Neural networks and deep learning currently provide the best solutions to many problems in image recognition, speech recognition, and natural language processing. This article will teach you many of the core concepts behind neural networks and deep learning.
Deep learning is a part of machine learning, which involves the use of computer algorithms to learn, improve and evolve on its own. Deep learning may be considered similar to machine learning. However, while machine learning works with simple concepts, deep learning uses artificial neural networks, which imitate the way humans learn and think.
This presentation deals with the basics of AI and it's connection with neural network. Additionally, it explains the pros and cons of AI along with the applications.
Deep learning is a part of machine learning, which involves the use of computer algorithms to learn, improve and evolve on its own. Deep learning may be considered similar to machine learning. However, while machine learning works with simple concepts, deep learning uses artificial neural networks, which imitate the way humans learn and think.
This presentation deals with the basics of AI and it's connection with neural network. Additionally, it explains the pros and cons of AI along with the applications.
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
This is my PPT on mini project on Image Classifier. It's was appreciated by my HOD of CSE of BBDU, Lucknow. It's easy and simple. I put some transitions in it too. So nobody has to think how to put transitions. I tried my best to make it simple for you all. Else you can put your own transitions in it, by simple downloading it.
PLEASE DO LIKE AND SHARE.
Thank You
The presentation briefly answers the questions:
1. What is Machine Learning?
2. Ideas behind Neural Networks?
3. What is Deep Learning? How different is it from NN?
4. Practical examples of applications.

For more information:
https://www.quora.com/How-does-deep-learning-work-and-how-is-it-different-from-normal-neural-networks-and-or-SVM
http://stats.stackexchange.com/questions/114385/what-is-the-difference-between-convolutional-neural-networks-restricted-boltzma
https://www.youtube.com/watch?v=n1ViNeWhC24 - presentation by Ng
http://techtalks.tv/talks/deep-learning/58122/ - deep learning tutorial and slides - http://www.cs.nyu.edu/~yann/talks/lecun-ranzato-icml2013.pdf
Deep learning for NLP - http://www.socher.org/index.php/DeepLearningTutorial/DeepLearningTutorial
papers: http://www.cs.toronto.edu/~hinton/science.pdf
http://machinelearning.wustl.edu/mlpapers/paper_files/AISTATS2010_ErhanCBV10.pdf
http://arxiv.org/pdf/1206.5538v3.pdf
http://arxiv.org/pdf/1404.7828v4.pdf
More recommendations - https://www.quora.com/What-are-the-best-resources-to-learn-about-deep-learning
https://www.learntek.org/blog/machine-learning-vs-deep-learning/
Learntek is global online training provider on Big Data Analytics, Hadoop, Machine Learning, Deep Learning, IOT, AI, Cloud Technology, DEVOPS, Digital Marketing and other IT and Management courses.
https://www.learntek.org/blog/machine-learning-vs-deep-learning/
Learntek is global online training provider on Big Data Analytics, Hadoop, Machine Learning, Deep Learning, IOT, AI, Cloud Technology, DEVOPS, Digital Marketing and other IT and Management courses.
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
This is my PPT on mini project on Image Classifier. It's was appreciated by my HOD of CSE of BBDU, Lucknow. It's easy and simple. I put some transitions in it too. So nobody has to think how to put transitions. I tried my best to make it simple for you all. Else you can put your own transitions in it, by simple downloading it.
PLEASE DO LIKE AND SHARE.
Thank You
The presentation briefly answers the questions:
1. What is Machine Learning?
2. Ideas behind Neural Networks?
3. What is Deep Learning? How different is it from NN?
4. Practical examples of applications.

For more information:
https://www.quora.com/How-does-deep-learning-work-and-how-is-it-different-from-normal-neural-networks-and-or-SVM
http://stats.stackexchange.com/questions/114385/what-is-the-difference-between-convolutional-neural-networks-restricted-boltzma
https://www.youtube.com/watch?v=n1ViNeWhC24 - presentation by Ng
http://techtalks.tv/talks/deep-learning/58122/ - deep learning tutorial and slides - http://www.cs.nyu.edu/~yann/talks/lecun-ranzato-icml2013.pdf
Deep learning for NLP - http://www.socher.org/index.php/DeepLearningTutorial/DeepLearningTutorial
papers: http://www.cs.toronto.edu/~hinton/science.pdf
http://machinelearning.wustl.edu/mlpapers/paper_files/AISTATS2010_ErhanCBV10.pdf
http://arxiv.org/pdf/1206.5538v3.pdf
http://arxiv.org/pdf/1404.7828v4.pdf
More recommendations - https://www.quora.com/What-are-the-best-resources-to-learn-about-deep-learning
https://www.learntek.org/blog/machine-learning-vs-deep-learning/
Learntek is global online training provider on Big Data Analytics, Hadoop, Machine Learning, Deep Learning, IOT, AI, Cloud Technology, DEVOPS, Digital Marketing and other IT and Management courses.
https://www.learntek.org/blog/machine-learning-vs-deep-learning/
Learntek is global online training provider on Big Data Analytics, Hadoop, Machine Learning, Deep Learning, IOT, AI, Cloud Technology, DEVOPS, Digital Marketing and other IT and Management courses.
DIFFERENCE BETWEEN MACHINE LEARNING VS DEEP LEARNING.pptxWriteMe
Deep learning is a subset of machine learning. Machine learning is the science of getting computers to act without being explicitly programmed. Deep learning is a type of machine learning that uses neural networks to learn from data. Read full blog https://writeme.ai/blog/machine-learning-vs-deep-learning-difference/#artificial-intelligence-vs-machine-learning-vs-neural-networks-vs-deep-learning
Deep learning is an emerging topic in artificial intelligence (AI). A subcategory of machine learning, deep learning deals with the use of neural networks to improve things like speech recognition, computer vision, and natural language processing. It's quickly becoming one of the most sought-after fields in computer science. In the last few years, deep learning has helped forge advances in areas as diverse as object perception, machine translation, and voice recognition--all research topics that have long been difficult for AI researchers to crack.
Deep learning and neural network convertedJanu Jahnavi
https://www.learntek.org/blog/industries-blockchain-disrupt/
https://www.learntek.org/
Learntek is global online training provider on Big Data Analytics, Hadoop, Machine Learning, Deep Learning, IOT, AI, Cloud Technology, DEVOPS, Digital Marketing and other IT and Management courses.
categories
Deep learning vs. machine learning what business leaders need to knowSameerShaik43
Artificial intelligence isn’t the future — it is the present. Already, businesses are deploying AI tools in a variety of ways: improving marketing and sales, guiding research and development, streamlining IT, automating HR and more.
https://www.tycoonstory.com/technology/deep-learning-vs-machine-learning-what-business-leaders-need-to-know/
Human Emotion Recognition using Machine Learningijtsrd
It is quite interesting to recognize the human emotions in the field of machine learning. Using a person's facial expression one can know his emotions or what the person wants to express. But at the same time it's not easy to recognize one's emotion easily its quite challenging at times. Facial expression consist of various human emotions such as sad, happy , excited, angry, frustrated and surprise. Few years back Natural language processing was used to detect the sentiment from the text and then it took a step forward towards emotion detection. Sentiments can be positive, negative or neutral where as emotions are more refined categories. There are many techniques used to recognize emotions. This paper provides a review of research work carried out and published in the field of human emotion recognition and various techniques used for human emotions recognition. Prof. Mrs. Dhanamma Jagli | Ms. Pooja Shetty "Human Emotion Recognition using Machine Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd25217.pdfPaper URL: https://www.ijtsrd.com/computer-science/artificial-intelligence/25217/human-emotion-recognition-using-machine-learning/prof-mrs-dhanamma-jagli
Toward enhancement of deep learning techniques using fuzzy logic: a survey IJECEIAES
Deep learning has emerged recently as a type of artificial intelligence (AI) and machine learning (ML), it usually imitates the human way in gaining a particular knowledge type. Deep learning is considered an essential data science element, which comprises predictive modeling and statistics. Deep learning makes the processes of collecting, interpreting, and analyzing big data easier and faster. Deep neural networks are kind of ML models, where the non-linear processing units are layered for the purpose of extracting particular features from the inputs. Actually, the training process of similar networks is very expensive and it also depends on the used optimization method, hence optimal results may not be provided. The techniques of deep learning are also vulnerable to data noise. For these reasons, fuzzy systems are used to improve the performance of deep learning algorithms, especially in combination with neural networks. Fuzzy systems are used to improve the representation accuracy of deep learning models. This survey paper reviews some of the deep learning based fuzzy logic models and techniques that were presented and proposed in the previous studies, where fuzzy logic is used to improve deep learning performance. The approaches are divided into two categories based on how both of the samples are combined. Furthermore, the models' practicality in the actual world is revealed.
It’s long ago, approx. 30 years, since AI was not only a topic for Science-Fiction writers, but also a major research field surrounded with huge hopes and investments. But the over-inflated expectations ended in a subsequent crash and followed by a period of absent funding and interest – the so-called AI winter. However, the last 3 years changed everything – again. Deep learning, a machine learning technique inspired by the human brain, successfully crushed one benchmark after another and tech companies, like Google, Facebook and Microsoft, started to invest billions in AI research. “The pace of progress in artificial general intelligence is incredible fast” (Elon Musk – CEO Tesla & SpaceX) leading to an AI that “would be either the best or the worst thing ever to happen to humanity” (Stephen Hawking – Physicist).
What sparked this new Hype? How is Deep Learning different from previous approaches? Are the advancing AI technologies really a threat for humanity? Let’s look behind the curtain and unravel the reality. This talk will explore why Sundar Pichai (CEO Google) recently announced that “machine learning is a core transformative way by which Google is rethinking everything they are doing” and explain why "Deep Learning is probably one of the most exciting things that is happening in the computer industry” (Jen-Hsun Huang – CEO NVIDIA).
Either a new AI “winter is coming” (Ned Stark – House Stark) or this new wave of innovation might turn out as the “last invention humans ever need to make” (Nick Bostrom – AI Philosoph). Or maybe it’s just another great technology helping humans to achieve more.
Everything You Need to Know About Computer VisionKavika Roy
https://www.datatobiz.com/blog/computer-vision-guide/
To most, they consist of pixels only, but digital images, like any other form of content, can be mined for data by computers. Further, they can also be analyzed afterward. Use image processing methods, including computers, to retrieve the information from still photographs, and even videos. Here we are going to discuss everything you must know about computer vision.
There are two forms-Machine Vision, which is this tech’s more “traditional” type, and Computer Vision (CV), a digital world offshoot. While the first is mostly for industrial use, as an example are cameras on a conveyor belt in an industrial plant, the second is to teach computers to extract and understand “hidden” data inside digital images and videos.
Facebook this August said it was open-sourcing its work to improve its Computer Visiontechnology software for users further. This image was posted by FB Research scientist Piotr Dollar to explain the difference between human and computer vision.
Thanks to advances in artificial intelligence and innovations in deep learning and neural networks, the field has been able to take big leaps in recent years, and in some tasks related to detection and labeling of objects has been able to surpass humans.
One of the driving factors behind computer vision development is the amount of data we produce now, which will then get used to educate and develop computer vision.
Traditional Machine Learning had used handwritten features and modality-specific machine learning to classify images, text or recognize voices. Deep learning / Neural network identifies features and finds different patterns automatically. Time to build these complex tasks has been drastically reduced and accuracy has exponentially increased because of advancements in Deep learning. Neural networks have been partly inspired from how 86 billion neurons work in a human and become more of a mathematical and a computer problem. We will see by the end of the blog how neural networks can be intuitively understood and implemented as a set of matrix multiplications, cost function, and optimization algorithms.
The 7 Key Steps To Build Your Machine Learning ModelRobert Smith
A machine learning model is a file that has been trained to recognize certain types of patterns. You train a model over a set of data, providing it an algorithm that it can use to reason over and learn from those data.
Environmental Monitoring System using IoT, AI and MLRobert Smith
AI, ML, IoT, and Emerging Tech. Cognitive technologies like machine learning and AI (artificial intelligence) certainly have proven to be an important part of the IoT (Internet of Things) sector because they can help make products and services smarter and, therefore, more valuable.
The Key Differences Between Rule-Based AI And Machine LearningRobert Smith
While a rules-based system could be considered as having “fixed” intelligence, in contrast, a machine learning system is adaptive and attempts to simulate human intelligence. Eventually, the machine will be able to interpret, categorize, and perform other tasks with unlabeled data or unknown information on its own.
Top 10 Skills You Need For A High-Paying Machine Learning CareerRobert Smith
There are vast applications of Machine Learning in computer science including different types of learning such as supervised learning, unsupervised learning, and reinforcement learning. Machine Learning can be a rewarding career for students who are good in mathematics and statistics and have sharp programming skills.
How Cyber Security Courses Opens Up Amazing Career Opportunities?Robert Smith
To become a security consultant, you might follow a career path similar to this: Earn a bachelor's degree in computer science, information technology, cyber security, or a related field. Or, gain equivalent experience with relevant industry certifications. Pursue an entry-level position in general IT or security.
5 Key Trends in Virtual Reality and Augmented Reality Careers in 2020?Robert Smith
The AR and VR market had sold 8.9 million units by the end of 2018, which is expected to grow to 65.9 million by the end of 2022. AR and VR headset sales are expected to grow to $9.7 billion in 2020. The majority of augmented reality users fall into the 16 to 34 age bracket.
How Will Chatbots Affect Customer Service?Robert Smith
AI chatbots use your existing information and resources, like FAQs or knowledge base articles, to help answer and resolve your customers' questions. They can recognize and answer multiple forms of the same question and can be trained to give instant responses using your preferred voice and tone.
Learn Where Artificial Intelligence Is Used NowadaysRobert Smith
This topic explains about AI Technology. Here you how AI use in different industries and they enhance your daily revenue and change your career opportunity.
How Is IoT Technology Transforming The Agricultural Sector?Robert Smith
Basically, Smart Agricultural Technology enables farmers to have better control over the process of growing crops and rearing livestock. This way it brings massive efficiencies of scale, cuts costs, and helps in saving scarce resources, like water.
Significance Of Hadoop For Data ScienceRobert Smith
Hadoop is an important tool for data science when the volume of data exceeds the system memory or when the business case requires data to be distributed across multiple servers.
How Python Is Used In Machine LearningRobert Smith
A python is a great tool for the development of programs that perform data manipulation whereas R is statistical software that works on a particular format of the dataset. Python provides the various development tools which can be used to work with machine learning & other systems. R has a learning curve to it.
Few Chatbots Expert Interview Questions & Answer For FreshersRobert Smith
Chatbots — automated conversation systems — have become increasingly sophisticated. Should you design and deploy one that can interact with your customers? If you’re an executive making that decision right now, you may feel caught between A.I. hype on the one hand and the fear that machines might not treat your customers right on the other.
How ai transforms the marketing domain for the better Robert Smith
AI solutions provide marketers with a deeper knowledge of consumers and prospective clients, enabling them to deliver the right message, to the right person, at the right time. Marketers can use AI solutions to take these profiles a step further, refine marketing campaigns, and create highly personalized content.
How machine learning & artificial intelligence implement in e commerce Robert Smith
In this presentation, we will discuss AI and ML with specific reference to the e-commerce domain and how it helps e-commerce companies in driving their sales. AI and ML, no doubt, are adding valuable elements to e-commerce platforms to help them stay in the market.
How artificial intelligence certification help you in future to grow your selfRobert Smith
We are living in an era where machines have influenced our lives. We have seen some huge transformations in this field and one such development is Artificial Intelligence which is the simulation of human intelligence by the machines. There have been some great developments in this field which has impacted our lives.
How to become an expert in augmented reality Robert Smith
Wouldn't it be great to have a world that can superimpose an image or create a real-world experience? Well, this is not a fantasy anymore. The world is now witnessing a paradigm shift where we are witnessing the surge of new technological developments that are changing the pace of learning and growth.
How virtual reality help the students to change the way of learning Robert Smith
Technology has transformed different fields; it has brought forth new ways that have kick-started a new era of learning and development. One such transformation is Virtual Reality.
How is ai important to the future of cyber security Robert Smith
Today’s era is driven by technology in every aspect of our lives, so much that we’ve now increased our dependence on technology on a daily basis. With an increase in the dependency, we’re now very vulnerable and exposed to the intermittent threat posed as cyber-attacks. Cyber-attack threats have plagued businesses, corporates, governments, and institutions.
In today’s context, the big data market is rapidly undergoing contortions that define market maturity, such as consolidation. Big data refers to large volumes of data. This can be both structured and unstructured data. Big data is data that is huge in size and grows exponentially with time. As the data is too large and complex, traditional data management tools are not sufficient for storing or processing it efficiently. But analyzing big data is crucial to know the patterns and trends to be adopted to improve your business.
Honest Reviews of Tim Han LMA Course Program.pptxtimhan337
Personal development courses are widely available today, with each one promising life-changing outcomes. Tim Han’s Life Mastery Achievers (LMA) Course has drawn a lot of interest. In addition to offering my frank assessment of Success Insider’s LMA Course, this piece examines the course’s effects via a variety of Tim Han LMA course reviews and Success Insider comments.
Instructions for Submissions thorugh G- Classroom.pptxJheel Barad
This presentation provides a briefing on how to upload submissions and documents in Google Classroom. It was prepared as part of an orientation for new Sainik School in-service teacher trainees. As a training officer, my goal is to ensure that you are comfortable and proficient with this essential tool for managing assignments and fostering student engagement.
Acetabularia Information For Class 9 .docxvaibhavrinwa19
Acetabularia acetabulum is a single-celled green alga that in its vegetative state is morphologically differentiated into a basal rhizoid and an axially elongated stalk, which bears whorls of branching hairs. The single diploid nucleus resides in the rhizoid.
Embracing GenAI - A Strategic ImperativePeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
For more information, visit-www.vavaclasses.com
Welcome to TechSoup New Member Orientation and Q&A (May 2024).pdfTechSoup
In this webinar you will learn how your organization can access TechSoup's wide variety of product discount and donation programs. From hardware to software, we'll give you a tour of the tools available to help your nonprofit with productivity, collaboration, financial management, donor tracking, security, and more.
The Roman Empire A Historical Colossus.pdfkaushalkr1407
The Roman Empire, a vast and enduring power, stands as one of history's most remarkable civilizations, leaving an indelible imprint on the world. It emerged from the Roman Republic, transitioning into an imperial powerhouse under the leadership of Augustus Caesar in 27 BCE. This transformation marked the beginning of an era defined by unprecedented territorial expansion, architectural marvels, and profound cultural influence.
The empire's roots lie in the city of Rome, founded, according to legend, by Romulus in 753 BCE. Over centuries, Rome evolved from a small settlement to a formidable republic, characterized by a complex political system with elected officials and checks on power. However, internal strife, class conflicts, and military ambitions paved the way for the end of the Republic. Julius Caesar’s dictatorship and subsequent assassination in 44 BCE created a power vacuum, leading to a civil war. Octavian, later Augustus, emerged victorious, heralding the Roman Empire’s birth.
Under Augustus, the empire experienced the Pax Romana, a 200-year period of relative peace and stability. Augustus reformed the military, established efficient administrative systems, and initiated grand construction projects. The empire's borders expanded, encompassing territories from Britain to Egypt and from Spain to the Euphrates. Roman legions, renowned for their discipline and engineering prowess, secured and maintained these vast territories, building roads, fortifications, and cities that facilitated control and integration.
The Roman Empire’s society was hierarchical, with a rigid class system. At the top were the patricians, wealthy elites who held significant political power. Below them were the plebeians, free citizens with limited political influence, and the vast numbers of slaves who formed the backbone of the economy. The family unit was central, governed by the paterfamilias, the male head who held absolute authority.
Culturally, the Romans were eclectic, absorbing and adapting elements from the civilizations they encountered, particularly the Greeks. Roman art, literature, and philosophy reflected this synthesis, creating a rich cultural tapestry. Latin, the Roman language, became the lingua franca of the Western world, influencing numerous modern languages.
Roman architecture and engineering achievements were monumental. They perfected the arch, vault, and dome, constructing enduring structures like the Colosseum, Pantheon, and aqueducts. These engineering marvels not only showcased Roman ingenuity but also served practical purposes, from public entertainment to water supply.
2. 2
Machine learning is a hot topic of discussion nowadays. And you can find its
multitude of uses across the different segments. Deep learning is an integral part
of machine learning. When we talk about machine learning or deep learning, it has
created quite a stir in the market. Basically, these technologies work on algorithms
and programming that enables the machine to think like a human and take a
decision. So what works behind them, you will learn about all this aspect in any of
the machine learning training programs. Still, here, we are going to cover one of
the elements that ensure the seamless working of deep learning enables
technology. Yes, we are going to talk about neural networks.
3. 3
We all know that machines have brains, but in recent years, we have seen some
significant development in this domain, and this is because of the surge of new
technologies like machine learning and deep learning. What works behind the
scene is the neural network. These are designed similar to the neurons of the
human brain. These neural networks work on the detangling complex relationship
and unfold it to the machines so that they can perform their task.
4. Subsets Of Machine Learning
If you have undergone machine learning training or are planning to go for a
machine learning course, then you would get to know that there are subsets of
machine learning and each of these subsets have the potential to form a new
career for those who are willing to grow in the field of artificial intelligence. The
following are the subsets of machine learning :
1. Neural network- As we have mentioned above, neural network functioning is
designed similar to the human brain, with the help of this network software
learns to recognize images and enables the machine to make a decision.
With the help of a neural network, machines can also make predictions and
decisions and that too with accuracy. But it also depends on the accuracy of
the data.
4
5. 2. Natural Language Processing (NLP)- It enables the machine to understand
human language and interpret. As it continues to develop, machines will also learn
how to respond in a similar way to humans.
3. Deep Learning- It uses machine learning tools and uses the same to solve
problems and make decisions. With deep learning, the data is processed via
neural networks and thus making the machine to work similarly as the human
does. This applies to text, images, speech to come to a conclusion. In simple
words, its objective is to make a decision similar to human beings.
5
6. What Sets The Neural Network Apart From
Other Networks?
Well, neural networks are designed to perform on the same base as the neurons
in the human brain. The functioning of a brain neuron involves receiving input and
then instigates an output that is used by another neuron. The neural network
security training also works on a similar pattern; they stimulate behaviour in
learning about the data which is collected and then predicting outcomes.
There are three layers of neurons in the neural network:
1. Input layer
2. Hidden layer
3. Output layer
6
7. 7
When we talk about deep neural networks, then it has many layers and the
process of using all these layers is called deep learning.
Deep neural networks are complex neural networks, and they have around 1000
or more neurons per layer. The more is the number of networks, the more complex
tasks it can handle. But, this might take some time for the same. In the case of
the classic neural network, the information traverses in a single direction where
each layer is wholly connected to neighbours.
8. How Can Neural Network Create An
Impact On Our Life?
Well, in the years to come, we are going to witness a significant impact on technology.
And ML is going to be an integral part of the same. Machine learning algorithms are
going to be a key game-changer. Here is how it is going to change our lives in the
years ahead:
1. Language barrier elimination via interpreting tools for personal use
2. Image processing and photo effects
3. Video analytics
4. Using a photo to find a person on the internet
5. IoT concept development via voice interaction interfaces
6. Self-learning system for production and device management
7. Object recognition and classification of images
8
9. 9
Conclusion
These are few of the many ways how machine learning and its subsets, neural
network, NLP and deep learning are going to impact our lives. This will inevitably
lead to a growing demand for individuals who are skilled in machine learning. This
field is not only going to be a great career booster but also will lead to a lot of
development. Global Tech Council is providing an online machine learning
training program. This program aims at imparting all the information about
machine learning and its allied concepts. You can simply enrol for this program
and let your career grow by leaps and bounds.