Creating AI Based Cameraman

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.

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Regression with Neural Networks using TensorFlow Keras API

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

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How to create an Apache Thrift Service – Tutorial


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.

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Use-cases of Machine Learning in E-Commerce

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.

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Phrase matching using Apache Spark

Recently, a friend whose company is working on large scale project reached out to us to seek a solution to a simple problem of finding a list of phrases (approximately 80,000) in a huge set of rich text documents (approx 6 million).

The problem at first looked simple. The way engineers had solved it is by simply loading the two documents in Apache Spark’s DataFrame and joining those using “like”. Something on these lines:

select, from phrases, docs where docs.txt like ‘%’ + phrases.phrase + ‘%’

But it was taking huge time even on the small subset of the data and processing is done in distributed fashion. Any Guesses, why?

They had also tried to use Apache Spark’s broadcast mechanism on the smaller dataset but still, it was taking a long while finishing even a small task.

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Scholarship Test for Machine Learning Course

After receiving a huge response in our last scholarship test, we are once again back with a basic conceptual test to attain scholarship for our upcoming Specialization course on Machine Learning and Deep Learning.

Concepts to be tested: Linear algebra, probability theory, statistics, multivariable calculus, algorithms and complexity, aptitude and Data Interpretation.

  • Date and Time: September 2, 2018, 8:00 am PDT (8:30 pm IST)
  • Type: objective (MCQ)
  • Number of questions: 25
  • Duration: 90 minutes
  • Mode: Online

If you have a good aptitude and general problem-solving skills, this test is for you. So, go ahead and earn what you deserve.

If you have any questions on the test or if anything else comes up, just click here to let us know. We’re always happy to help.


How to Teach Online Effectively

I founded in 2014 after working in Amazon. Teaching is my passion, and technology, specifically large-scale computing my forte, thanks to my working experience with Amazon, InMobi, D. E. Shaw and my own startup tBits Global. Therefore, I wanted to help people learn technology online. I launched, an online instructor-led training on MongoDB followed by Big Data and Machine learning. Eventually, we innovated a lot in learning and shaped KnowBigData into which is currently a major gamified learning environment for Machine Learning, AI, and Big Data.

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How To Optimise A Neural Network?

When we are solving an industry problem involving neural networks, very often we end up with bad performance. Here are some suggestions on what should be done in order to improve the performance.

Is your model underfitting or overfitting?

You must break down the input data set into two parts – training and test. The general practice is to have 80% for training and 20% for testing.

You should train your neural network with the training set and test with the testing set. This sounds like common sense but we often skip it.

Compare the performance (MSE in case of regression and accuracy/f1/recall/precision in case of classification) of your model with the training set and with the test set.

If it is performing badly for both test and training it is underfitting and if it is performing great for the training set but not test set, it is overfitting.

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Machine Learning with Mahout

[This blog is from It is pretty old. Many things have changed since then. People have moved to MLLib. We have also moved to]

What is Machine Learning?

Machine Learning is programming computers to optimize a Performance using example data or past experience, it is a branch of Artificial Intelligence.

Types of Machine Learning

Machine learning is broadly categorized into three buckets:

  • Supervised Learning – Using Labeled training data, to create a classifier that can predict the output for unseen inputs.
  • Unsupervised Learning – Using Unlabeled training data to create a function that can predict the output.
  • Semi-Supervised Learning – Make use of unlabeled data for training – typically a small amount of labeled data with a large amount of unlabeled data.

Machine Learning Applications

  • Recommend Friends, Dates, Products to end-user.
  • Classify content into pre-defined groups.
  • Find Similar content based on Object Properties.
  • Identify key topics in large Collections of Text.
  • Detect Anomalies within given data.
  • Ranking Search Results with User Feedback Learning.
  • Classifying DNA sequences.
  • Sentiment Analysis/ Opinion Mining
  • Computer Vision.
  • Natural Language Processing,
  • BioInformatics.
  • Speech and HandWriting Recognition.


Mahout – Keeper/Driver of Elephants. Mahout is a Scalable Machine Learning Library built on Hadoop, written in Java and its Driven by Ng et al.’s paper “MapReduce for Machine Learning on Multicore”. Development of Mahout Started as a Lucene sub-project and it became Apache TLP in Apr’10.

Topics Covered

  • Introduction to Machine Learning and Mahout
  • Machine Learning- Types
  • Machine Learning- Applications
  • Machine Learning- Tools
  • Mahout – Recommendation Example
  • Mahout – Use Cases
  • Mahout Live Example
  • Mahout – Other Recommender Algos

Machine Learning with Mahout Presentation

Machine Learning with Mahout Video

AutoQuiz: Generating ‘Fill in the Blank’ Type Questions with NLP

Can a machine create quiz which is good enough for testing a person’s knowledge of a subject?

So, last Friday, we wrote a program which can create simple ‘Fill in the blank’ type questions based on any valid English text.

This program basically figures out sentences in a text and then for each sentence it would first try to delete a proper noun and if there is no proper noun, it deletes a noun.

We are using textblob which is basically a wrapper over NLTK – The Natural Language Toolkit, or more commonly NLTK, is a suite of libraries and programs for symbolic and statistical natural language processing for English written in the Python programming language.

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