Have you ever thought how Google’s translator can translate entire paragraphs from one language into another within milliseconds? How YouTube and Netflix and are able to give us relevant recommendations? Or how self-driving automobiles are even possible? All of this is a product of Artificial Neural Networks and Deep Learning.
Deep learning is considered as a subset of machine learning. It is a field that is based on learning and improving on its own by examining computer algorithms. We are providing
6 Months Program in Deep Learning in collaboration with IBM by DataTrained
Most deep learning techniques use neural network architectures, that is the reason why deep learning models are often known as deep neural networks.
The term “deep” typically refers to the number of hidden layers in the neural networking. Traditional neural networks just include 2-3 hidden layers, while deep networks can have as many as 150 layers.
Deep learning models are trained by using huge data sets and neural network architectures that learn functions directly from data without the need for manual feature extraction.
One of the most famous types of deep neural networks is called as as convolutional neural networks. A CNN convolves learned characterstics with input data, also makes use of 2D convolutional layers, making this architecture properly suited to processing 2D data, like pictures.
CNNs remove the need for manual element extraction, so you do not need to recognize features used to classify pictures. The CNN works by extracting features directly from pictures. The relevant features are not pretrained; they are learned while the network trains on a collection of pictures. automated feature extraction makes deep learning models highly accurate and precise for computer vision tasks like object classification.
CNNs learn to detect various attributes of a picture using many hidden layers. Every hidden layer adds to the complexity of the learned picture features.
Practical examples of deep learning
Whether it’s Alexa ,Cortana or siri, the virtual assistants of online service providers work with deep learning to help you understand your speech and the language humans use whenever they communicate with them.
deep learning algorithms can automatically translate between languages. This can be extremely effective for travelers, business individuals as well as government officials.
3. Vision for driverless trucks, drones and autonomous cars
The more information the algorithms get, the better they are able to act human-like in their data processing.
Chatbots and service bots
Chatbots are able to respond in an intelligent and helpful way to an increasing amount of auditory and text questions due to deep learning.
Today, deep learning algorithms are in a position to utilize the context and objects in the pictures to color them to recreate the black and white image in color. The results are accurate and very impressive.
Deep learning is used for face recognition not just for security reasons but for tagging individuals on Facebook posts and we might be able to pay for things in a store only by using our faces in the future.
7. Medicine and pharmaceuticals
From tumor diagnoses to personalized medication created especially for an person’s genome, deep learning in the healthcare field has the attention of a lot of pharmaceutical companies.
8.Personalized shopping and entertainment
Ever wondered how Netflix comes up with suggestions for what you should watch next? Or how Amazon comes up with ideas for what you should buy next and those suggestions are exactly what you need but just never knew it before? Yes, it’s deep-learning algorithms at work.
