Deep Learning for Image Classification in Python with CNN
Convolutional Neural Networks for Computer Vision With Keras and TensorFlow on Google Colab Platform
Note: 0/5 (0 notes) 528 students
Instructor(s): Karthik K
Last update: None
What you’ll learn
- Understand the fundamentals of Convolutional Neural Networks (CNNs)
- Build and train a CNN using Keras with Tensorflow as a backend using Google Colab
- Assess the performance of trained CNN
- Learn to use the trained model to predict the class of a new set of image data
- Basic knowledge of Python Programming
Welcome to the “Deep Learning for Image Classification in Python with CNN” course. In this course, you will learn how to create a Convolutional Neural Network (CNN) in Keras with a TensorFlow backend from scratch, and you will learn to train CNNs to solve Image Classification problems. Please note that you don’t need a high-powered workstation to learn this course. We will be carrying out the entire project in the Google Colab environment, which is free. You only need an internet connection and a free Gmail account to complete this course. This is a practical course, we will focus on Python programming, and you will understand every part of the program very well. By the end of this course, you will be able to build and train the convolutional neural network using Keras with TensorFlow as a backend. You will also be able to visualise data and use the model to make predictions on new data. This image classification course is practical and directly applicable to many industries. You can add this project to your portfolio of projects which is essential for your following job interview. This course is designed most straightforwardly to utilize your time wisely.
How much does an Image Processing Engineer make in the USA? (Source: Talent)
The average image processing engineer salary in the USA is $125,550 per year or $64.38 per hour. Entry-level positions start at $102,500 per year, while most experienced workers make up to $174,160 per year.
Who this course is for
- Beginners starting out to the field of Deep Learning
- Industry professionals and aspiring data scientists
- People who want to know how to write their image classification code
- Artificial Intelligence
- Machine Learning
- Deep Learning
- Artificial Neural Networks (Conventional / Traditional)
- Backward Propagation of Errors
- Gradient Descent
- Stochastic Gradient Descent
- Convolutional Neural Networks (CNN)
- Input Layer, Convolutional Layer
- Pooling Layer, Activation Function Layer
- Fully Connected Layers / Dense Layer, Dropout Layer
- Image Classification and its Applications
- How image classification is done?
- Transfer Learning
- Architecture of ResNet (Residual Networks)
- Building, Evaluating and Predicting Image Classification Model
- Download Dataset
- What is inside train folder?
- What is the .hdf5 file?
- What is inside test folder?
- What is inside our_prediction folder?
- Image Classification Python Code
- Enabling GPU in Google Colab
- Is GPU connected to Colab notebook?
- Download TensorFlow and CUDA
- Compare the Speed of GPU with CPU
- Connect Google Colab with Google Drive
- Check the Number of Images in the Dataset
- Image Augmentation
- Transfer Learning
- Fine Tuning / Freezing of the Layers
- Model Compilation
- Callbacks: EarlyStopping
- Callbacks: ModelCheckpoint
Time remaining or 472 enrolls left
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