This article aims to provide all the information including the high-end advantages of utilizing this framework in several applications and use cases.

A Brief Introduction to Tensor2Tensor

Tensor2Tensor, which is also called T2T in short is created to hasten the accessibility to the deep learning models across several platforms. The platform includes integrated datasets and deep learning models that can be exploited for distinct tasks such as image generations, image classifications, speech recognition, and sentiment analysis, and also for tedious tasks such as language translation.

Collectively, the T2T is a single shot library with several integrated models and datasets which can be utilized for several tasks. The application library fecilitates the flexibility to include vital data models into their library as they stimulate the inclusion of data models and also fix the potential bugs that arise.

Therefore, let us understand some of the vital functionalities facilitated by the Tensor2Tensor library. Notably, there are 4 main functionalities backed by the T2T library.

Problems

The following functionality facilitates distinct features, inputs and targets to be aquired from the models. to record, a standard directory stores the data features.

Hyperparameter Set

Its functionality is responsible for storing some of the hyperparameters of distinct models and the ready availability of data for each problem in the library.

Adding custom components

The T2T library includes the addition of required data and models according to requirements. So the primary function of this feature is to serve as the mechanism that allows the data and model addition as per the Tensor2Tensor library.

Trainer

This is one of the important functionalities of the T2T library that is utilised for accessing the models and examining the models that exist in the library. The functionality enables users the easiness to switch between the models, data, and hyperparameters.

Why Tensor2Tensor is Essential?

The core aim of the T2T library is to ensure deep learning and distinct complex models are easily obtainable and producible regardless of device limitations and specifications. The Tensor2Tensor (T2T) enables the storage of distinct types of data like audio, images, text, and more in one library and trains several models with different levels of architecture and complexity in a single framework. 

Speech recognition, image generation, and language translation are some of the data and models that are accessible in the T2T library.

Features of Tensor2Tensor

The massive potential of the T2T library has enabled the library to allow certain standard characteristics of execution which accounts for its utilization.

• Several complex models are made accessible in a simple, intuitive format, and if essential, auxiliary models can be included in the library that could be utilized in the future.

• Distinct forms of datasets such as text, audio, and image are available that could be utilized either to generate data or to use for different tasks. 

• Datasets and the models can be made accessible, and the model’s hyperparameters can be modified according to necessities and trained on the platform constraints and hardware specifications. 

Benefits of Tensor2Tensor

The T2T library has distinct types and uses cases that could be exploited to execute tedious tasks and modelling. 

Here we list some of the standard functionalities and benefits of the Tensor2Tensor library.

Mathematical Language

Understanding For this functionality, the T2T fecilitates a handy dataset that is known as the MLU dataset under the problems functionality.

Question Answering

The Tensor2Tensor library includes a pretrained dataset called the “BABI” dataset. There are distinct sets of question answering sets and subsets in the data. Image classification, Image generation, Language modelling, Sentiment analysis, and speech recognition are some other benefits of the Tensor2Tensor model.

Tensor2Tensor in a Nutshell

The T2T library objects to facilitate a single shot framework to allow flexible utilization of complex data and models across different platforms and hardware specifications. The platform thrives to accelerate the deep learning training process and make complex deep learning easily accessible to users.

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Although, because of the disaster, the market currently gets tougher to be able to set it up again with more men force as they are doing previous. So, it might possible that you have to prepare yourself mentally for the long run hiring journey and many rejections in along the way.

Below I give 8 unique ideas for your data science portfolio with attached reference articles from where you will get the insights of how to get started with any particular idea.

1. Sentiment analysis for depression based on social media posts

This topic is so responsive to be considered nowadays and in urgent need to do something about it. There are more than 264 million individuals worldwide who are suffering from depression. Depression is the main reason of disability worldwide and is a important supporter of the overall global load of disease, and nearly 800,000 individuals consistently bite the dust because of suicide every year

Internet-based life gives the main edge chance to change early melancholy mediation services, especially in youthful grown-ups. Consistently, roughly 6,000 Tweets are tweeted on Twitter, which relates to more than 350,000 tweets sent for each moment, 500 million tweets for every day, and around 200 billion tweets for each year.

As indicated by the Pew Research Center, 72% of the public uses some sort of internet-based life. Datasets released from social networks are important to numerous fields, for example, human science and brain research. But the supports from a specialized point of view are a long way from enough, and explicit methodologies are desperately out of luck.

2. Sports match video to text summarization using neural network

So this project idea is basically based on getting a exact summary out of sports match videos. There are sports websites that tell about highlights of the match. Various models have been proposed for the task of extractive text summarization, but neural networks do the best job. As a rule, summarization alludes to introducing information in a brief structure, concentrating on parts that convey facts and information, while safeguarding the importance.

Automatically creating an outline of a game video gives rise to the challenge of unique charming minutes or highlights of a game.

So, one can attain that using some deep learning techniques like 3D-CNN, RNN , LSTM, and also through machine learningalgorithms by dividing the video into different sections and then applying SVM NN and k-means algorithms.

3. Handwritten equation solver using CNN

Among all the issues, handwritten mathematical expression recognition is one of the confusing issues in the region of computer vision research. You can train a handwritten equation solver by handwritten digits and mathematical symbols using Convolution Neural Network (CNN) with some image processing techniques. Developing such a system requires training our machines with data, making it capable at learning and making the required forecast.

4. Business meeting summary generation using NLP

Ever got stuck in a situation where everyone wants to see a summary and not the full report? Well, I faced it during my school and college days, where we spent a lot of time preparing a whole report, but the teacher only has time to read the summary.

Summarization has risen as an inevitably helpful way to tackle the issue of data over-burden. Extracting information from conversations can be of very good profitable and educational value. This can be done by feature capture of the statistical, linguistic, and sentimental aspects with the dialogue structure of the conversation.

Manually changing the report to a summed up form is too time taking, isn’t that so? But one can rely on Natural Language Processing techniques to achieve that.

Text summarization using deep learning can understand the context of the whole text. Isn’t it a dream comes true for all of us who need to come up with a quick summary of a document!

5. Facial recognition to detect mood and suggest songs accordingly

The human face is an important part of an individual’s body, and it mainly plays a significant role in knowing a person’s state of mind. This eliminates the dull and tedious task of manually dividing or grouping songs into various records and helps in generating an appropriate playlist based on an individual’s moving features.

Computer vision is an interdisciplinary field that helps conveys a high-level understanding of digital images or videos to computers. Computer vision components can be used to determine the user’s emotion through facial expressions.

6. Finding out habitable exo-planet from images captured by space vehicles like Kepler

In the most recent decade, over a million stars were monitored to recognize transiting planets. Manual understanding of potential explanted candidates is labor-intensive and subject to human mistake, the consequences of which are hard to evaluate. Convolution neural networks are fit for identifying Earth-like explants in noisy time-series data with more prominent precision than a least-squares strategy.

7. Image regeneration for old damaged reel picture

I know how time-consuming and sore it is to get back your old damaged photo in the original form as it was previous. So, this can be done using deep learning by finding all the image defects, and using in painting algorithms, so that one can easily find out the defects based on the pixel values around them to restore and colorize the old photos.

8. Music generation using deep learning

Music is an variety of tones of various frequencies. So, automatic music generation is a process of composing a short piece of music with the least human arbitration. Recently, deep learning engineering has become the cutting edge for programmed music generation.

Conclusion

I know that it’s a real struggle to build up a cool data science portfolio. But with such a collection that I have provided above, you can make above-average development in that field. The collection is new, which gives the chance for research purposes too. So, researchers in Data Science can also choose these ideas to work on so that their research would be a great help for Data Scientists to start with the project.

So, I will not only recommend this for newbies in the data science area but also senior data scientists. It will open many new paths during your career, not only because of the projects but also through the newly gained network.

These ideas show you the wide range of possibilities and give you the ideas to think out of the box.

So, basically, I enjoy doing such projects that give us a way to gain huge knowledge in a way and let us explore the unexplored dimensions. That is our main focus when dedicating time to such projects.

Original. Reposted with permission.

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In this blog, we’ll look at variety of areas of tech, how much demand exists for each Technology Skills, and where to go to start your learning journey.

1. Artificial Intelligence

AI is quickly changing the scenery of work, making it a thrilling time for programmers looking for something new. Hiring growth for AI specialists has grown 74% yearly in the past 4 years. Because of its increasingly extensive acceptance, AI specialists earned LinkedIn’s #1 rising jobs spot.

There is crossover with machine learning here, but the key disparity is that AI is a broader concept pertaining to machines future to act intelligently like humans, whereas machine learning relies on devices making sense of a specific set of data.

2. Machine Learning

Machine learning is one of the most advanced and exciting fields moving into the future, making it one of the most gainful skills you can learn. From Siri and Alexa to chatbots to prognostic analysis to self-driving cars, there are a ton of uses for this innovative tech.

Those who starting taking online courses in machine learning now will still be getting in comparatively early, as demand is only rising from here. According to McKinsey, 49% of companies are currently exploring or planning to use machine learning.

3. Data Science & Analytics

Two consistently in-demand tech jobs within Big Data include data science and data analytics. Revenue from Big Data applications and analytics is future to grow from $5.3B in 2018 to $19.4B in 2026.

84% of enterprise has launched higher analytics and Big Data initiatives to go faster their decision-making and bring greater correctness. This is part of why data science has earn a top mark on LinkedIn’s rising jobs report all three years the report has been conducted.

4. Data Engineering

Data engineering is dividing from data science, but the former is what enable the latter to exist. Data engineers build the communications and tool those data scientists rely on to conduct their own work.

Since 2015, the hiring growth rate of this technology job has increased by nearly 35% across a wide diversity of industries.

5. Data Visualization

Data visualization is a way to help people understand the meaning of data by insertion it in a visual context. For example, by turning spreadsheets or reports into charts and graphs that can be easily understood.

Think of this career as a viaduct between technical and non-technical roles. You’re taking the data collected by analysts and transforming it into a form anyone can understand.

6. Network and Information Security (Cybersecurity)

For any company that collect customer information or deals with responsive data of their own, keeping networks secure is supreme.

When data breaches do happen, they can be big, interesting, and costly for the company to recover from. The number of data breaches increased by 60% in 2019, and companies notably hacked in the past include Sony, LinkedIn, Chipotle, and many more.

7. Cloud Computing/AWS

Cloud computing jobs are on the go up because more and more companies are switching from the traditional server communications to cloud solutions. According to Gartner, the market for public cloud services is future to grow by 17% in 2020 to total $266.4 billion.

Cloud Amazon Web Services is one of these cloud platforms, feature content delivery, database storage, networking, and more–over 50 services in total.

Which Technology Skills Will You Learn This Year?

Any of these gainful technical skills will set you well on your way to a successful new tech career in 2020 and further than. Now it just comes down to which is best suited for your personality and interests–and that one’s up to you.

Whether you’re brand-new to coding and tech, or you have some experience already and are ready to narrow down and concentrate in a specific area, there are a lot of opportunities to level up your skills without spending luck.

We are NearLearn  providing the  Machine Learning Training institute in Bangalore, Best Blockchain Training in Bangalore and also we provide top training on Artificial Intelligence, Deep Learning, Data Science, Fullstack, Blockchain, DevOps and ReactJs.

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10 Mandatory Skills to Become a Data Scientist 

Technical Skills  

Programming, Packages, and Software 

Data scientists is to fold all the information or raw data and alter this into actionable visions, they need to have progressive knowledge in coding and statistical data processing. Some of the most common programming languages used by data scientists are Python, R, SQL, NoSQL, Java, Scala, Hadoop etc.

Machine Learning and Deep Learning 

Machine Learning and Deep Learning are subsets of Artificial Intelligence (AI). Data science mainly overlays the growing field of AI, as data scientists use their abilities to clean, prepare, and extract data to run several AI requests. While machine learning allows supervised, unsupervised, and reinforced learning, deep learning helps in making datasets study and learn from existing information.

Example- Facial recognition feature in photos, doodling games like quick draw, and more. 

NLP, Cloud Computing and others 

Natural Language Processing, a branch of AI that uses the language used by human beings, processes it and learns to respond so. Several apps and voice-assisted devices like Alexa and Siri are previously using this extraordinary feature. As data scientists use large quantities of data stored on clouds, knowledge with cloud computing software like AWS, Azure, and Google cloud will be beneficial.

Database knowledge, management, and visualization 

A collection of information prearranged to easily admission, manage, and update the data is called a database. Data scientists must have strong database knowledge and use its different types to their advantage.

Example- SQL, distributed database, cloud database, and many more.

Domain knowledge  

Domain knowledge about the domain in which data is to be examined and forecasts will be made is important. One can bind the true power and completest possible of an algorithm and data only by having specific domain language. Instead of waiting to analyze the data, the goals itself can be shaped towards criminal results with the help of domain knowledge.  

Non-technical Skills 

Communication skills 

As I explained above, once the rare data is treated, it needs to be presented reasonably. This does not limit the job to just visually intelligible information but also the ability to communicate the visions of these visual pictures. The data scientist should be excellent at interactive the results to the marketing team, sales team, business leaders, and other clients. 

Team player 

This is related to the preceding point. Along with real communication skills, data scientists need to be good team players, helpful feedback, and other inputs from business teams. They should also be able to efficiently communicate their requirements to the data engineers, data analysts, and other members of the team. Group with their team members can harvest faster results and best outputs. 

Business insight  

Data scientist ultimately boils down to improving/growing the business; they need to be able to think from a business viewpoint while exactness their data structures. They should have in-depth knowledge of the industry of their business, the existing business problems of their company, and predicting potential business problems and their solutions. 

Critical thinking 

Data scientists need to align these results with the business. They need to be able to frame suitable questions and steps/solutions to solve business problems. This impartial ability to examine data and addressing the problem from multiple viewpoints is crucial in a data scientist. 

Intelligent curiosity  

According to some important survey, data scientists spend 80% of their time learning and making data. For this, they must always be a step fast and catch up with the newest trends. Constant up skilling and a inquisitiveness to learn new ways to solve existing problems earlier can get data scientists a long way in their careers. 

Taking data-driven choices 

Data science is indubitably one of the leading businesses today. Whether you are from a technical field or a non-technical background, there are numerous ways to build up the skill to become a data scientist. From online courses to gumboot camps, one should always be a step ahead in this modest field to build up their data work collections. Moreover, reading up on the latest technologies with NearLearn and regular research with new trends is the way forward for candidates.  

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Deep Learning is part of machine learning and a subset of artificial intelligence. Artificial intelligence allows machines to copy human behavior whereas machine learning uses its algorithms to achieve artificial intelligence. Deep Learning is a type of machine learning with structure same as the human brain. In Deep Learning, we can call it an Artificial Neural Network.

A driverless car is one example of deep learning. It enables car in a way that it can recognize stop sign, pedestrian and traffic signals. Deep Learning is achieving things that were not possible previously and of course, that independence comes only because of high-value data.

How does deep learning works?

In deep learning neural network, there are input layer, hidden layer, and output layer. The main difference between simple neural network and deep learning neural network is that- in a simple neural network, there is only one hidden layer but in deep learning neural network there are more than one (deep) hidden layers.

The input layer receives nonlinear transformations as input data. Mathematical computations are performed on input data in the hidden layer. The hidden layer repeatedly performs computation on input data until it sends accurate data to output. For Deep Learning, one of the challenge is deciding no. of hidden layers and neurons for each of them.

Deep Learning Applications

Customer support: nowadays most people contacting customer support for any business and that conversation seems real where from another side there is bot assisting them. Deep learning is being used in many businesses to improve customer satisfaction and it results in improving businesses.

Healthcare: In healthcare, deep learning models can detect cancer cells and give detailed results.

Self-driving cars: It is an example of science changing into reality. This vehicle can detect signals, speed breakers, and even pedestrian paths too. Tesla and Nissan are the companies that are trying to work on these self-driving cars.

Text generation: with the use of deep learning models, Machines can read and analyze grammar and style of text and then can automatically create an entirely matching new text with proper spelling and style of the original text.

Military and Aerospace: Deep learning models can detect satellite objects and it can also detect unsafe and safe zones.

Industrial automation: one of the useful applications of deep learning is it is used to detect if the worker is getting too close to the machine. This application is used in lots of factories and improving the safety of workers. Adding color to Photos: previously, applying color to photos was a manual and longtime task but now, deep learning models can be used to add your favorite color to black and white photos and videos in very less time.

Limitations of Deep Learning

Deep learning models can learn through observations and this becomes the biggest disadvantage for them. They can only know what was there in data which was applied to get trained. With a small amount of data, the models will not learn the inaccurate way.

In Deep learning, if a model learns on data which includes biases, the model will replicate those biases in its output predictions also.

Deep learning models require hardware such as processing unit and high-performing GPUs to improve productivity and to decrease time consumption. This hardware is high cost and needs lots of energy for working.

One biggest limitation of Deep learning is the requirement of a huge database. If you want to produce the most accurate output and prediction you will require more amount of data in the input. Deep learning models cannot handle multiple tasks at the same time. They can generate accurate solution but for one particular problem only. To solve a similar problem again, they need retraining.

Career opportunities in Deep Learning

Deep learning is a popular term in the computer field. A career in Artificial intelligence, machine learning, and deep learning are booming nowadays. Enormous job opportunities are getting created every day for these fields but demand for deep learning is high for sure, as per the reports.

For deep learning professionals, the average salary range in the US is approximately $70,000 to $75,000/year.

There is a high demand for careers in deep learning and AI as many jobs are available for this field with a good salary. As a result, most of the professionals are trying to get trained in deep learning. NearLearn provides Deep Learning training in Bangalore at affordable prices. We also provide Machine learning certification and Python Training in Bangalore. Online training and corporate programs are also available for the same.

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Online Learning is an educational medium through online mode from where one can learn and get expert by a lot of knowledge via internet. They don’t need to visit classroom so they can learn whatever they want from the comfort from their own home.

It’s a kind of distance learning from highly recognized colleges, institutes, universities. By, online enrolling themselves they can pay, learn and get certified. For advance multiple courses, duration for them maybe short term or long term. Anyone having knowledge to share or a professor can easily create online course option for a large of students living all over the world, as a medium of earning income.

Nowadays, students more prefer online courses or training, as it’s convenient and reasonable for them. By learning through online mode travelling time and cost both saves and they easily get knowledge of the course from their own place. Some people just take some classes just to pass their time or in their free time they opt online training option.

The Real Benefits of taking Online Training:

• Comfort of learning from your own home

Some days body need rest, and you don’t want to step out from your relaxation place, your house. If one is pursuing online training, its easy to attend course without getting dressed or steping your foot outside your home. A big thanks to mobile technologies through which from anywhere anyone can continue. Some people can’t concentrate at their concept without proper place and absolute silence. And some needs music or surround themselves with such activity to stay motivated.

• Bonus Point for Resume

To improve your resume, those courses will help you to add those specific skills for your ideal job search. This gives an plus focus point with the skills you learnt throughout the course and to find related jobs in specific field.

Nowadays, there are varities of IT jobs, just need to grab the knowledge and take the opportunity.

• Reduced Pressure

Some students can’t handle pressure for their studies, whether its higher education or primary classes education.

Online learning offers a completely different environment for them to learn and apply. Students can still interact with each other and with their instructors also, so they don’t need to face any kind of competitive atmosphere everyday. They can easily learn in a safe atmosphere which help them in learning instead of competition.

• Convenience and Flexibilty

Over scheduling causes pressure, tension, depression kinds of things to a person. Because he is willing to learn but he don’t have managed their routine time and efforts in order to get some knowledge.

Online Learning classes are designed to help you in learning new skills, so you give your proper attention towards it. As physical course require you to stay in classroom for a fixed time period, whereas, in online training you can schedule your class as per your convenience, distance learning gives you more flexibility.

• Study with Thousands of Students Worldwide

Our study partner matters just as much as our instructors. We also learn from our fellow students as well as teachers in an online environment. We might share notes, figure out ways to work together in the future. Internet doesn’t separate people by city, country, state.

Our fellow students can be from different-different regions having different cultures that one can understand and follow. “The more you broaden your horizons, the more powerful the learning experience becomes.

• Opportunities to Scale

Business model leads to future success and ensures that you won’t run out of ideas. One can continue to offer the same courses that built your success, but you can also build upon them to create new opportunities and meet new people.

As a learner, you can continue and grow scale of your education through multiple online courses. At a time, may people take several courses. It’s upto you to mangae your time and desire according to your wants.

• Work While you Study

Most of the people can’t afford to take six months off work to learn a new skill.

Through online training medium, one can study while working. After you get home from the office, take a book for an hour or two, and can repeat the process for everyday. Those new skills help to add experience and knowledge in your career life and lead to growth for reaching at a point willing to move.

• Expand your Network

Above, we mentioned that one of the advantages of online learning is in the ability to work with students all over the world. These benefits are not stopping even after the course ends.

You maybe meet them as your business partners or collaborator. Or you can connect with fellow students on Likedin and other social media platforms. One can take another’s skills when you learn of new job opportunities, and continue to support each other in career.

There’s nothing wrong with Traditional Education.

However, Online Education opens up a new world of Education. Infact, one can do both sidebyside. Whatever the situation is Online Education can’t exist in physical classes. The Internet has created an environment that encourages learning, so it’s time to take those advantages. Online platforms allow everyone to become a teacher.

It’s time to take advantages of the educational opportunities available for you- Build your Future- Career- Online Training- NearLearn.

We NearLearn, offers Classroom as well as Online Training for our most of the courses, such as, Machine Learning with Python Training in Bangalore, Artificial Intelligence, Data Science, Reactjs Training in Bangalore. Feel free to interact with us- visit www.nearlearn.com or info@nearlearn.com.

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