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Showing posts from May, 2021

Can OpenCV use for Machin learning? OpenCV – An Introduction to OpenCV.

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Original Source Here Can OpenCV use for Machin learning? OpenCV – An Introduction to OpenCV. OpenCV is an open-source package/library which is aimed at real-time computer vision. The library is cross-platform – it can support Python, Java, c++, etc. It was originally developed by intel. It’s free for use under the open-source BSD license. The OpenCV library is one of the most widely used packages for implementing video detection, motion detection, video recognition image recognition, and even deep learning face recognition applications. Few use cases for OpenCV are 2d and the 3d featured toolkits. The facial recognition application is one of the most widely used applications of computer vision. Gesture recognition. Human-computer interaction Motion understanding Object Detection Segmentation and Recognition M o tion Tracking. The following are the key research areas in computer vision and image analytics: Deep neural networks for biometric evaluation Facial sentimen

How to Build A Machine Learning API Using Flask

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Original Source Here Implementation Now you understand the basic concept of REST API. Let’s get into the implementation using Flask. There are several processes that we will cover: Importing libraries Load the machine learning model Build functions to preprocess and to predict the image Initialize the flask object Set the route and the function that returns something to the user’s browser Run and test the API Importing libraries The first step is to load the libraries. The libraries that we will import are TensorFlow, Flask, Pillow, and other supporting libraries. If those libraries are not installed, you can install them by using the pip command. Here is the code to import libraries: Load the model After you load the libraries, the next step is to load the machine learning model. In this case, I will use my image classifier model, which I’ve already pre-trained before using TensorFlow. The model will predict whether the image contains food or not. Here is the code

Tackling Noise Pollution with Machine Listening

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Original Source Here Tackling Noise Pollution with Machine Listening How audio analytics can help cities mitigate noise problem Common sources of noise pollution Sound is a natural part of our surroundings. But when sounds become unwanted and loud, they can turn into noise pollution. Noise pollution is considered to be any unwanted or disturbing sound that affects the health and well-being of humans and other organisms. Sound is measured in decibels. Noises are considered to be at an acceptable level if they are between 40 and 60 decibels, or match the ambient background noise — whichever is higher. Any sound above acceptable levels is generally considered noise pollution, and sounds that reach 85 decibels or higher can harm a person’s ears. Examples of typical noises include: city traffic (70 decibels), lawn mowers (90 decibels), subway trains (90 to 115 decibels), and car horns (110 decibels). Other major contributors to the urban noise include loud music and constructi

Self-Training Machines

https://miro.medium.com/max/1200/0*SiJ9s1hXtTegibI_ Original Source Here Artificial Intelligence for Computers who want to be human. Photo by Possessed Photography on Unsplash Humans are biological machines who can teach themselves. If we want to achieve actual machine intelligence, we can truthfully label “Artificial Intelligence” and build machines that can teach themselves. This may sound like a crazy idea from a non-PhD, and it may be, but everyone loves a crazy idea when it works. And it seems to be given to me to share crazy ideas everyone might learn to love because they might work. How do humans learn? Humans begin by learning elementary things. For instance, humans learn simple shapes like circles, squares, triangles, and others. Then humans learn how to apply these simple shapes to other shapes. For instance, what is the shape of a clock? Clocks do not have to be round, but the typical form is round. Clocks have numbers arranged clockwise, and clockwise is a

Classification Models and Thresholds

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Original Source Here Classification Models and Thresholds How to chose the thresholds and why they matter Photo by Pawel Czerwinski on Unsplash Classification models are a subset of supervised machine learning . A classification model reads some input and generates an output that classifies the input into some category. For example, a model might read an email and classify it as either spam or not — binary classification. Alternatively a model can read a medical image, say a mammogram, and classify it as either benign or malignant. Classification algorithms, like log i stic regression, generate a probability score that assigns some probability to the input belonging to a category. This probability is then mapped to a binary mapping, assuming the classification is binary (malignant or benign, spam or not spam). In the previous spam example, a model might read an email and generate a probability score of 92% spam, implying that there is a very high chance that this email i

How Agile Project Management Made AI a Better Platform?

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Original Source Here How Agile Project Management Made AI a Better Platform? Agile project management uses the standards of Agile programming advancement to different management measures, especially project management. Following the presence of the Manifesto for Agile Software Development in 2001, Agile strategies began to spread into unalike spaces of movement. How Agile Project Management Made AI a Better Platform? What does Agile Project Management depend upon? Agile project management is an iterative way to deal with overseeing programming advancement projects that center around constant deliveries and consolidating client criticism with each emphasis. Programming groups that embrace agile project management systems speed up, grow joint effort, and encourage the capacity to more readily react to advertise patterns. Agile Project Management | Commerce.AI Agil e approaches are amazingly famous for an extensive scope of utilization improvement purposes and in lig

Create projects on GitHub like a Pro!

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Original Source Here Different Sections regarding Documentation The number and types of sections will differ from person to person. In this article, I will share the sections which I use and will be beneficial for everyone. The documentation is done on the README.md file. 1. Project Title, and a short description of the project. For kicking things off, we need to have a title to our project. A project without its title is like a book without its title. To follow that, a 2–3 lines description of our project. As an example, I’ll be showing my project, CROPify to give you a more clear understanding: Title and short description 2. Motivation and purpose of the project. The motivation of a project is paramount, as it lets the recruiter/people know your purpose for creating the said project. In this example, I have listed down why I created CROPify: The motivation of the project. 3. Tech Stack and Resources used. “Tech Stack” basically means the different technologies you

Decoding the Future of Voice Commerce

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Original Source Here Decoding the Future of Voice Commerce VP of Product, Lynette Tay shares her opinion on why the future of customer experience lies in voice activation and Singapore is lagging behind. illustrations by Amanda “To be pandemic prepared, is to be tech-prepared,” says VP of Product, Lynette Tay, What life will be like in 2025 in the wake of the global pandemic and other crises, Lynette shared her view that human-technology relationships will deepen as larger segments of the population come to rely more on digital connections for work, education, healthcare, daily transactions, and essential social interactions. The implementation of social distancing and avoiding physical contact have given voice technology new momentum and encouraged a push towards touchless controls and experience. The latest popular mobile application to feed our manifestation of the desire to connect with one another as we isolate ourselves at home is Clubhouse due to its by-invite-

Learnings from FastAI Lesson 1

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Original Source Here Learnings from FastAI Lesson 1 In my effort to grab a Co-op, I managed to set up a brief 30 min meeting with an AI/ML-focused founder. I really wanted to know what I could do to make myself a strong candidate. One of the first things he mentioned was FastAI. And so started my FastAI learning journey. So, What did I learn from my First Lesson? a) Origination of Machine Learning: Contrary to popular belief, M a chine Learning is a relatively old topic. “It goes back to 1943 when Warren McCulloch and Walter Pitts teamed up to develop a mathematical model of a neuron.” (Howard & Gugger, 2018) They were able to “represent a simplified model of a neuron using simple addition and thresholding”(Howard & Gugger, 2018). Frank Rosenblatt, a psychologist from Cornell “further developer the artificial neuron to give it the ability to learn.”(Howard & Gugger, 2018) His contribution was especially influential as he worked on building the first device using Mac

Tuning the Hyperparameter thus automating Deep Learning model

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Original Source Here Tuning the Hyperparameter thus automating Deep Learning model ML model to train ML model… sounds cool..? Introduction This is the era where every Computer Science Student is looking forward to be a Data Scientist. Their job heavily involves using the data to make predictions. But there is a lot of manual things which needs to be done like hyperparamater tuning. So why not just automate it? This blog contains automation of hyperparameter tuning using python and devops idea. What are Hyperparameters ? Hyperparameters are the variabl e s which determines the network structure (Eg: Number of Hidden Units) and the variables which determine how the network is trained (Eg: Kernel Size). Hyperparameters are set before training (before optimizing the weights and bias). Now Since we are aware with the objective of the main objective of the blog, let us continue. Here the blog is divided into 5 separate jobs that will do the work for us, so let’s get started.

Learning Neural Networks: Intro to CNN using Keras to classify Rock, Paper, Scissors Images

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Original Source Here Learning Neural Networks: Intro to CNN using Keras to classify Rock, Paper, Scissors Images For the longest time, Neural Networks was one of the topics I was eager to learn the most not only b e cause of their application in many incredible tasks such as Image Recognition and Natural Language Processing , but because I could never wrap my head around the fact that many of the advancements in the field came from research in the way our own brain works. To think that something as complex as the brain’s architecture is somehow replicable and even more so , that we’ve managed to make it work is mind blowing. One of my early encounters with Machine Learning was reading The Master Algorithm by Pedro Domingos. In his book, Domingos describes the quest for the ultimate learning machine and the implications of such a learner, and along the way, he explains the progress there has been in the field of AI. Domingos explained the different tribes in Machine learning

On-Demand Applications: A new Trend in the Digital Economy

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Original Source Here On-Demand Applications: A new Trend in the Digital Economy Gone are the days where big online retailers would choose what goods to offer for sale online. Gone are the days when retailers would decide what the new fashion would be, which destination would be hype, what services would be cool. Consumers didn’t have a choice, or a say in deciding what to choose or buy. The on-demand economy is changing business as we know it by forcing us to redefine the employee/employer relationship and give consumers a taste of ultra-personalized service without the concierge level price tag, without the lease fees. The on-d e mand economy has played a major role in the recent evolution of business practices, getting stronger, and it’s certainly disrupting traditional business models, at the same time delivering a more streamlined service, outpacing the growth of ecommerce and its market share of retail sales. The industry has plenty of room for growth. One main reason

5 Ideas For Integrating AI in Customer Service

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Original Source Here 5 Ideas For Integrating AI in Customer Service Prevent CSR burnout and improve customer service workflows with AI Source: Unsplash Got customer service data? Customer service inquiries come in all shapes and forms. Emails, support tickets, tweets, chat conversations with support staff, and chatbot conversations. That’s a lot of data that you’re dealing with. Plus, it’s mostly unstructured and scattered, making it that much harder to manage. Sources of customer support queries How can you leverage all this data to improve speed in responding to customers? Or reducing the number of incoming support tickets? Well, it can be done with automation using Natural Language Processing (NLP), a sub-area of AI. Let’s explore 5 areas in customer service that can benefit from NLP-driven automation. This is not an exhaustive list by any means, but one that applies to most customer support teams at medium to large organizations. #1 Recommend best answers Pho