Facial Emotion Recognition (FER) is an important topic in the fields of Computer vision and Artificial Intelligence owing to its significant academic and commercial potential.
We at Let’s Nurture are always ready for new experiments!
See the Video: A standard set of 7 emotion classifications are used: Anger, Disgust, Scared, Happy, Sad, Surprised, Neutral.
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Artificial Intelligence and Machine Learning is a hot topic in the tech industry. Check out this video – AI & Machine Learning for Object Identification By Team Let’s Nurture.
Google Cloud Developers Community co-organized an event named “TensorFlow Developer Summit 2019 Viewing Party” at Let’s Nurture’s Offshore Development Center (ODC) in Ahmedabad.
TensorFlow is an open source Machine Learning (ML) library by Google. TensorFlow held its third and biggest yet annual Developer Summit in California on March 6 and 7, 2019 having launched TensorFlow 2.0. TensorFlow Dev Summit brings together a diverse mix of machine learning users from around the world for two days of highly technical talks, demos, and conversation with the TensorFlow team and community.
Pioneering the innovation for 10+ years, Let’s Nurture Infotech, a leading IoT app development company in Ahmedabad, has always been a catalyst for organizing such events by Google at its ODC.
As a consequence, Google Cloud Developers Community and Let’s Nurture co-organized an event to bring machine learning enthusiasts at Let’s Nurture head office in Ahmedabad. The vision was to address and expand the AI-ML Engineers Community to address the questions and queries so that they can learn more about the latest version of TensorFlow 2.0. A number of mobile app developers, Embedded engineers, web developers were in the house. The event hosts were Kashyap Raval (Team Lead, AI Engineering Team, Let’s Nurture), Harsh Shah and Nikunj.
It all started with an abstract introduction about the event, followed by a brief introduction about every individual in the house. The agenda of the event was to watch together the videos of the 2019 TensorFlow Dev Summit at Let’s Nurture and discuss machine learning followed by a networking session.
Video 1: It was about the keynotes on TensorFlow 2.0 Dev Summit 2019. Megan Kacholia- Engineering Director, Rajat Monga- Engineering Director, Kemal El Moujahid-Director, Alina Shinkarsky – Program Manager talked about important updates of TensorFlow dev summit.
(Video Source: YouTube)
Video2: Introducing TensorFlow 2.0 and its high-level APIs used for mobile, web and embedded systems development on ML-based projects.
Video3: TensorFlow Lite introduction was given and how it can be used for embedded devices or mobile devices solving Machine Learning related issues. Moreover, the difference between TensorFlow 1.0 and TensorFlow 2.0 was highlighted for ML enthusiasts to take notes.
Now was the time for the networking session. We arranged Pizzas and Soft drinks for the guests, hosts and our team.
The networking session turned out to be an interactive one where AI based questions and queries were asked.The hosts explained solutions to their doubts and also about the issues developers faced during development using TensorFlow.
AI-ML engineers at Let’s Nurture have hands-on experience in using TensorFlow as a Machine learning framework to provide solutions which empower businesses to think, predict and act by making better decisions.
Get in touch with our AI-ML Experts for technical consultation and business-driven solutions using such cognitive technologies.
Nowadays, Machine Learning has become a major component in large business operations that are utilizing the technological advancements of artificial intelligence. The term, ‘Machine Learning’ was coined back in 1959 by IBM but hasn’t seen any significant progression until 2016. ML is considered a subset of artificial intelligence, as it provides the systems needed for an AI to learn, improve upon, and apply what it experiences without explicit programming to tell it to do so. To put it simply, machine learning focuses on developing frameworks for computers to access data, use data, and learn from said data on their own. Although complex in its own right, machine learning frameworks such as Google’s TensorFlow, have simplified the process, refining down results, training models, and analytic predictions. This has caused a massive ripple across all industries that use information technology, including healthcare, automotive, gaming, and aviation to name a few.
TensorFlow is an open-source machine learning framework or software library, created by the Google Brain team. It makes use of observation skills and reasoning skills through the combination of machine learning, deep learning, and efficiency algorithms that speed up the learning and application process. The framework has a seamless front-end API, courtesy of programming language Python, which is useful for building all sorts of applications, and it utilizes C++ (programming language) for high-performance purposes.
Although categorized as a software library, it is actually a set of APIs, that allow programmers to have full control over the models they build, meaning that they do not have to hand over low-level algorithms each time they want to use the codebase. Plus, TensorFlow works as a visual learning tool through TensorBoard, which gives programmers real-time visualizations of all machine learning work done within the framework.
TensorFlow allows programmers to exclusively focus in on the overall logic of the application they are building rather than having to deal with or program every micro detail into the application (this is a process called abstraction). This means less time is spent on incorporating multiple algorithms that result in one actionable function.
TensorFlow has an eager execution mode, which is a method that allows programmers to modify and evaluate each individual graph operation rather than requiring them to develop one whole graph as a single object. The TensorBoard visualization tool also allows programmers to profile and inspect these graphs in a similar way you would on an interactive dashboard, making it quite user-friendly.
The flexible nature of the TensorFlow architecture allows its computation to be deployed across a large variety of platforms. This can be used to make these platforms more efficient; allow developers to create in-browser incarnations for shared developmental models, and allow performance to be amplified.
TensorFlow brings machine learning to everyone as programmers can use it to build deep neural networks that can then be run across thousands of computers through data centers. Due to the fact that it is open-source software, industry sectors and associated companies can leverage TensorFlow for natural language processing, image recognition, and video/audio message scanning. Currently, TensorFlow is being widely used to prevent blindness in patients with diabetic retinopathy.
Due to the fact that almost all modern living amenities are powered through data, such as Netflix’s recommendations list and Siri’s answers on the iPhone, major businesses and companies are using machine learning through TensorFlow to improve upon internal processes. Things like conversion rates are getting boosted as companies are now able to use predictive analysis to make more accurate decisions. Beyond this, companies are looking for new ways to enhance customer service by using chatbots, voice-activated devices, and text-based applications. TensorFlow impacts these by providing the prediction, perception, understanding, and classification require to make these types of applications work.
TensorFlow is categorized as a second generation machine learning system and is currently being used by Google itself due to the wide-ranging capabilities and implications it has. Beyond making a ripple across the entire IT sector, TensorFlow is best known for its capability at handling unstructured data, dealing with large-scale problems, and conveying images with high accuracy. This is what makes this open-source library a must-have framework companion for companies who are adopting artificial intelligence, predictive analytics, and machine learning.
Let’s Nurture is the top custom mobile app development company providing AI-ML solutions to an array of industries. Our AI-ML engineers have developed multiple solutions like face detection, object detection, deep learning systems using Python, OpenCV, and Tensorflow. We also have expertise in custom AI-powered Chatbot development using Tensorflow and Dialogflow. Our AI programmer also has proficiency in AI engines like IBM Watson, Amazon Lex, Microsoft Luis, Facebook Wit, and Google AI. Let’s Nurture is also the best IoT app development company who provides custom IoT solutions using Tensorflow technology. We have developed in-house IoT based Smart urban farming solutions named AgriKonnect. Using Tensorflow, we can detect plants being affected by pest and diseases through IoT sensors and cameras.
We utilize machine learning, neural networks, and artificial intelligence to help businesses think, predict & act. If you wish to know more about what we can do with Tensorflow to provide innovative solutions, please get in touch with our Machine Learning experts.
That new artist you just favorited; that new genre you just discovered or that new song you are saving for a new day, are all products of music discovery and algorithms. Although we are perfectly capable of curating our own playlists, searching up new artists and bands, and finding new genres to enjoy, sometimes we just don’t have the time for it or don’t feel like thumbing through hundreds of thousands of songs. This is where your music streaming app becomes your best friend because it can turn a bad day into a bearable one and a boring transit ride into a silent concert, all with the push of a button. Welcome to automated music curation, a product of big data, machine learning, and artificial intelligence. Yes, this is how your music streaming app knows you so well; let’s explore how recommendation models work.
Generally speaking, there are three types of music recommendation models that use three different types of analysis. The first is collaborating filtering models, which looks at a user’s behavior on the music streaming app as well as other people’s behavior. The second is natural language processing (NLP models), which analyze text, and the third are audio models which take a look at the raw audio tracks themselves. In most cases, music streaming applications will use a combination of all three types of analysis, as this provides a unique and powerful discovery engine.
The most common example of collaborative filtering is star-based movie ratings that you get on Netflix. It provides you with a basic understanding of which movies you may like based on your previous ratings and it provides Netflix with the ability to recommend movies/television shows based on what similar users have enjoyed. For music applications, the collaborative filtering is based on implicit feedback data, meaning the live stream of your music is counted. This includes how many tracks are streamed and additional streaming data like whether a song is saved to a playlist or whether a user visits that particular artist’s page after listening to one of their songs. Although this is great, how does it actually work?
A user has a set of track preferences, such as P, R, Q, and T, whereas, another user may have a set of preferences denoted as R, F, P, and Q. The collaborative data shows that both users like Q, R, and P and so you probably are both very similar in what you like. This is furthered to say, you will probably enjoy what each other listen to and so you should check out the only track currently not mentioned in the preference list. So in this instance, for the first user it is F and for the second, it is track T. Now the question is, how does a music streaming app do this for millions of preferences? By using matrix math. Essentially, at the end of the mathematical equation, you get two types of vectors, where X is the user and Y is the song. When you compare these vectors, you find out which users have similar music tastes and which song’s are similar to the current song you are looking at.
The second type of music recommendations come from natural language processing which is sourced from text data. This can include news articles, blogs, text through the internet, and even metadata. What occurs here is your music streaming applications crawl the web, constantly looking out for written text about music, such as blogs or song titles, and figures out what people are saying about these songs or artists. Now because natural language processing allows a computer to understand human speech, it is able to see which adjectives are being used frequently in reference to the songs and artists in question. This data then gets processed into cultural vectors and top terms and given an associated weight that has corresponding importance. Basically, the probability that someone is going to use that specific term to describe a song, band, genre, or artist. If the probability is high, that piece of music is likely to be categorized as similar.
The third type of music recommendation comes from analyzing raw audio tracks. Although it may not seem like you would need this in your music streaming app if you have the first two, what this particular model does is improves the accuracy of recommendations by taking both old and new songs into account. An example of this would be a new song coming onto the music application and only getting 50-100 listens but since there are so few filtering against it, this type of song could end up on a discovery playlist alongside popular songs.
This is because raw audio models do not discriminate against new and popular songs, especially if natural language processing hasn’t picked the track up through text online. So how does raw audio tracks get analyzed? Through convolutional neural networks that form spectrograms. This works by creating convolutional layers or “thick and thin” bars that showcase time-frequency representations of audio frames and inputs. After passing through each layer, you are able to see what computing statistics are learned across the time of the song, or in layman’s terms the features of the songs. This can include a time signature, mode, tempo, loudness, and key of the song. Your music streaming application can then understand the fundamental similarities of songs and recommend them based on their listening features.
When it comes down to it, your music streaming application knows you because of the massive amount of data that it stores and analyzes. In order to work correctly though, audio files, matrices, mathematics, and text must all be analyzed in real-time, applied, and updated through machine learning processes. Yes, a form of artificial intelligence powers that perfect recommended playlist you tap into on a daily basis.
Want to develop beautiful music streaming app like Spotify for Android OR iOS? Let’s start a new project together!
Jignesh Thanki 