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
if x < 30:
mult = 10
elif x < 60:
mult = 20
mult = 50
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.
These Machine Learning Interview Questions, are the real questions that are asked in the top interviews.
For hiring machine learning engineers or data scientists, the typical process has multiple rounds.
A basic screening round – The objective is to check the minimum fitness in this round.
Algorithm Design Round – Some companies have this round but most don’t. This involves checking the coding / algorithmic skills of the interviewee.
ML Case Study – In this round, you are given a case study problem of machine learning on the lines of Kaggle. You have to solve it in an hour.
Bar Raiser / Hiring Manager – This interview is generally with the most senior person in the team or a very senior person from another team (at Amazon it is called Bar raiser round) who will check if the candidate fits in the company-wide technical capabilities. This is generally the last round.
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 phrase.id, docs.id 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.
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)
I founded KnowBigData.com 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 KnowBigData.com, 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 Cloudxlab.com which is currently a major gamified learning environment for Machine Learning, AI, and Big Data.