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.
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.
In this blog we will show how to label custom images for making your own YOLO detector. We have other blogs that cover how to setup Yolo with Darknet, running object detection on images, videos and live CCTV streams. If you want to detect items not covered by the general model, you need custom training.
In our case we will build a truck type detector. There are 4 types of trucks we will try to identify
we will see how to setup object detection with Yolo and Python on images and video. We will also use Pydarknet a wrapper for Darknet in this blog. The impact of different configurations GPU on speed and accuracy will also be analysed.
This blog is part of series, where we examine practical applications of Yolo. In this blog, we will see how to setup object detection with Yolo and Python on images and video. We will also use Pydarknet a wrapper for Darknet in this blog. The impact of different configurations GPU on speed and accuracy will also be analysed.
We will explore YOLO for image recognition in a series of blogs. This is the first one. In this blog, we will see how to setup YOLO with darknet and run it. We will also demonstrate the various choices you have with YOLO in terms of accuracy, speed and cost, enabling you to make a more informed choice of how you would want to run your models.
Setup Yolo with Darknet
The content in the blog is not unique. However if you are starting with YOLO, this is the first thing you need to do.