Whenever we have our live talks of CloudxLab, in presentations or in a conference, we want to live stream and record it. The main challenge that occurs is the presenter gets out of focus as the presenter moves. And for us, hiring a cameraman for three hours of a session is not a viable option. So, we thought of creating an AI-based pan and tilt platform which will keep the camera focussed on speaker.
So, Here are the step-by-step instructions to create such a camera along with the code needed.
As part of this blog post, I am going to walk you through how an Artificial Neural Network figures out a complex relationship in data by itself without much of our hand-holding. You should modify the data generation function and observe if it is able to predict the result correctly. I am going to use the Keras API of TensorFlow. Keras API makes it really easy to create Deep Learning models.
Machine learning is about computer figuring out relationships in data by itself as opposed to programmers figuring out and writing code/rules. Machine learning generally is categorized into two types: Supervised and Unsupervised. In supervised, we have the supervision available. And supervised learning is further classified into Regression and Classification. In classification, we have training data with features and labels and the machine should learn from this training data on how to label a record. In regression, the computer/machine should be able to predict a value – mostly numeric. An example of Regression is predicting the salary of a person based on various attributes: age, years of experience, the domain of expertise, gender.
The notebook having all the code is available here on GitHub as part of cloudxlab repository at the location deep_learning/tensorflow_keras_regression.ipynb . I am going to walk you through the code from this notebook here.
Generate Data: Here we are going to generate some data using our own function. This function is a non-linear function and a usual line fitting may not work for such a function
Let us say, you have trained, fine-tuned and tested Machine Learning(ML) model – sgd_clf, which was trained and tested using SGD Classifier on MNIST dataset. And now you want to deploy it in production, so that consumers of this model could use it. What are different options you have to deploy your ML model in production?
Mathematics is the science which deals with the logic of quantity, shape, and arrangement. Undeniably, math is all around us, in fact in everything we do. It wouldn’t be wrong to say, math is the building block for everything in our daily life period. Money, sports, architecture (ancient and modern), television, mobile devices, and even art, all of it has some mathematical concepts involved in it.
In India, mathematics has its origins in Vedic literature which is nearly four thousand years old. It should come as no surprise that the concept of number ‘0’ was discovered in India; also, various treatises on mathematics were authored by Indian mathematicians. The techniques of trigonometry, algebra, algorithm, square root, cube root, negative numbers, and the most significant decimal system are concepts which were discovered by Indian mathematician from ancient India and are employed worldwide even today.
One of the classic problem that has been used in the Machine Learning world for quite sometime is the MNIST problem. The objective is to identify the digit based on image. But MNIST is not very great problem because we come up with great accuracy even if we are looking at few pixels in the image. So, another common example problem against which we test algorithms is Fashion-MNIST.
Fashion-MNIST is a dataset of Zalando’s fashion article images —consisting of a training set of 60,000 examples and a test set of 10,000 examples. Each instance is a 28×28 grayscale image, associated with a label.
Usually, the learners from our classes schedule 1-on-1 discussions with the mentors to clarify their doubts. So, thought of sharing the video of one of these 1-on-1 discussions that one of our CloudxLab learner – Leo – had with Sandeep last week.
Below are the questions from the same discussion.
You can go through the detailed discussion which happened around these questions, in the attached video below.
You already know that artificial
intelligence is grabbing the world, transforming nearly every industry,
business, trade, and function. But what you might not know are the incredible
AI technologist and researchers powering the edge of this momentous revolution.
Breakthroughs in AI are incredible, isn’t
it? But how does it all occur? It’s when extremely talented and diverse
thinkers from unique backgrounds, disciplines, expertise, and perspectives come
forward. You might think of names like Andrew Ng (Baidu), Amit Singhal (Uber), Elon
Musk (SpaceX & Tesla), Ginni Rometty (IBM) and Ray Kurzweil (Google) – but
wait why all men?
2019 March, on International Women’s Day, CloudXLab aims to showcase
11 incredible women in AI.
In recent years, career opportunities in artificial
intelligence (AI) has grown exponentially to meet the rising demand of
digitally transformed industries. But while there are amply of jobs available
in AI, there’s a momentous shortage of top talent with the essential skills.
But why does this demand and supply gap exist? Many aspiring candidates wish to
join the AI bandwagon, but several myths hold them behind.
Job site Indeed
highlights, the demand for AI skills has doubled over the last 3 years,
and the total number of job postings has upsurged by 119 percent. But,
job-aspirants interest in a career in artificial intelligence seems to have
leveled off. This clearly indicates employers are struggling to get good
talent. This is surely good news for all those planning to transit their
careers into AI!
Well, building a career in artificial intelligence demands a self-controlled approach. You might be interested in a career switch because of the exciting opportunities floating in this booming industry or maybe for that long deep-rooted interest to pursue AI as a career. Regardless of what your inspiration is, the first step to moving ahead in the AI career path is to ditch the myths and misconceptions that for long has been blocking your path.
Say you come up with a wonderful idea such as a really great phone service. You would want this phone service to be available to the APIs in various languages. Whether people are using Python, C++, Java or any other programming language, the users should be able to use your service. Also, you would want the users to be able to access globally. In such scenarios, you should create the Thrift Service. Thrift lets you create a generic interface which can be implemented on the server. The clients of this generic interface can be automatically generated in all kinds of languages.
Let us get started! Here we are going to create a very simple service that just prints the server time.
What computing did to the usual industry earlier, Machine Learning is doing the same to usual rule-based computing now. It is eating the market of the same. Earlier, in organizations, there used to be separate groups for Image Processing, Audio Processing, Analytics and Predictions. Now, these groups are merged because machine learning is basically overlapping with every domain of computing. Let us discuss how machine learning is impacting e-commerce in particular.
The first use case of Machine Learning that became really popular was Amazon Recommendations. Afterwards, the Netflix launched a challenge of Movie Recommendations which gave birth to Kaggle, now an online platform of various machine learning challenges.
Before I dive deep into the details further, lets quickly brief the terms that are found often confusing. AI stands for Artificial Intelligence which means being able to display human-like intelligence. AI is basically an objective. Machine learning is making computers learn based on historical or empirical data instead of explicitly writing the rules. Artificial Neural networks are the computing constructs designed on a similar structure like the animal brain. Deep Learning is a branch of machine learning where we use a complex Artificial Neural network for predictions.