Neural Networks

Solving Industry based and Solution based problems through Neural Networks

1. Neural Nets as Regressor

DOMAIN : Electronics and Telecommunication

CONTEXT

Communications equipment manufacturing companies have products which are responsible for emitting informative signals. We will build a machine learning model which can help the companies to predict the equipment’s signal quality using various parameters.

• Data Description: The data set contains information on various signal tests performed:

1. Parameters: Various measurable signal parameters.
2. Signal_Quality: Final signal strength or quality.

• Objective: Building a regressor which can use these parameters to determine the signal strength or quality [as number].

• Steps and tasks:

1. Import data.
2. Data analysis & visualisation • Performing relevant and detailed statistical analysis on the data. • Performing relevant and detailed uni, bi and multi variate analysis.
3. Designing, training, tuning and testing a neural network regressor.
4. Pickling the model for future use.

2. Neural Nets as Classifier

DOMAIN : Electronics and Telecommunication

CONTEXT

Communications equipment manufacturing companies have products which are responsible for emitting informative signals. We will build a machine learning model which can help the companies to predict the equipment’s signal quality using various parameters.

• Data Description: The data set contains information on various signal tests performed:

1. Parameters: Various measurable signal parameters.
2. Signal_Quality: Final signal strength or quality.

• Objective: The need is to build a classifier which can use these parameters to determine the signal strength or quality [as number].

• Steps and tasks:

1. Import data

2. Data analysis & visualisation • Perform relevant and detailed statistical analysis on the data. • Perform relevant and detailed uni, bi and multi variate analysis.

3. Design, train, tune and test a neural network classifier..

4. Pickle the model for future use.

3. Clickable GUI

A clickable GUI [desk application] which can automate Part 1 & 2 of this project.

4. Digital Classifier For Identifying House Numbers

DOMAIN: Autonomous Vehicles

CONTEXT A Recognising multi-digit numbers in photographs captured at street level is an important component of modern-day map making. A classic example of a corpus of such street-level photographs is Google’s Street View imagery composed of hundreds of millions of geo-located 360-degree panoramic images. The ability to automatically transcribe an address number from a geo-located patch of pixels and associate the transcribed number with a known street address helps pinpoint, with a high degree of accuracy, the location of the building it represents. More broadly, recognising numbers in photographs is a problem of interest to the optical character recognition community. While OCR on constrained domains like document processing is well studied, arbitrary multi-character text recognition in photographs is still highly challenging. This difficulty arises due to the wide variability in the visual appearance of text in the wild on account of a large range of fonts, colours, styles, orientations, and character arrangements. The recognition problem is further complicated by environmental factors such as lighting, shadows, specularity, and occlusions as well as by image acquisition factors such as resolution, motion, and focus blurs. In this project, we have uses the dataset with images centred around a single digit (many of the images do contain some distractors at the sides). Although we are taking a sample of the data which is simpler, it is more complex than MNIST because of the distractors.

DATA DESCRIPTION The SVHN is a real-world image dataset for developing machine learning and object recognition algorithms with the minimal requirement on data formatting but comes from a significantly harder, unsolved, real-world problem (recognising digits and numbers in natural scene images). SVHN is obtained from house numbers in Google Street View images. Where the labels for each of this image are the prominent number in that image i.e. 2,6,7 and 4 respectively. The dataset is availaible in the form of h5py files. One can read about this file format here: http://docs.h5py.org/en/stable/high/dataset.html

Acknowledgement: Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, Andrew Y. Ng Reading Digits in Natural Images with Unsupervised Feature Learning NIPS Workshop on Deep Learning and Unsupervised Feature Learning 2011. PDF http://ufldl.stanford.edu/housenumbers as the URL for this site when necessary

OBJECTIVE Build a digit classifier on the SVHN (Street View Housing Number) dataset.

Steps and tasks

1. Importing the data.
2. Data pre-processing and visualisation.
3. Designing, training, tuning and testing a neural network image classifier.
4. Plotting the training loss, validation loss vs number of epochs and training accuracy, validation accuracy vs number of epochs plot.