CloudxLab conducted a successful webinar on “Introduction to Machine Learning” on the 15th of October, 2019. It was a 2-hour session in which the instructor explained the concepts based on Understanding Computer Vision with Deep Learning.
More than 250 learners around the globe attended the webinar. The participants were from countries namely; United States, Canada, Australia, Indonesia, India, Thailand, Philippines, Malaysia, Macao, Japan, Hong Kong, Singapore, United Kingdom, Saudi Arabia, Nepal, & New Zealand.
Every day the world is advancing into the new level of industrialization and this has resulted in the production of a vast amount of data. And, at initial stages, people started considering it as a bane, but later they found out that it’s a boon. So, they started using this data in a productive way. Big data and machine learning are terminologies based on the concept of analyzing and using the same data. Let’s get into more details.
RACE360, an Emerging Technology Conference 2019 (Powered by The Times of India) is happening on Wed, Aug 28th at The Lalit Ashok, Bengaluru. It is presented by REVA University, Bengaluru (REVA Academy for Corporate Excellence (RACE)).
The emergence of Artificial Intelligence has played an essential role in revolutionizing the technical industry. According to many people, Artificial Intelligence is something that makes their work easy; well, it is just one of the qualities of Artificial Intelligence.
What is Artificial Intelligence?
According to Wikipedia, Artificial Intelligence “is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans.”
Artificial intelligence can be categorized into several stages, depending upon the role they play. In this article, we will go through all of these stages, including their real-world application.
Henry Ford (Founder of Ford Motor Company) once said- “The only worse thing than training your employees and having them leave is not training them and having them stay”.
Most organizations face this dilemma and sometimes choose not to upskill their workforce only to impede its own growth and relinquish opportunities of gaining competitive advantage. While organizations actively promoting workforce learning & development (L&D) often face indifferent employee behaviours to such initiatives. There are other concerns as well, such as- customised learning platforms, hands on learning, training quality, accreditation, post training support and what not……..
Generally, Machine Learning (or Deep Learning) projects are quite unique and also different from traditional web application projects due to the inherent complexity involved with them.
The goal of this article is, not to go through full project management life cycle, but to discuss a few complexities and finer points which may impact different project management phases and aspects of a Machine Learning(or Deep Learning) project, and, which should be taken care of, to avoid any surprises later.
Below is a quick ready reckoner for the topics that we will be discussing in this article.
The deep learning algorithms and frameworks have changed the approach to computer vision entirely. With the recent development in computer vision with Convolutional Neural Networks such as Yolo, a new era has begun. It would open doors to new industries as well as personal applications.
After the successful bootcamps held at IIT Bombay, NUS Singapore, RV College of Engineering, etc, CloudxLab in collaboration with IoTSG and Google Asia conducted a successful conference on Understanding Computer Vision with AI using Tensorflow on May 11, 2019, at Google Asia, Singapore office.
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?
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.