Hands On Python Course For Deep Learning In Computer Vision

Python Programming; Deep Learning; Computer Vision; CNNs; Caltech-101; MNIST Digits;

What you'll learn

Build knowledge on deep learning programming for computer vision tasks

Be introduced to Google Colab and Python Programming

Augment research enterprise

Update their professional skills to seek a better career in AI companies

Requirements

Basic knowhow of Python programming

Description

By completing this course, the learner has a smart hands-on learning from an seasoned academic instructor with more than 10 years of experience teaching UG and PG Engineering students. The course features topics like building CNNs from scratch, importing state-of-the-art pre-trained CNNs, data augmentation, transfer learning, etc. for demonstrating Python programming on popular computer vision datasets like Caltech-101,  and MNIST Digits, among others.The course begins with an intro to deep learning and its phenomenal success. The course immeditaely proceeds to hands-on training on the Cats versus Dogs Image dataset binary classification task, by giving a fluid transition into the seemingly overwhelming environment of python programming by making it lucid and simple by many orders.As I have learned from teaching many batches of UG and PG engineering students, a soft and friendly tone coupled with intelligent and smart-teaching based explanation is the key to making students go step-by-step up the ladder of professional excellence.This course is about fantasy coupled with ambition, all combined in easy to undertsand and friendly teaching style to help you ace the deep learning programming scenario using powerful languages like Python. Of course, the concepts developed can be applied to any other scenario, by proper understanding of the course.

Overview

Section 1: Introduction

Lecture 1 Welcome and Introduction

Section 2: Task 1, Part 1: The Cats v Dogs Binary Classification Computer Vision Task

Lecture 2 Task 1, Part 1

Lecture 3 Task 1, Part 2

Lecture 4 Task 1, Part 3

Section 3: Task 2: Using Python for classifying the MNIST Computer Vision Task

Lecture 5 Task 2, Lecture 1

Lecture 6 Task 2, Lecture 2

Section 4: Task 3: Using Python for transfer learning on the Caltech-101 Vision Dataset

Lecture 7 Task 3, Part 1

Lecture 8 Task 3, Part 2

AI students, research scholars, academicians, working professionals, job seekers.