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Showing posts with the label Artificial Intelligence

AI and ML: Are they one and the same?

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As children we believed in magic, imagined, and a fantasy where robots would one day follow our commands, undertaking our most meager tasks and even help with our homework at the push of a button! But sadly it always seemed that these beliefs, along with the idea of self-driven aero cars and jetpacks, belonged in a future beyond our imagination or in a Hollywood Sci-fi. Would we ever get to experience the future in our lifetime? But then it arrived! Artificial Intelligence, aka AI, made its debut in real life and became the buzz word of the 21st century, providing us with new ideas to explore and incredible possibilities. And just as we were getting used to AI we were introduced to Futuristic Learning, Deep Learning, and another term we often confuse with AI: Machine Learning (ML). Whew! Suddenly the future is well and truly here, and it’s hard to keep up with the advancement of these technologies, what each term means and how they relate to one another – particu

Build Your First Nueral Network: Basic Image Classification Using Keras

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Image classification is one of the most important problem to solve in machine learning. It can provide vital solutions to a variety of computer vision problems, such as face recognition, character recognition, object avoidance in autonomous vehicles and many others. Convolutional Neural Network (CNN), since its inception has been used for image classification and other computer vision problems. It is called convolutional neural network because of convolutional layer. Keras is a high level library which provides an easy way to get started with machine learning and neural networks. It will be used here to implement CNN to classify handwritten digits of MNIST dataset. Image Classification is  a process to determine which of the given classes an input image belongs to. CNNs represent a huge breakthrough in image classification. In most cases, CNN outperforms other image classification methods and provides near to human-level accuracy. CNN models do not simply spit the class nam

How Computers Understand Human Language?

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How Computers Understand Human Language? Photo by Alex Knight on Unsplash Natural languages are the languages that we speak and understand, containing large diverse vocabulary. Various words have several different meanings, speakers with different accents and all sorts of interesting word play. But for the most part human can roll right through these challenges. The skillful use of language is a major part what makes us human and for this reason the desire for computers that understand or speak human language has been around since they were first conceived. This led to the creation of natural language processing or NLP. Natural Language Processing is a disciplinary field combining computer science and linguistics. There is an infinite number of ways to arrange words in a sentence. We can't give computers a dictionary of all possible sentences to help them understand what humans are blabbing on about. So, an early and fundamental NLP problem was deconstructing sentences

Supervised Learning vs Unsupervised Learning vs Reinforcement Learning

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Supervised Learning vs Unsupervised Learning vs Reinforcement Learning Machine learning models are useful when there is huge amount of data available, there are patterns in data and there is no algorithm other than machine learning to process that data. If any of these three conditions are not satisfied, machine learning models are most likely to under-perform. Machine learning algorithms find patterns in data and try to learn from it as much as it can. Based on the type of data available and the approach used for learning, machine learning algorithms are classified in three broad categories. Supervised learning Unsupervised learning Reinforcement learning An abstract definition of above terms would be that in supervised learning, labeled data is fed to ML algorithms while in unsupervised learning, unlabeled data is provided. There is a another learning approach which lies between supervised and unsupervised learning, semi-supervised learning. Semi supervised learning al

AI Vs Machine Learning Vs Deep Learning

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AI Vs Machine Learning Vs Deep Learning Artificial intelligence, deep learning and machine learning are often confused with each other. These terms are used interchangeably but do they do not refer to the same thing. These terms are closely related to each other which makes it difficult for beginners to spot differences among them. The reason I think of this puzzle is that AI is classified in many ways. It is divided into subfields with respect to the tasks AI is used for such as computer vision, natural language processing, forecasting and prediction, with respect to the type of approach used for learning and the type of data used. Subfields of Artificial Intelligence have much in common which makes it difficult for beginners to clearly differentiate among these areas. Different approaches of AI can process similar data to perform similar tasks. For example Deep learning and SVM both could be used for object detection task. Both have pros and cons. In some cases Machine Learn

Unsupervised Singing Voice Conversion: Facebook's AI Can Convert Your Voice Into Any Other Singer's Voice

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unsplash-logo Jason Rosewell You might have heard of the miracles artificial intelligence is doing these days. From movie recommendation on Netflix to Sophia, the robot, from intelligent humanoids in video games to super intelligent Google Assistant, artificial intelligence is dominating most aspects of our lives. It has an impact on our present and future in either way. Continuing the pursuit to a technology intensive future, Facebook in collaboration with Tel Aviv University published their new research last week on arxiv.org ( Unsupervised Singing Voice Conversion ). Researchers at Facebook have trained an intelligent machine which can instantly convert audio of one singer to voice of another singer. You check the sample results here . All that is needed is to record a song in own "beautiful" voice and feed the file to the machine. AI will never disappoint you. The results will astound you.   Summary of the work According to authors of the work, Our approach

Deep Learning: A Quick Overview

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Photo by William Stitt on Unsplash This photo is from 1980, and the reason why we are dwelling into history is that neural networks along with deep learning have been around for quite some time. They have started picking up now and impacting the world right now, but if you look back into the 80s you will realize that even though they computers weren’t invented in 60s and 70s they really caught on to a trend and got the wind in 80, So people started talking about them a lot. There was done a lot of research in that area and everybody thought that deep learning or neural networks this new thing that is going to impact the world and is going to change everything and will solve all the world problems. And then it kind of slowly died for the next decade. So what happened why didn’t the neural networks survived and not change the world? The reason was they were not just good enough, they are not that good at predicting things, and not that good at modeling or they were not a

7 Awesome Examples of Computer Vision

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Photo by David Travis on Unsplash Though early experiments in computer vision started from the 1950s  and it was initially put to use to distinguish between handwritten and typed text from the 1970s. Today the applications for computer vision have increased exponentially. In this article, we will share with you some of the recent implementation trends of computer vision. What's Computer Vision (CV)? Computer vision is the use of computers which process visual data and then make conclusions from it or gain understanding about the situation and the surroundings. One of the factors behind the growth of computer vision is the amount of data today which we use subsequently to train and improve computer vision machines. We have a bulk amount of visual data in the form of images and videos produced by built-in cameras of our phones alone. However, while visual data can include photos and  videos, it can also get information from other sources and detectors. Besides with t

Where Is Artificial Intelligence Leading The World? To The END?

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Less than 100 years old Artificial intelligence (AI), born in the middle of 20 th century is ruling over the world. If you think it is not then I would say it is about to rule. It is also said that AI is 5000 years old but who cares. I will go with the younger one. AI made its way all along from symbolic AI of good old fashioned artificial intelligence to future predicting models and expert systems. It is everywhere, in agriculture, in health, education, finance, media, music, marketing, HR, games, security, and over and beneath the tides of oceans and in the silent space and everywhere you can think of. All I am trying is just to say that AI is ubiquitous Period. Researchers have made fascinating breakthroughs with breakneck speed but they are still striving to make milestones. AI is no more confined only to Hollywood, it is a reality now. The question is how this reality would come to us? As a curse or as a blessing? Max Hawkin, a software engineer, consciously submitte