What makes Deep Learning different from traditional Machine Learning methods?

1. So you are at a party where you c̶a̶n̶’̶t̶ ̶s̶e̶e̶m̶ ̶t̶o̶ ̶s̶h̶u̶t̶ ̶u̶p̶ ̶ are telling everyone enthusiastically about how AI will change the world and you know that because you are an _AI Enthusiast _or a Machine Learning Practitioner or whatever fancy-sounding title you use to describe yourself. Then, someone who reads the newspaper every now and then asks you “I don’t really get the difference between Deep Learning and traditional Machine Learning. What is the difference between Deep Learning and (classical) Machine learning?”. You give some handwavy explanation about how neurons in a neural network are based on the human brain blah blah blah…an answer that leaves people unconvinced and leaves you embarrassed.

Hierarchical Compositionality

Deep Learning model architectures tend to be based on the idea that the world around exhibits hierarchical compositionality. In simple terms, every complex thing in the world is made up of simple building blocks and these simple building blocks are in turn made up of even simpler building blocks. This is analogous to how in Chemistry, a compound is made up of molecules and molecules in turn are made of atoms or in a business context, how the organisation is made up of departments, departments are made of teams and teams are made of employees.

Likewise, in an image, pixels form edges, edges form shapes, shapes form textons (a complex combination of shapes), textons form objects and objects form a complete image. In the case of Natural Language Processing, characters form words, words form phrases, phrases form sentences, sentences form paragraphs. By mirroring this hierarchical nature of the data through their architectures, Deep Learning models are able to learn how the simpler parts form the complex whole by modeling the hierarchical relationships in the data.

In a traditional Machine Learning approach, an expert would get involved to hand-pick these low-level features and then hand-engineer the extraction of these low-level features, which could then be fed to model such as a SVM classifier. Not only is this process cumbersome, choosing and defining the features involves some degree of interpretative decision making , which is prone to bias and loss of information due to oversimplification of the data. Deep Learning, on the other hand, takes the raw data as its input and automatically learns the hierarchical elements and their relationships through the training process.

Distributed Representation

In a neural network, neurons work together to learn representations of the data. A single neuron, by itself, neither encodes everything nor does it really encode one particular thing. Just as how in an orchestra no single instrument can truly express the full richness of a musical composition, in a neural network, the complex representation of data is learnt by the whole network of neurons working together by passing information back and forth.

Contrarily, in a traditional Machine Learning approach, to classify the image of a cat as such, parts of the model pipeline would be configured to detect specific features of a cat. For example, there could be a component detecting whiskers, another component detecting a tail and so on. In Deep Learning, however, no single neuron is specifically configured to look for whiskers or a tail.

This trait of distributed representation actually gives Deep Learning an edge in certain situations. One such situation is transfer learning. For example, a deep learning model trained for classifying cat breeds could easily be used for dog breed classification with some simple fine-tuning.

End-to-end Learning

The idea of Deep Learning applying the learning process in an end-to-end manner was implied heavily in the aforementioned points. However, this distinction is non-trivial and bears repeating as its own separate distinguishing characteristic of Deep Learning. In Deep Learning, the learning process is applied to every step of the process — the model takes the raw data as input, performs feature extraction by learning the feature representations and learns the parameters to perform the necessary task such as classification. In fact, the process is _so _end-to-end that the line between feature extraction and parameter optimisation for a task such as classification is very blur.

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Applications of machine learning in different industry domains

Machine learning applications are a staple of modern business in this digital age as they allow them to perform tasks on a scale and scope previously impossible to accomplish.Businesses from different domains realize the importance of incorporating machine learning in business processes.Today this trending technology transforming almost every single industry ,business from different industry domains hire dedicated machine learning developers for skyrocket the business growth.Following are the applications of machine learning in different industry domains.

Transportation industry

Machine learning is one of the technologies that have already begun their promising marks in the transportation industry.Autonomous Vehicles,Smartphone Apps,Traffic Management Solutions,Law Enforcement,Passenger Transportation etc are the applications of AI and ML in the transportation industry.Following challenges in the transportation industry can be solved by machine learning and Artificial Intelligence.

• ML and AI can offer high security in the transportation industry.
• It offers high reliability of their services or vehicles.
• The adoption of this technology in the transportation industry can increase the efficiency of the service.
• In the transportation industry ML helps scientists and engineers come up with far more environmentally sustainable methods for powering and operating vehicles and machinery for travel and transport.

Healthcare industry

Technology-enabled smart healthcare is the latest trend in the healthcare industry. Different areas of healthcare, such as patient care, medical records, billing, alternative models of staffing, IP capitalization, smart healthcare, and administrative and supply cost reduction. Hire dedicated machine learning developers for any of the following applications.

• Identifying Diseases and Diagnosis
• Drug Discovery and Manufacturing
• Medical Imaging Diagnosis
• Personalized Medicine
• Machine Learning-based Behavioral Modification
• Smart Health Records
• Clinical Trial and Research
• Better Radiotherapy
• Crowdsourced Data Collection
• Outbreak Prediction

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Finance industry**

In financial industries organizations like banks, fintech, regulators and insurance are Adopting machine learning to improve their facilities.Following are the use cases of machine learning in finance.

• Fraud prevention
• Risk management
• Investment predictions
• Customer service
• Digital assistants
• Marketing
• Network security
• Loan underwriting
• Algorithmic trading
• Process automation
• Document interpretation
• Content creation
• Trade settlements
• Money-laundering prevention
• Custom machine learning solutions

Education industry

Education industry is one of the industries which is investing in machine learning as it offers more efficient and easierlearning.AdaptiveLearning,IncreasingEfficiency,Learning Analytics,Predictive Analytics,Personalized Learning,Evaluating Assessments etc are the applications of machine learning in the education industry.

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Future of machine learning
Continuous technological advances are bound to hit the field of machine learning, which will shape the future of machine learning as an intensively evolving language.

• Improved Unsupervised Algorithms
• Increased Adoption of Quantum Computing
• Enhanced Personalization
• Improved Cognitive Services
• Rise of Robots

**Conclusion
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Today most of the business from different industries are hire machine learning developers in India and achieve their business goals. This technology may have multiple applications, and, interestingly, it hasn’t even started yet but having taken such a massive leap, it also opens up so many possibilities in the existing business models in such a short period of time. There is no question that the increase of machine learning also brings the demand for mobile apps, so most companies and agencies employ Android developers and hire iOS developers to incorporate machine learning features into them.

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Machine Learning Vs Deep Learning: Difference Between Machine Learning and Deep Learning

Machine learning and Deep learning both are the buzzwords in the tech industry. Machine learning and deep learning both are the subdivision of artificial intelligence technology. If we further breakdown, deep learning is a subdivision of machine learning technology.

If you are familiar with the basics of machine learning and deep learning, it is excellent news!

However, if you are new to the AI field, then you must be confused. What is the difference between machine learning and deep learning?

There is nothing to worry about. This article will explain the differences in easy to understand language.

What is Machine Learning?

Machine learning is a branch of technology that studies computer algorithms. These algorithms allow the system to learn from data or improve by itself through experience. Machine learning algorithms make predictions or decisions without being explicitly programmed.

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5 Latest Technology Trends of Machine Learning for 2021

Check out the 5 latest technologies of machine learning trends to boost business growth in 2021 by considering the best version of digital development tools. It is the right time to accelerate user experience by bringing advancement in their lifestyle.

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Hire Machine Learning Developers in India

Hire machine learning developers in India ,DxMinds Technologies is the best product engineering company in India making innovative solutions using Machine learning and deep learning. We are among the best to hire machine learning experts in India work in different industry domains like Healthcare retail, banking and finance ,oil and gas, ecommerce, telecommunication ,FMCG, fashion etc.
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Hire machine learning developers in India ,DxMinds Technologies is the best product engineering company in India making innovative solutions using Machine learning and deep learning. We are among the best to hire machine learning experts in India work in different industry domains like Healthcare retail, banking and finance ,oil and gas, ecommerce, telecommunication ,FMCG, fashion etc.

Services

Product Engineering & Development

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Integration / Data Management

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