Convolutional Neural Networks for Image Classification

LeeAndro · May 26, 2022

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

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

Deep CNN network for accurate image recognition: design, train and test

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 by camera

Generate synthetic data to augment existing dataset

Course content

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

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

At the bning, 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 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 chag.

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

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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Convolutional Neural Networks for Image Classification

LeeAndro

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