network have to be put to use, of course.
Generative Model Chatbots
They are in hype. A sequence-to-sequence model is widely used to design chatbots which are capable of generating their own answer when trained on a wide set of real-live interactive datasets.
Text translation is very easy to perform without following any proper sequence, allowing algorithms to ace dependencies between words and plotting to a new language.
Automatic Game Playing
Here, a model is trained to play a computer game formulated on the pixels on the screen. The task is fairly challenging and is one of the most fascinating domains of deep reinforcement models, Deep Mind.
Automatic Handwriting Generation
Here, you have to generate a new handwriting for a particular word or phrase using this technology. The handwritting is given as a sequence of coordinates written by a pen once the samples are done.
As parting thoughts, Deep Learning is still in a nascent stage in India. But, its diverse uses and capabilities will surely put it in the industry frontline some day soon. So, if you are looking for good deep learning training courses in Gurgaon, DexLab Analytics offers some out of the box kind of learning experience. Do check out their deep learning certification courses, they are excellent!
The blog has been sourced from — medium.com/@shridhar743/a-beginners-guide-to-deep-learning-5ee814cf7706
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