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10 free image training data resources online

Not too long ago, we would have chuckled at the idea of a vehicle driving itself while the driver catches those extra few minutes of precious sleep. But this is 2019, where self-driving cars aren’t just in the prototyping stage but being actively rolled out to the public. And, remember those days when we were marveled by a device recognizing it’s users face? Well, that’s a norm in today’s world. With rapid developments, AI & ML technologies are increasingly penetrating our lives. However, developments of such systems are no easy task. It requires hours of coding and thousands, if not millions, of data to train & test these systems. While there are a plethora of training data service providers that can help you with your requirements, it’s not always feasible. So, how can you get free image datasets?

There are various areas online where you can discover Image Datasets. A lot of research bunches likewise share the labeled image datasets they have gathered with the remainder of the network to further machine learning examine in a specific course.

In this post, you’ll find top 9 free image training data repositories and links to portals you’re ready to visit and locate the ideal image dataset that is pertinent to your projects. Enjoy!

Labelme

Free image training dataset at labelme | Bridged.co

This site contains a huge dataset of annotated images.

Downloading them isn’t simple, however. There are two different ways you can download the dataset:

1. Downloading all the images via the LabelMe Matlab toolbox. The toolbox will enable you to tweak the part of the database that you need to download.

2. Utilizing the images online using the LabelMe Matlab toolbox. This choice is less favored as it will be slower, yet it will enable you to investigate the dataset before downloading it. When you have introduced the database, you can utilize the LabelMe Matlab toolbox to peruse the annotation records and query the images to extricate explicit items.

ImageNet

Free image training dataset at ImageNet | Bridged.co

The image dataset for new algorithms is composed by the WordNet hierarchy, in which every hub of the hierarchy is portrayed by hundreds and thousands of images.

Downloading datasets isn’t simple, however. You’ll need to enroll on the website, hover over the ‘Download’ menu dropdown, and select ‘Original Images.’ Given you’re utilizing the datasets for educational/personal use, you can submit a request for access to download the original/raw images.

MS COCO

Free image training dataset at mscoco | Bridged.co

Common objects in context (COCO) is a huge scale object detection, division, and subtitling dataset.

The dataset — as the name recommends — contains a wide assortment of regular articles we come across in our everyday lives, making it perfect for preparing different Machine Learning models.

COIL100

Free image training dataset at coil100 | Bridged.co

The Columbia University Image Library dataset highlights 100 distinct objects — going from toys, individual consideration things, tablets — imaged at each point in a 360° turn.

The site doesn’t expect you to enroll or leave any subtleties to download the dataset, making it a simple procedure.

Google’s Open Images

Free image training data at Google | Bridged.co

This dataset contains an accumulation of ~9 million images that have been annotated with image-level labels and object bounding boxes.

The training set of V4 contains 14.6M bounding boxes for 600 object classes on 1.74M images, making it the biggest dataset to exist with object location annotations.

Fortunately, you won’t have to enroll on the website or leave any personal subtleties to get the dataset allowing you to download the dataset from the site without any obstructions.

On the off chance that you haven’t heard till now, Google recently released a new dataset search tool that could prove to be useful if you have explicit prerequisites.

Labelled Faces in the Wild

Free image training dataset at Labeled Faces in The Wild | BridgedCo

This portal contains 13,000 labeled images of human faces that you can readily use in any of your Machine Learning projects, including facial recognition.

You won’t have to stress over enrolling or leaving your subtleties to get to the dataset either, making it too simple to download the records you need, and begin training your ML models!

Stanford Dogs Dataset

Image training data at Stanford Dogs Dataset | Bridged.co

It contains 20,580 images and 120 distinctive dog breed categories.

Made utilizing images from ImageNet, this dataset from Stanford contains images of 120 breeds of dogs from around the globe. This dataset has been fabricated utilizing images and annotation from ImageNet for the undertaking of fine-grained picture order.

To download the dataset, you can visit their website. You won’t have to enroll or leave any subtleties to download anything, basically click and go!

Indoor Scene Recognition

Free image training data at indoor scene recognition | Bridged.co

As the name recommends, this dataset containing 15620 images involving different indoor scenes which fall under 67 indoor classes to help train your models.

The particular classifications these images fall under incorporated stores, homes, open spaces, spots of relaxation, and working spots — which means you’ll have a differing blend of images used in your projects!

Visit the page to download this dataset from the site.

LSUN

This dataset is useful for scene understanding with auxiliary assignment ventures (room design estimation, saliency forecast, and so forth.).

The immense dataset, containing pictures from different rooms (as portrayed above), can be downloaded by visiting the site and running the content gave, found here.

You can discover more data about the dataset by looking down to the ‘scene characterization’ header and clicking ‘README’ to get to the documentation and demo code.

Well, here are the top 10 repositories to help you get image training data to help in the development of your AI & ML models. However, given the public nature of these datasets, they may not always help your systems generate the correct output.

Since every system requires it’s own set of data that are close to ground realities to formulate the most optimal results, it is always better to build training datasets that cater to your exact requirements and can help your AI/ML systems to function as expected.

The need for training data in ai and ml models

Not very long ago, sometime towards the end of the first decade of the 21st century, internet users everywhere around the world began seeing fidelity tests while logging onto websites. You were shown an image of a text, with one word or usually two, and you had to type the words correctly to be able to proceed further. This was their way of identifying that you were, in fact, human, and not a line of code trying to worm its way through to extract sensitive information from said website. While it was true, this wasn’t the whole story.

Turns out, only one of the two Captcha words shown to you were part of the test, and the other was an image of a word taken from an as yet non-transcribed book. And you, along with millions of unsuspecting users worldwide, contributed to the digitization of the entire Google Books archive by 2011. Another use case of such an endeavor was to train AI in Optical Character Recognition (OCR), the result of which is today’s Google Lens, besides other products.

Do you really need millions of users to build an AI? How exactly was all this transcribed data used to make a machine understand paragraphs, lines, and individual words? And what about companies that are not as big as Google – can they dream of building their own smart bot? This article will answer all these questions by explaining the role of datasets in artificial intelligence and machine learning.

ML and AI – smart tools to build smarter computers

In our efforts to make computers intelligent – teach them to find answers to problems without being explicitly programmed for every single need – we had to learn new computational techniques. They were already well endowed with multiple superhuman abilities: computers were superior calculators, so we taught them how to do math; we taught them language, and they were able to spell and even say “dog”; they were huge reservoirs of memory, hence we used them to store gigabytes of documents, pictures, and video; we created GPUs and they let us manipulate visual graphics in games and movies. What we wanted now was for the computer to help us spot a dog in a picture full of animals, go through its memory to identify and label the particular breed among thousands of possibilities, and finally morph the dog to give it the head of a lion that I captured on my last safari. This isn’t an exaggerated reality – FaceApp today shows you an older version of yourself by going through more or less the same steps.

For this, we needed to develop better programs that would let them learn how to find answers and not just be glorified calculators – the beginning of artificial intelligence. This need gave rise to several models in Machine Learning, which can be understood as tools that enhanced computers into thinking systems (loosely).

Machine Learning Models

Machine Learning is a field which explores the development of algorithms that can learn from data and then use that learning to predict outcomes. There are primarily three categories that ML models are divided into:

Supervised Learning

These algorithms are provided data as example inputs and desired outputs. The goal is to generate a function that maps the inputs to outputs with the most optimal settings that result in the highest accuracy.

Unsupervised Learning

There are no desired outputs. The model is programmed to identify its own structure in the given input data.

Reinforcement Learning

The algorithm is given a goal or target condition to meet and it is left to its devices to learn by trial and error. It uses past results to inform itself about both optimal and detrimental paths and charts the best path to the desired endgame result.

In each of these philosophies, the algorithm is designed for a generic learning process and exposed to data or a problem. In essence, the written program only teaches a wholesome approach to the problem and the algorithm learns the best way to solve it.

Based on the kind of problem-solving approach, we have the following major machine learning models being used today:

  • Regression
    These are statistical models applicable to numeric data to find out a relationship between the given input and desired output. They fall under supervised machine learning. The model tries to find coefficients that best fit the relationship between the two varying conditions. Success is defined by having as little noise and redundancy in the output as possible.

    Examples: Linear regression, polynomial regression, etc.
  • Classification
    These models predict or explain one outcome among a few possible class values. They are another type of supervised ML model. Essentially, they classify the given data as belonging to one type or ending up as one output.

    Examples: Logistic regression, decision trees, random forests, etc.
  • Decision Trees and Random Forests
    A decision tree is based on numerous binary nodes with a Yes/No decision marker at each. Random forests are made of decision trees, where accurate outputs are obtained by processing multiple decision trees and results combined.
  • Naïve Bayes Classifiers
    These are a family of probabilistic classifiers that use Bayes’ theorem in the decision rule. The input features are assumed to be independent, hence the name naïve. The model is highly scalable and competitive when compared to advanced models.
  • Clustering
    Clustering models are a part of unsupervised machine learning. They are not given any desired output but identify clusters or groups based on shared characteristics. Usually, the output is verified using visualizations.

    Examples: K-means, DBSCAN, mean shift clustering, etc.
  • Dimensionality Reduction
    In these models, the algorithm identifies the least important data from the given set. Based on the required output criteria, some information is labeled redundant or unimportant for the desired analysis. For huge datasets, this is an invaluable ability to have a manageable analysis size.

    Examples: Principal component analysis, t-stochastic neighbor embedding, etc.
  • Neural Networks and Deep Learning
    One of the most widely used models in AI and ML today, neural networks are designed to capture numerous patterns in the input dataset. This is achieved by imitating the neural structure of the human brain, with each node representing a neuron. Every node is given activation functions with weights that determine its interaction with its neighbors and adjusted with each calculation. The model has an input layer, hidden layers with neurons, and an output layer. It is called deep learning when many hidden layers are encapsulating a wide variety of architectures that can be implemented. ML using deep neural networks requires a lot of data and high computational power. The results are without a doubt the most accurate, and they have been very successful in processing images, language, audio, and videos.

There is no single ML model that offers solutions to all AI requirements. Each problem has its own distinct challenges, and knowledge of the workings behind each model is mandatory to be able to use them efficiently. For example, regression models are best suited for forecasting data and for risk assessment. Clustering modes in handwriting recognition and image recognition, decision trees to understand patterns and identify disease trends, naïve Bayes classifier for sentiment analysis, ranking websites and documents, deep neural networks models in computer vision, natural language processing, and financial markets, etc. are more such use cases.

The need for training data in ML models

Any machine learning model that we choose needs data to train its algorithm on. Without training data, all the algorithm understands is how to approach the given problem, and without proper calibration, so to speak, the results won’t be accurate enough. Before training, the model is just a theorist, without the fine-tuning to its settings necessary to start working as a usable tool.

While using datasets to teach the model, training data needs to be of a large size and high quality. All of AI’s learning happens only through this data. So it makes sense to have as big a dataset as is required to include variety, subtlety, and nuance that makes the model viable for practical use. Simple models designed to solve straight-forward problems might not require a humongous dataset, but most deep learning algorithms have their architecture coded to facilitate a deep simulation of real-world features.

The other major factor to consider while building or using training data is the quality of labeling or annotation. If you’re trying to teach a bot to speak the human language or write in it, it’s not just enough to have millions of lines of dialogue or script. What really makes the difference is readability, accurate meaning, effective use of language, recall, etc. Similarly, if you are building a system to identify emotion from facial images, the training data needs to have high accuracy in labeling corners of eyes and eyebrows, edges of the mouth, the tip of the nose and textures for facial muscles. High-quality training data also makes it faster to train your model accurately. Required volumes can be significantly reduced, saving time, effort (more on this shortly) and money.

Datasets are also used to test the results of training. Model predictions are compared to testing data values to determine the accuracy achieved until then. Datasets are quite central to building AI – your model is only as good as the quality of your training data.

How to build datasets?

With heavy requirements in quantity and quality, it is clear that getting your hands on reliable datasets is not an easy task. You need bespoke datasets that match your exact requirements. The best training data is tailored for the complexity of the ask as opposed to being the best-fit choice from a list of options. Being able to build a completely adaptive and curated dataset is invaluable for businesses developing artificial intelligence.

On the contrary, having a repository of several generic datasets is more beneficial for a business selling training data. There are also plenty of open-source datasets available online for different categories of training data. MNIST, ImageNet, CIFAR provide images. For text datasets, one can use WordNet, WikiText, Yelp Open Dataset, etc. Datasets for facial images, videos, sentiment analysis, graphs and networks, speech, music, and even government stats are all easily found on the web.

Another option to build datasets is to scrape websites. For example, one can take customer reviews off e-commerce websites to train classification models for sentiment analysis use cases. Images can be downloaded en masse as well. Such data needs further processing before it can be used to train ML models. You will have to clean this data to remove duplicates, or to identify unrelated or poor-quality data.

Irrespective of the method of procurement, a vigilant developer is always likely to place their bets on something personalized for their product that can address specific needs. The most ideal solutions are those that are painstakingly built from scratch with high levels of precision and accuracy with the ability to scale. The last bit cannot be underestimated – AI and ML have an equally important volume side to their success conditions.

Coming back to Google, what are they doing lately with their ingenious crowd-sourcing model? We don’t see a lot of captcha text anymore. As fidelity tests, web users are now annotating images to identify patterns and symbols. All the traffic lights, trucks, buses and road crossings that you mark today are innocuously building training data to develop their latest tech for self-driving cars. The question is, what’s next for AI and how can we leverage human effort that is central to realizing machine intelligence through training datasets?

5 common misconceptions about AI

Ever wondered what your life would be without those perky machines lying around which sometimes/most times replaced a significant part of your daily routine? In Terminology fancied by Scientists, we call them AI (Artificial Intelligence,) and in plain layman or lazy man terms that is us, we fancy calling them machines and bots.

Let’s define the exact meaning of AI in terms of science because I hate disappointing aspiring scientists out there who don’t take puns lightly. For those that do, welcome to the fraternity of loose and lost minds. Let’s get down to business, shall we?

Definition: Artificial Intelligence or machine intelligence, is intelligence demonstrated by machines in contrast to the natural intelligence displayed by humans. Colloquially, the term "artificial intelligence" is often used to describe machines (or computers) that mimic "cognitive" functions that humans associate with the human mind such as "learning" and "problem-solving.”

Isn’t it evident I copied the above definition from Wikipedia? And did your natural intelligence decipher the meaning of the definition stated above?

Let me introduce you to the lazy man definition of Artificial Intelligence. Like all engineering scholars, I will take the absolute pleasure of dismantling the words and assembling it together again.

Artificial – Non-Human, something that can’t breathe air or respond to a feeling. 

Intelligence – the ability to display intellect, sound reasoning, judgment, and a ready wit.

Put the two words together and voila! Artificially intelligent machines are capable of displaying or mimicking human intellect, sound reasoning, and judgment towards it's surrounding.

Now that we got the definition of AI out of the way, look around you, what do you see? What’s in your hands? Do you not spot a single electronic device or bots?

Things or machines work a lot differently in this era. You must be awestruck of the skyrocketing shiny monuments. The big bird moving 33,000ft above your head carrying humans from one country to another, hospitals treating the diseased and the ill with technology your mind can’t fathom.

Fast cars, microwave and yes, we no longer communicate using crows or pigeons we have cell phones!

Don’t be surprised if I reveal that these are the necessity and an extension to our lives. And no, we cannot live without them anymore.

Our purpose of life has changed drastically, growing crops and putting food on the table isn’t what give us lines on the forehead. We built replacement models that take care of that too. We are living in a fast lane where technology, eventually, will slingshot us to the moon or another planet.

With such a drastic rise in AI and the current trend where all companies want a piece of it, there are some misconceptions about AI as well. With this blog, I try to debunk the misconceptions highlighting both the positive and negative aspects of artificial intelligence.

“If these machines are handling even the simplest of tasks, what are people going to do? Is it the destruction of jobs?”

Fret not. If there is technological advancement, there are always career opportunities as it is the human mind that does the ‘thinking.’ You are the master of your creation.

In fact, in 2020 there will be 2.3 million new jobs available thanks to AI, which results in less muscle power and more brainpower.

“Can Artificial Intelligence solve any/all problems?“

This question is debatable, while AI is designed to assist and make our jobs easier, it cannot save a human being from rubbing off cancers and illness.

Human intelligence hasn’t discovered a way to program the bots to predict or diagnose illness proactively. One must remember, bots act on what is fed/programmed by humans.

“Is AI infallible?“

If you thought it was, then I have slightly bad news. Humans are in a common misconception assuming the machines are no less than perfection and display little to no mistake. The non-sentient systems are trained by us, data selected and curated by us, and human tendency is to make mistakes and learn from them.

Artificial Intelligence is just as good as the training data used, which is created by humans. Any mistake with the training data will reflect on the performance of the system and the technology will be compromised. Ensuring you use a high-quality training dataset is critical to the success of the AI system.

Speaking of data being compromised, during the 2016 presidential election campaign, we witnessed the information of US citizens being evaluated by gaining access to their social media accounts. To proactively block their social media feeds with ads that will prove to be of interest. Therefore, stealing away the votes from the opposition.

We call this “data/information manipulation.” Sadly, the downside of Artificial Intelligence.

“AI must be expensive.”

Well, implementing a fully automated system doesn’t come easy and doesn’t come cheap. But depending on the needs and goals of the organization, it may be entirely possible to adopt AI and get the desired results without breaking your treasure chest.

The key is for each business to figure out what they want and apply AI as needed, for their unique goals and company scale. If businesses can workout their scalability and incorporate the right Artificial intelligence, it can be economical in the long run.

“Will Artificial Intelligence be the end of humanity?”

We are a work in progress, standing at the foyer of technological advancements with a long way to go. But, much like the misconception about robots replacing humans in the workforce, the question is more of smoke in the mirror.

The AI in its current level is not fully capable of self-conscious and decision making. Don’t let Star Trek, Iron Man and Terminator movies fool you into believing bots will lose their nuts (literally and hypothetically) and foreshadow the destruction of humanity. On the flip side, it is the natural disasters the bots are being designed to protect us from.

Oh, look what’s in every body’s hand, it’s what we call a cell phone. A device primarily designed to communicate with people that are at a greater distance.

Communication takes place using microwaves, very different from sand waves. Look closely and you’ll see people doing weird things using their fingers on the cell phone and a weird thing hanging from their ears going through to the same device. Yes, these devices are their partners for life.

Here we are, say Konnichiwa to the lady, don’t touch her! She’s just a hologram.

Welcome to the National Museum of Emerging Science and Innovation simply known as the Miraikan (future museum) where obsessiveness over technology has led us to build a museum for itself.

There’s Asimo, the Honda robot and, what you’re looking at isn’t another piece of asteroid that struck earth years ago, it is Geo-Cosmos. A high-resolution globe displaying near real-time events of global weather patterns, ocean temperatures, and vegetation covering across geographic locations.

You must be contemplating why has mankind reached such level of advancement? Let’s go back to the last question “Will AI be the end of humanity?”

The seismometer, a device that responds and records the ground motions, earthquake, and volcanic eruptions. There are a lot of countries that have lost far too many lives to even comprehend the tragic events of active earthquakes.

This device is a way to predict and bring citizens of Japan to safe grounds. Artificial Intelligence will not be the end of humanity, it can, in fact, be the opposite and could be an answer to humanity’s biggest natural calamities and disasters.

The human mind is something to behold, from its complex neural nerves in the brain to the nerves connecting to every part of the body to achieve motor functions. To replicate or clone it using artificial chips and wires is nearly impossible in the current era but the determination we hold and our adamant nature drives us to dream, the dream of one day successfully cloning the human consciousness into nuts and bolts of a bot.

One day to look at the stars and send bots for space exploration. To look for a suitable second home in an event of space disasters that humans have no control over. And, why send bots into deep space and not humans to add a feather to the hat of achievement?

Simply because we breathe, we starve, and our very own nervous system advertently detects the brutal nature of space above the earth. In this case, Artificial Intelligence and robots are in fact helping humans explore the possibilities of life in outer space. Which is against the misconception that AI will be the end of humanity.

So, there we have it, all the major misconceptions about artificial intelligence and what the reality is. End of the day, it all comes down to how we incorporate artificial intelligence and what we use it for.

If used in the right way, there will be a revolution in the way humans work. Which makes it important for all of us to work on educating people about artificial intelligence and using it to make the world a better place.

Understanding the difference between AI, ML & NLP models

Technology has revolutionized our lives and is constantly changing and progressing. The most flourishing technologies include Artificial Intelligence, Machine Learning, Natural Language Processing, and Deep Learning. These are the most trending technologies growing at a fast pace and are today’s leading-edge technologies.

These terms are generally used together in some contexts but do not mean the same and are related to each other in some or the other way. ML is one of the leading areas of AI which allows computers to learn by themselves and NLP is a branch of AI.

What is Artificial Intelligence?

Artificial refers to something not real and Intelligence stands for the ability of understanding, thinking, creating and logically figuring out things. These two terms together can be used to define something which is not real yet intelligent.

AI is a field of computer science that emphasizes on making intelligent machines to perform tasks commonly associated with intelligent beings. It basically deals with intelligence exhibited by software and machines.

While we have only recently begun making meaningful strides in AI, its application has encompassed a wide spread of areas and impressive use-cases. AI finds application in very many fields, from assisting cameras, recognizing landscapes, and enhancing picture quality to use-cases as diverse and distinct as self-driving cars, autonomous robotics, virtual reality, surveillance, finance, and health industries.

History of AI

The first work towards AI was carried out in 1943 with the evolution of Artificial Neurons. In 1950, Turing test was conducted by Alan Turing that can check the machine’s ability to exhibit intelligence.

The first chatbot was developed in 1966 and was named ELIZA followed by the development of the first smart robot, WABOT-1. The first AI vacuum cleaner, ROOMBA was introduced in the year 2002. Finally, AI entered the world of business with companies like Facebook and Twitter using it.

Google’s Android app “Google Now”, launched in the year 2012 was again an AI application. The most recent wonder of AI is “the Project Debater” from IBM. AI has currently reached a remarkable position

The areas of application of AI include

  • Chat-bots – An ever-present agent ready to listen to your needs complaints and thoughts and respond appropriately and automatically in a timely fashion is an asset that finds application in many places — virtual agents, friendly therapists, automated agents for companies, and more.
  • Self-Driving Cars: Computer Vision is the fundamental technology behind developing autonomous vehicles. Most leading car manufacturers in the world are reaping the benefits of investing in artificial intelligence for developing on-road versions of hands-free technology.
  • Computer Vision: Computer Vision is the process of computer systems and robots responding to visual inputs — most commonly images and videos.
  • Facial Recognition: AI helps you detect faces, identify faces by name, understand emotion, recognize complexion and that’s not the end of it.

What is Machine Learning?

One of the major applications of Artificial Intelligence is machine learning. ML is not a sub-domain of AI but can be generally termed as a sub-field of AI. The field of machine learning is concerned with the question of how to construct computer programs that automatically improve with experience.

Implementing an ML model requires a lot of data known as training data which is fed into the model and based on this data, the machine learns to perform several tasks. This data could be anything such as text, images, audio, etc…

 Machine learning draws on concepts and results from many fields, including statistics, artificial intelligence, philosophy, information theory, biology, cognitive science, computational complexity and control theory. ML itself is a self-learning algorithm. The different algorithms of ML include Decision Trees, Neural Networks, SEO, Candidate Elimination, Find-S, etc.

History of Machine Learning

The roots of ML lie way back in the 17th century with the introduction of Mechanical Adder and Mechanical System for Statistical Calculations. Turing Test conducted in 1950 was again a turning point in the field of ML.

The most important feature of ML is “Self-Learning”. The first computer learning program was written by Arthur Samuel for the game of checkers followed by the designing of perceptron (neural network). “The Nearest Neighbor” algorithm was written for pattern recognition.

Finally, the introduction of adaptive learning was introduced in the early 2000s which is currently progressing rapidly with Deep Learning is one of its best examples.

Different types of machine learning approaches are:

Supervised Learning uses training data which is correctly labeled to teach relationships between given input variables and the preferred output.

Unsupervised Learning doesn’t have a training data set but can be used to detect repetitive patterns and styles.

Reinforcement Learning encourages trial-and-error learning by rewarding and punishing respectively for preferred and undesired results.

ML has several applications in various fields such as

  • Customer Service: ML is revolutionizing customer service, catering to customers by providing tailored individual resolutions as well as enhancing the human service agent capability through profiling and suggesting proven solutions. 
  • HealthCare: The use of different sensors and devices use data to access a patient’s health status in real-time.
  • Financial Services: To get the key insights into financial data and to prevent financial frauds.
  • Sales and Marketing: This majorly includes digital marketing, which is currently an emerging field, uses several machine learning algorithms to enhance the purchases and to enhance the ideal buyer journey.

What is Natural Language Processing?

Natural Language Processing is an AI method of communicating with an intelligent system using a natural language.

Natural Language Processing (NLP) and its variants Natural Language Understanding (NLU) and Natural Language Generation (NLG) are processes which teach human language to computers. They can then use their understanding of our language to interact with us without the need for a machine language intermediary.

History of NLP

NLP was introduced mainly for machine translation. In the early 1950s attempts were made to automate language translation. The growth of NLP started during the early ’90s which involved the direct application of statistical methods to NLP itself. In 2006, more advancement took place with the launch of IBM’s Watson, an AI system which is capable of answering questions posed in natural language. The invention of Siri’s speech recognition in the field of NLP’s research and development is booming.

Few Applications of NLP include

  • Sentiment Analysis – Majorly helps in monitoring Social Media
  • Speech Recognition – The ability of a computer to listen to a human voice, analyze and respond.
  • Text Classification – Text classification is used to assign tags to text according to the content.
  • Grammar Correction – Used by software like MS-Word for spell-checking.

What is Deep Learning?

The term “Deep Learning” was first coined in 2006. Deep Learning is a field of machine learning where algorithms are motivated by artificial neural networks (ANN). It is an AI function that acts lie a human brain for processing large data-sets. A different set of patterns are created which are used for decision making.

The motive of introducing Deep Learning is to move Machine Learning closer to its main aim. Cat Experiment conducted in 2012 figured out the difficulties of Unsupervised Learning. Deep learning uses “Supervised Learning” where a neural network is trained using “Unsupervised Learning”.

Taking inspiration from the latest research in human cognition and functioning of the brain, neural network algorithms were developed which used several ‘nodes’ that process information like how neurons do. These networks have multiple layers of nodes (deep nodes and surface nodes) for different complexities, hence the term deep learning. The different activation functions used in Deep Learning include linear, sigmoid, tanh, etc.…

History of Deep Learning

The history of Deep Learning includes the introduction of “The Back-Propagation” algorithm, which was introduced in 1974, used for enhancing prediction accuracy in ML.  Recurrent Neural Network was introduced in 1986 which takes a series of inputs with no predefined limit, followed by the introduction of Bidirectional Recurrent Neural Network in 1997.  In 2009 Salakhutdinov & Hinton introduced Deep Boltzmann Machines. In the year 2012, Geoffrey Hinton introduced Dropout, an efficient way of training neural networks

Applications of Deep Learning are

  • Text and Character generation – Natural Language Generation.
  • Automatic Machine Translation – Automatic translation of text and images.
  • Facial Recognition: Computer Vision helps you detect faces, identify faces by name, understand emotion, recognize complexion and that’s not the end of it.
  • Robotics: Deep learning has also been found to be effective at handling multi-modal data generated in robotic sensing applications.

Key Differences between AI, ML, and NLP

Artificial intelligence (AI) is closely related to making machines intelligent and make them perform human tasks. Any object turning smart for example, washing machine, cars, refrigerator, television becomes an artificially intelligent object. Machine Learning and Artificial Intelligence are the terms often used together but aren’t the same.

ML is an application of AI. Machine Learning is basically the ability of a system to learn by itself without being explicitly programmed. Deep Learning is a part of Machine Learning which is applied to larger data-sets and based on ANN (Artificial Neural Networks).

The main technology used in NLP (Natural Language Processing) which mainly focuses on teaching natural/human language to computers. NLP is again a part of AI and sometimes overlaps with ML to perform tasks. DL is the same as ML or an extended version of ML and both are fields of AI. NLP is a part of AI which overlaps with ML & DL.

7 Best Practices For Creating Training Data

The success of any AI or ML model is determined by the quality of the data used. A sophisticated model using a bad dataset would eventually fail to function the way it was expected to. With such models continually learning from the data provided, it’s necessary to build datasets that can help these model achieve their objectives. 

If you’re still unsure what training datasets are and why are they important to the success of your system. Here’s a quick read to get you up to speed with training data and building high-quality training sets.

While building a dataset sounds like a mundane and tedious task, it determines the success or failure of the model being built. To help you look past the dreadful hours spent on collecting, tagging, and labeling data, here are 7 things to follow when making training datasets. 

Avoid Target Leakage

When building training data for AI/ML models, it’s necessary to avoid any target leakage or data leakage. The issue of data leakage arises when the model is trained on parameters that might not be available during real-time prediction. Since the system already knows all possible outcomes, the output would be unrealistically accurate during training. 

Since data leakage causes the model to overrepresent its generalization error, making it useless for real-world applications. It’s necessary to remove any data from the training set that might not be known during real-time prediction to avoid target leakage issue. Furthermore, to mitigate the risks of data leakage, its necessary to involve business analysts and professionals with the domain expertise to be involved in all aspects of data science projects from problem specifications to data collection to deployment.  

Avoid Training-Serving Skew In Training Sets

Training-serving skew problem arises when the performance during training is different from the performance during serving. The most common reasons for this issue to arise are the discrepancy in how data is handled in training compared to serving, change in data between training and serving. And, the feedback loop between the model and algorithm. 

Exposing a model to training-serving skew can negatively impact the model’s performance, and the model might not function the way it’s expected to. One way to ensure you avoid training-serving skew is by measuring the skew. You can do this by, measuring the difference the performance on training data and the holdout data, the difference between holdout data and ‘next-day’ data, and the difference in performance between ‘next-day’ data and live data.

Make Information Explicit Where Needed 

As mentioned earlier, when working on data science projects, it’s important to involve business analysts and professionals of the domain to be part of the projects. Machine learning algorithms use a set of input data to create an output. This input data is called features, structured in the form of columns. 

Domain professionals can help in feature engineering, i.e., understanding those features that can make the model work. This helps in two primary ways, preparing proper input datasets compatible with the algorithm used and improving the accuracy of the model over time.

Avoid Biased Data When Building Training Sets

When building a training dataset for your AI/ML model, it’s important to make sure the training data is a representation of the entire universe of data. And, not biased towards a set of inputs. 

For example, an e-commerce website that ships products globally wants to use a chatbot to help its users shop better and faster. In such a scenario, if the training data is built only using exchanges/queries from customers of only one region. The system might throw exceptions when a customer from any other region interacts with the bot, given the nuances of language. So, to make sure the system is free of bias, the training data should contain exchanges of all kind of users the e-commerce shop caters to. 

Ensure Data Quality Is Maintained In Training Data 

As stated earlier, the quality of your training data is an essential factor in determining the accuracy and success of AI/ML models. A training dataset that’s filled with bias, and features not available in real-world scenarios would result in the model showing outputs that are far from ground-truths. 

We at Bridged.co have employed two ways of ensuring every dataset we deliver is of the highest quality – consensus approach, and sample review. These approaches make sure that the models trained using these datasets produce results as close to ground-realities as possible. 

Use Enough Training Data

It just isn’t enough to have good-quality data. The dataset you use to train your model must cover all possible variations of the features chosen to train the system. Failing to do so can cause the system function abnormally and produce inaccurate results.

The more features you use to train your model more the data that will be needed to sufficiently train the system. While there is no ‘one size fits all’ when deciding the size of training data. A good rule of thumb for classification models is to have at least 10 times the number of data as you have features, and for regression models, 50 times the number of data as you have features.

Set Up An In-house Workforce or Get A Fully-managed Training Data Solution Provider

Building a dataset is no overnight task. It’s a long tedious process that stretches on for weeks if not months. 

It would be ideal to have an ops team in-house whom you can train, monitor, and ensure the highest quality is maintained. However, it isn’t a scalable solution. 

You can also check out training data solution providers, such as ourselves, to help you with all your training data requirements. A fully-managed solution provider doesn’t just provide you with quality control but also ensure your requirements can be met even if at scale. 


It’s a no brainer that a good quality training dataset is fundamental to the success of your AI/ML systems. These important tips are bound to make sure the training data you build is of the highest quality and helps your system produce accurate results. 

Understanding training data and how to build high-quality training data for ai/ml models

We are living in one of the most exciting times, where faster processing power and new technological advancements in AI and ML are transcending the ways of the past. From conversational bots helping customers make purchases online to self-driving cars adding a new dimension of comfort and safety for commuters. While these technologies continue to grow and transform lives, what makes them so powerful is data.

Tons and tons of data.

Machine Learning systems, as the name suggests, are systems that are constantly learning from the data being consumed to produce accurate results.

If the right data is used, the system designed can find relations between entities, detect patterns, and make decisions. However, not all data or datasets used to build such models are treated equally.

Data for AI & ML models can be essentially classified into 5 categories: training dataset, testing dataset, validation dataset, holdout dataset, and cross-validation dataset. For the purpose of this article, we’ll only be looking at training dataset and cover the following topics.

What Is Training Data

Training data also called training dataset or training set or learning set, is foundational to the way AI & ML technologies work. Training data can be defined as the initial set of data used to help AI & ML models understand how to apply technologies such as neural networks to learn and produce accurate results.

Training sets are materials through which an AI or ML models learn how to process information and produce the desired output. Machine learning uses neural network algorithms that mimic the abilities of the human brain to take in diverse inputs and weigh them, to produce neural activations, in individual neurons. These provide a highly detailed model of how human thought process works.

Given the diverse types of systems available, training datasets are structured in a different way for different models. For conversational bots, the training set contains the raw text that gets classified and manipulated.

On the other hand, for convolution models using image processing and computer vision, the training set consists of a large volume of images. Given the complexity and sophistication of these models, it uses iterative training on each image to eventually understand the patterns, shapes, and subjects in a given image.

In a nutshell, training sets are labeled and organized data needed to train AI and ML models.

Why Are Training Datasets Important

When building training sets for AI & ML models, one needs huge amounts of relevant data to help these models make the most optimal decision. Machine learning allows computer systems to tackle very complex problems and deal with inherent variations of hundreds and thousands or millions of variables.

The success of such models is highly reliant on the quality of the training set used. A training set that accounts for all variations of the variables in the real world would result in developing more accurate models. Just like in the case of a company collecting survey data to know about their consumer, larger the sample size for the survey is, more accurate the conclusion will be.

If the training set isn’t large enough, the resultant system won’t be able to capture all variations of the input variables resulting in inaccurate conclusions.

While AI & ML models need huge amounts of data, they also need the right kind of data, as the system learns from this set of data. Having a sophisticated algorithm for AI & ML models isn’t enough when the data used to train these systems are bad or faulty. Training a system on a poor dataset or a dataset that contains wrong data, the system will end up learning wrong lessons, and generate wrong results. And eventually, not work the way it is expected to. On the contrary, a basic algorithm using a high-quality dataset will be able to produce accurate results and function as expected.

For example, in the case of a speech recognition system. The system can be made on a mathematical model to train the system on textbook English. However, this system is bound to show inaccurate results.

When we talk about language, there is a massive difference between textbook English and how people actually speak. To this add the factors – such as voice, dialects, age, gender – varying among speakers. This system would struggle to handle any cases or conversations that stray from the textbook English used to train it. For inputs having loose English or a different accent or use of slang, the system would fail to function for the purpose it was created.

Also, in a case, such a system is used to comprehend a text chat or email it would throw unexpected results. As a system trained in textbook English would fail to account for abbreviations and emojis used, which are commonly used among people in everyday conversations.

So, to build an accurate AI or ML model, it’s essential to build a comprehensive and high-quality training dataset. To help these systems learn the right lessons and formulate the right responses. While it’s a substantial task to generate such a high volume of data, it is necessary to do so.

How To Build A Training Dataset

Now, that we have understood why training data are integral to the success of an AI or ML model, it’s necessary to know how to build a training dataset.

The process of building a training dataset can be classified into 3 simple steps: data collection, data preprocessing, and data conversion. Let’s take a look at each of these steps and how it helps in building a high-quality training set.

Data Collection

The first step in making a training set is choosing the right number of features for a particular dataset. The data should be consistent and have the least amount of missing values. In case a feature has 25% to 30% of missing values, then this feature should not be considered to be part of the training set.

However, there might be instances when such features might be closely related to another feature. In such a case, it’s advisable to impute and handle the missing values correctly to achieve desired results. At the end of the data collection step, you should clearly know how to handle preprocessing data.

Data Preprocessing

Once the data has been collected, we enter the data preprocessing stage. In this step, we collect the right data from the complete data set and build a training set. The steps to be followed here are:

  • Organize and Format: If the data is scattered across multiple files or sheets, it’s necessary to compile all this data to form a single dataset. This includes finding the relation between these datasets and preprocess to form a dataset of required dimensions.
  • Data Cleaning: Once all the scattered data is compiled to a single dataset, it’s important to handle the missing values. And, remove any unwanted characters from the dataset.
  • Feature extraction: The final step in the data preprocessing step deals with finalizing the right number of features required for the training set. One has to analyze and find out features that are absolutely important for the model to function accurately and select them for faster computations and low memory consumption.

Data Conversion

The data conversion stage consists of the following steps,

  • Scaling: Once the data is placed, it’s necessary to scale the data as per a definite value. For example, a bank application containing transaction amount being important, then it’s required to scale the data on transaction value to build a robust model.
  • Disintegration and composition: There might be certain features in the training data that can be better understood by the model when split. For example, time-series function, where days, month, year, hour, minutes, and seconds can be split for better processing.
  • Composition: While some features can be better utilized when disintegrated, other features can be better understood when combined with another.

This covers the necessary steps to be taken to build a high-quality training set for AI & ML models. While this might help you formulate a framework that helps you build training sets for your system, here’s how you can put these frameworks into action.

Dedicated In-house Team

One of the easiest way for you could be to hire an intern to help you with the task of collecting and preprocessing data. You can also set up a dedicated ops team to help with your training set requirements. While this method provides you with greater control over the quality, it isn’t scalable, and you’ll be forced to look for more efficient methods eventually.

Outsource Training Set Creation

If having an in-house team doesn’t cut it, it would be a smarter move to outsource it, right? Well, not entirely.

Outsourcing your training set creation has its own set of troubles. Right from training people to ensuring quality is maintained to making sure people aren’t cutting slack.

Training Data Solutions Providers

With AI & ML technologies continuing to grow and more companies joining the bandwagon to roll out AI-enabled tools. There are a plethora of companies that can help you with your AI/ML training dataset requirement. We at Bridged.co have served prominent enterprises delivering over 50 million datasets.

And that is everything you need to know about training data, and how to go about creating one that helps you build powerful, robust, and accurate systems.