In this duology of blogs, we will explore how to create a custom number plate reader.
In this duology of blogs, we will explore how to create a custom number plate reader. We will use a few machine learning tools to build the detector. An automatic number plate detector has multiple applications in traffic control, traffic violation detection, parking management etc. We will use the number plate detector as an exercise to try features in OpenCV, tensorflow object detection API, OCR, pytesseract
In the current article, I am presenting the results of my experiments with Fashion-MNIST using Deep Learning (Convolutional Neural Network – CNN) which I have implemented using TensorFlow Keras APIs (version 2.1.6-tf).
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
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.
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.