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20-06-2021, 10:13

Convolutional Neural Networks for Image Classification

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Convolutional Neural Networks for Image Classification
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 48 lectures (15h 41m) | Size: 13 GB




Deep CNN network for accurate image recognition: design, train and test
What you'll learn:
Assemble own, custom dataset for Classification tasks
Modify existing dataset for Classification tasks
Apply preprocessing techniques for dataset before training
Design deep CNNs architectures with high accuracy results
Train deep CNNs in Keras
Classify new images after training
Demonstrate classification in Real Time by camera
Generate synthetic data to augment existing dataset
Course content

Requirements
Basic knowledge of Image Classification Algorithms
Basics on how CNN works
Intermediate knowledge of Python V3
Basic knowledge of OpenCV
Basic knowledge of Tensorflow
Basics on how to use Anaconda Environments
Basics on how to code in Jupyter Notebook

Description
In this practical course, you'll design, train and test your own Convolutional Neural Network (CNN) for the tasks of Image Classification.

By the end of the course, you'll be able to build your own applications for Image Classification.

At the beginning, you'll implement convolution, pooling and combination of these two operations to grayscale images by the help of different filters, pure Numpy library and 'for' loops.

After that, you'll assemble images together, compose custom dataset for classification tasks and save created dataset into a binary file.

Next, you'll convert existing dataset of Traffic Signs into needed format for classification tasks and save it into a binary file.

Then, you'll apply preprocessing techniques before training, produce and save processed datasets into separate binary files.

At the next step, you'll construct CNN models for classification tasks, select needed number of layers for accurate classification and adjust other parameters.

When the models are designed and datasets are ready, you'll train constructed CNNs, test trained models on completely new images, classify images in Real Time by camera and visualize training process of filters from randomly initialized to finally trained.

At the final step, you'll pass Practice Test according to the all learned material during the course.

As a bonus part, you'll generate up to 1 million additional images and extend prepared dataset by new images via image rotation, image projection and brightness changing.

The main goal of the course is to develop and improve your hard skills in order to apply them for real problems of Image Classification based on Convolutional Neural Networks.

Every lecture of the course has SMART objectives. It means, that you can track your progress and witness practical results within the visible time frame, right after the end of the lecture.

S - specific (the lecture has specific objectives)

M - measurable (results are reasonable and can be quantified)

A - attainable (the lecture has clear steps to achieve the objectives)

R - result-oriented (results can be obtained by the end of the lecture)

T - time-oriented (results can be obtained within the visible time frame)

Who this course is for
Students who want to build complete application for Image Classification with CNN
Students who want to improve their hard skills on Image Classification with CNN before their next interview for internship or dream job
Students who want to use CNN with their Own Data for Image Classification but don't know where to start
Young Researchers who study different Image Classification Algorithms and want to Train CNN with Custom Data and Compare results with other approaches
Students who know basics of Image Classification but want to know how to Train CNN with New Data
Students who study Computer Vision and want to know how to use CNN for Image Classification
Students who work on project of safety driven and want to Classify Traffic Signs with CNN
Students who develop alarm-warning system for driver and need to Classify Traffic Signs




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