Identifying Diseases in Plants with Image Categorization in Edge Devices

ACF Type: date_picker

Start Date: April 1, 2022

ACF Type: text

Course duration: 15 hours

ACF Type: radio

Cost: donation

ACF Type: checkbox

Skill level: advanced

Course Description

For whom is this course

ACF Type: wysiwyg

This course is a suitable choice for beginners in Deep Learning and Computer Vision. It encompasses the rudiments of Neural Networks, Convolutions and the essential math concepts with a real-world case study.

Objective

The aim is to share knowledge in the field of Computer Vision with the assistance of an end-to-end pipeline starting with Data processing till preparing a deep learning model for edge deployment. 

  • How artificial Neuron functions and a Neural Network is formed
  • Understanding the learning  process of Neural Networks
  • Optimization of  model training
  • The idea of Pre-trained models and Transfer Learning
  • Implementation of an Image Classifier in keras(Tensorflow) and preparing the model for edge deployment

What you will learn

ACF Type: wysiwyg

  • A complete understanding of the entire learning process in CNN
  • How to preprocess the images to increase the training samples
  • Good knowledge of model implementation in keras(tensorflow)
  • Entire pipeline understanding for edge device deployment

Prerequisites

ACF Type: wysiwyg

Python basics(nice to have)

Syllabus

ACF Type: wysiwyg

Session 1: Introduction to Deep Learning

  • Basics of Machine Learning & Deep Learning
  • Why Deep Learning
  • Linear and Nonlinear functions
  • What are learnable parameters?
  • Forward propagation and Backward pass in DL
  • Loss function and its significance
  • Implementation of a simple Neural Network

Session 2: Enhancing the Learning process

  • Bias-Variance Tradeoff
  • What is Underfitting and Overfitting
  • Various optimizers for deep learning
  • Implementation of an Image Classifier using FNN(keras)

Session 3: Convolution Neural Networks

  • What are CNNs and Why they are introduced
  • Filters, Channels, Pooling layers
  • Learning process in CNNs
  • Data Augmentation
  • Pretrained Models
  • Transfer Learning
  • Implementation of an Image Classifier using CNN(keras)

Session 4: Real-world case study (Edge Deployment)

  • What are edge devices
  • Model Quantization and the significance
  • Different Quantization Techniques
  • Building an Image Classifier to Identify disease in the plants
  • Model Conversion to TFlite format
  • Edge Compiler and quantized model

Course Features

ACF Type: text

Lectures: Updating

ACF Type: text

Duration: 15 hours

ACF Type: text

Students: 35-40

ACF Type: radio

Certificate: yes

ACF Type: radio

Cost: donation

ACF Type: checkbox

Skill level: advanced

Video

ACF Type: oembed

Instructor

ACF Type: image

h

ACF Type: text

Amal Mathew

ACF Type: url

ACF Type: textarea

Product Owner, Lead ML Engineer & Chapter Lead @Omdena

Upcoming Courses

JOIN OUR NEWSLETTER

Want to build the skills that matter? Never miss an Omdena Course.

Omdena | Building AI Solutions for Real-World Problems