The remaining useful life (RUL) is the length of time a machine is likely to operate before it requires repair or replacement. By taking RUL into account, engineers can schedule maintenance, optimize operating efficiency, and avoid unplanned downtime. For this reason, estimating RUL is a top priority in predictive maintenance programs.
Three are modeling solutions used for predicting the RUL which are mentioned below:
Regression: Predict the Remaining Useful Life (RUL), or Time to Failure (TTF).
Binary classification: Predict if an asset will fail within a certain time frame (e.g., Hours).
Multi-class classification: Predict if an asset will fail in different time windows: E.g., fails in window [1, w0] days; fails in the window [w0+1, w1] days; not fail within w1 days.
In this blog, I have covered binary classification and multi-class classification in the below sections.
As of today, the hottest jobs in the industry are around AI, Machine Learning and Deep Learning. Let me try to outline the learning path for you in machine learning for the job profiles such as Data Scientist, Machine Learning Engineer, AI Engineer or ML Researcher.
AI basically means Artificial Intelligence – Making machines behave like an intelligent being. AI is defined around its purpose. To achieve AI, we use various hardware and software. In software, we basically use two kinds of approaches: Rule-Based and Machine Learning based.
In the rule-based approach, the logic is coded by people by understanding the problem statement. In the machine learning approach, the logic is inferred using the data or experience.
There are various algorithms or approaches that are part of the machine learning such as linear regression (fitting a line), Support vector machines, decision trees, random forest, ensemble learning and artificial neural networks etc.
The artificial neural network-based algorithms have proven very effective in recent years. The area of machine learning that deals with a complex neural network is called Deep Learning.
As part of this post, I want to help you plan your learning path in Machine Learning.
If you are looking for a non-mathematical and light on coding approach, please go through the course on “AI for Managers“. It is a very carefully curated and a very unique course that deals with AI and Machine Learning for those who are looking for a less mathematical approach.
Malcolm X once said, “Education is our passport to the future”. This has become more relevant than ever in the last year. The COVID-19 pandemic gave a big jolt to the economy and the existing strata of professions across the world. Many succumbed to the pandemic by losing their jobs and facing extensive pay cuts.
People who were up-to-date with technology made it through the darkest times, making online education become the next big thing across the globe. According to a recent LinkedIn survey, around more than 60% of professionals have increased the amount of time spent on online learning for upskilling during the lockdown period. But the challenge here was that online education was becoming more and more expensive with a consistent fall in the quality of content. Online education slowly started becoming a very far-fetched dream for the common man.
At CloudxLab, we strive to ensure that education does not feel like a luxury but a basic need that everybody is entitled to. Keeping this in mind, we bring forth the “#NoPayJan” where you can access some of the most sought after and industry-relevant courses completely free of cost. During #NoPayJan anybody who is signing up at CloudxLab will be able to access the contents of all the self-paced courses. This offer will be running from January 1 till January 31, 2021. CloudxLab provides an online learning platform where you can learn and practice Data Science, Deep Learning, Machine Learning, Big Data, Python, etc.
When the highly competitive and commercialized education providers have cluttered the online learning platform, CloudxLab tries to break through with a disruptive change by making upskilling affordable and accessible and thus, achievable.
It is a well-known fact that deep learning models are heavy; with a lot of weights for the deep layers. And it is obviously an overhead to load the model every time we need to get the predictions from the model. Thus this is costly in terms of the time of execution.
In this project, we will mainly focus on addressing this issue, by uniquely integrating the networking functionalities provided by ZMQ library. We will build a server-client based architecture to make the model load exactly once(that is during the starting of the app). The predictions from the model will be served by the model server, as long as it listens to its Flask client which requests it for the predictions for an input image.
REVA University and Cloudxlab research collaboration intends to work on technologies involving, deep learning, reinforced learning, curiosity-based machines, distributed computing, and launching specialized courses in these advanced technologies.
This collaboration will be aimed at providing and launching some highly sought-after courses in deep technologies involving experts from Academia as well as the industry. These courses will be delivered in hybrid mode – a combination of physical classroom, online instructor-led, self-paced, and project-based modes.
Dr. P. Shyama Raju, honorable Chancellor, REVA University said “This one-of-a-kind collaboration is aimed at being a launchpad for those who are planning to step into the world of AI, Deep Learning and other advanced technologies. It affirms REVA University’s commitment to make high-end technical education available to everyone in the world.”
“With Artificial Intelligence, machine learning, and other high-end technologies influencing every aspect of our lives, we are optimistic that this collaboration will help professionals in shaping their career”, says Sandeep Giri, CEO, and Founder at CloudxLab.
About REVA University
REVA University is one of the top-ranked private Universities in Bangalore, India, offering a wide range of UG, PG and PhD programs. REVA Academy for Corporate Excellence (RACE) is one of the initiatives of REVA University focused on corporate training to develop visionary enterprise leaders through progressive and integrated learning capabilities. RACE offers best-in-class, specialized, techno-functional, and interdisciplinary programs that are designed to suit the needs of working professionals.
If you are interested in Machine Learning or Deep Learning, but struggling to decide which book to use to study the same, here is a list of the best books in these fields. What makes this list even better is that some of these books are available online, for free! So go through the list, and pick the one that suits you best.
1. Deep Learning Book – by Aaron Courville, Ian Goodfellow, and Yoshua Bengio This book covers them all, including the mathematics required for Deep Learning. What’s more, it is available for free from the official website of this book. This is a must have for any serious Deep Learning practitioner.
Backpropagation is considered as one of the core algorithms in Machine Learning. It is mainly used in training the neural network. What if we tell you that understanding and implementing it is not that hard? Anyone who knows basic of Mathematics and has knowledge of basics of Python Language can learn this in 2 hours. Let’s get started.
Though there are many high-level overviews of the backpropagation algorithm what I found is that unless one implements the backpropagation from scratch, he or she is not able to understand many ideas behind neural networks.
Recently, I came up with an idea for a new Optimizer (an algorithm for training neural network). In theory, it looked great but when I implemented it and tested it, it didn’t turn out to be good.
Some of my learning are:
Neural Networks are hard to predict.
Figuring out how to customize TensorFlow is hard because the main documentation is messy.
Theory and Practical are two different things. The more hands-on you are, the higher are your chances of trying out an idea and thus iterating faster.
I am sharing my algorithm here. Even though this algorithm may not be of much use to you but it would give you ideas on how to implement your own optimizer using Tensorflow Keras.
A neural network is basically a set of neurons connected to input and output. We need to adjust the connection strengths such that it gives the least error for a given set of input. To adjust the weight we use the algorithms. One brute force algorithm could be to try all possible combinations of weights (connections strength) but that will be too time-consuming. So, we usually use the greedy algorithm most of these are variants of Gradient Descent. In this article, we will write our custom algorithm to train a neural network. In other words, we will learn how to write our own custom optimizer using TensorFlow Keras.
GPT-3 is the largest NLP model till date. It has 175 billion parameters and has been trained with 45TB of data. The applications of this model are immense.
GPT3 is out in private beta and has been buzzing in social media lately. GPT3 has been made by Open AI, which was founded by Elon Musk, Sam Altman and others in 2015. Generative Pre-trained Transformer 3 (GPT3) is a gigantic model with 175 billion parameters. In comparison the previous version GPT2 had 1.5 billion parameters. The larger more complex model enables GPT3 to do things that weren’t previously possible.