Goto

Collaborating Authors

Deep learning definition, algorithms, models, applications & advantages Science online

#artificialintelligence

Deep learning is also known as deep structured learning or hierarchical learning, It is part of a broader family of machine learning methods based on the layers used in artificial neural networks, Deep learning is a subset of the field of machine learning, which is a subfield of AI, Deep learning applications are used in industries from automated driving to medical devices. It is a class of machine learning algorithms that use a cascade of multiple layers of nonlinear processing units for feature extraction and transformation, Each successive layer uses the output from the previous layer as input, It can be learned in supervised (e.g., classification) and/or unsupervised (e.g., pattern analysis) manners, It enables computational models which are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. It is a subfield of machine learning concerned with algorithms inspired by the structure & function of the brain called artificial neural networks, It can teach computers to do what comes naturally to humans: learn by example, Deep learning can be used in driverless cars, allowing them to recognize the stop sign, or to distinguish the pedestrian from the lamppost. The computer model learns to perform classification tasks from images, text, or sound, Deep learning models can achieve state of art accuracy, sometimes exceeding human-level performance, Models are trained by using a large set of labeled data & neural network architectures that have many layers. Neural networks are static & symbolic, They were inspired by information processing & distributed communication nodes in biological systems synaptic structures, they have many differences from the structural & functional properties of biological brains, that make them incompatible with the neurological evidence, while the biological brain of most living organisms is dynamic (plasticity) and analog.


3 Things You Need to Know About Deep Learning

#artificialintelligence

Deep learning is a machine learning technique that teaches computers to do what comes naturally to humans: learn by example. Deep learning is a key technology behind driverless cars, enabling them to recognize a stop sign, or to distinguish a pedestrian from a lamppost. It is the key to voice control in consumer devices like phones, tablets, TVs, and hands-free speakers. Deep learning is getting lots of attention lately and for good reason. It's achieving results that were not possible before.


3 Things You Need to Know About Deep Learning

#artificialintelligence

Deep learning is a machine learning technique that teaches computers to do what comes naturally to humans: learn by example. Deep learning is a key technology behind driverless cars, enabling them to recognize a stop sign, or to distinguish a pedestrian from a lamppost. It is the key to voice control in consumer devices like phones, tablets, TVs, and hands-free speakers. Deep learning is getting lots of attention lately and for good reason. It's achieving results that were not possible before.


Evolution of Deep learning models

@machinelearnbot

None of deep learning models discussed here work as classification algorithms. Instead, they can be seen as Pretrainin, automated feature selection and learning, creating a hierarchy of features etc. Once trained (features are selected), the input vectors are transformed into a better representation and these are in turn passed on to a real classifier such as SVM or Logistic regression. This can be represented as below.


Sentiment Analysis using Deep Learning

#artificialintelligence

The growth of the internet due to social networks such as Facebook, Twitter, Linkedin, Instagram etc. has led to significant users interaction and has empowered users to express their opinions about products, services, events, their preferences among others. It has also provided opportunities to the users to share their wisdom and experiences with each other. The faster development of social networks is causing explosive growth of digital content. It has turned online opinions, blogs, tweets, and posts into a very valuable asset for the corporates to get insights from the data and plan their strategy. Business organizations need to process and study these sentiments to investigate data and to gain business insights(Yadav & Vishwakarma, 2020).