Deep Learning with Python for Image Classification

Learn Deep Learning & Computer Vision for Image Classification using Pre-trained Models with Python using Google Colab
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Rating: 4.2 out of 5 (16 ratings)
2,949 students
44min of on-demand video
English
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Learn Image Classification using Deep Learning PreTrained Models
Learn Single-Label Image Classification and Multi-Label Image Classification
Learn Deep Learning Architectures Such as ResNet and AlexNet
Write Python Code in Google Colab
Connect Colab with Google Drive and Access Data
Perform Data Preprocessing using Transformations
Perform Single-Label Image Classification with ResNet and AlexNet
Perform Multi-Label Image Classification with ResNet and AlexNet

Requirements

  • Python and Pytorch required Deep Learning skills are taught in this course
  • A Google Gmail account to get started with Google Colab to write Python Code

Description

In this course, you will learn Deep Learning with Python and PyTorch for Image Classification using Pre-trained Models. Image Classification is a computer vision task to recognize an input image and predict a single-label or multi-label for the image as output using Machine Learning techniques.

  • You will use Google Colab notebooks for writing the python code for image classification using Deep Learning models.

  • You will learn how to connect Google Colab with Google Drive and how to access data.

  • You will perform data preprocessing using different transformations such as image resize and center crop etc.

  • You will perform two types of Image Classification, single-label Classification, and multi-label Classification using deep learning models with Python.

In single-label Cassification, when you feed input image to the network it predicts single label. In multi-label Classification, when you feed input image to the network it predicts multiple labels.  You will Learn Deep Learning architectures such as ResNet and AlexNet. The ResNet is a deep convolution neural network proposed for image classification and recognition. ResNet network architecture designed for classification task, trained on the imageNet dataset of natural scenes that consists of 1000 classes. Deep residual nets won the 1st place on the ILSVRC 2015 Classification challenge. Alexnet is a deep convolution neural network trained on ImageNet dataset to classify the images into 1000 classes. It has five convolution layers followed by max-pooling layers, and 3 fully connected layers. AlexNet won the ILSVRC 2012 Classification challenge. You will perform image classification using ResNet and AlexNet deep learning models. The Deep Learning community has greatly benefitted from these open-source models where pre-trained models are a major reason for rapid advancements in the Computer Vision and deep learning research.


Who this course is for:

  • Deep Learning enthusiasts interested to learn with Python and Pytorch
  • Students and researchers interested in Deep Learning for Image Classification

Instructors

Learn Deep Learning, Computer Vision, & Python Courses
Computer Science & AI School
  • 4.1 Instructor Rating
  • 257 Reviews
  • 8,223 Students
  • 3 Courses

Computer Science & AI School aims to open the door to sought-after technology careers for you by learning cutting edge Computer Science courses in Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), Computer Vision (CV), Data Science (DS), Programming and Databases with course material ranges from entry-level  to specialized topics. You will be able to update your marketable and competitive skills through commercial applications of computing practices. You’ll master in-demand computing skills, solve complex problems, and hone your innovation and creativity. The hands-on exercises and project-based approach will help develop the technical and transferable skills needed for a fulfilling career in your field.

Computer Science Lecturer and Programmer
Mazhar Hussain
  • 4.2 Instructor Rating
  • 263 Reviews
  • 8,894 Students
  • 4 Courses

Mazhar Hussain is teaching Computer Science courses since 2015 at the National University of Computer and Emerging Sciences.  He holds a Master's Degree in Computer Science and is passionate to deliver practical knowledge and skills to his students.  He has been teaching courses in Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), Computer Vision (CV), Data Science (DS), Programming, and Databases especially SQL SERVER, MYSQL, ORACLE, and MS ACCESS for more than 5 years span. He has been working as a developer in the Microsoft Innovation Center and is now taking all that he has learned to help you discover amazing career opportunities. Please do not hesitate if you have any questions, I am always available for your help at any time to transform a passionate, enthusiastic learner into a skilled person.

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