• DeepDream
• Data augmentation
• VGG
• Inception
• Data augmentation
• Con2D
• MaxPooling2D
• EarlyStopping
• Matplotlib
• Confusion matrix
• Pandas
• Numpy
• MinMaxScaler
• Google Colab
• Deep Learning.
• Training Neural Network.
• Splitting Data into Training Set and Test Set.
• Testing Accuracy.
• Confusion Matrix.
• Make a Prediction.
• Model compilation.

Interested in image processing? Then this course is for you!

This is currently the most comprehensive course in the market about convolutional neural networks. The course will guide you from zero to hero on a convolutional neural network which is mostly not covered in any other courses.

This course is built in a very practical way as there are lots of projects for you to practice along the way. So you will have lots of projects in your portfolio to show to your potential employers or clients

The course is split into 4 major parts:

1. Convolutional Neural Network fundamental

2. CIFAR-10 project

3. Clothing image project

4. Advanced implementation of CNN

PART 1: Convolutional Neural network fundamental

In this section, you will learn about the fundamental of the convolutional neural network. This is the first section so there will not be any advanced concept about CNN. This is just an introduction to what a convolutional neural network looks like, and what libraries we will be using. We will also implement a simple CNN model so you will learn how to build it with a detailed explanation step-by-step

PART 2: CIFAR-10 project

In this section, you will apply what will we have learned so far in the course to build a model for big dataset images. A convolution neural network is mostly used for image processing. This project will help us to reinforce what we have learned so far in the course. Furthermore, it will help us to combine the knowledge together to build a model for the big dataset.

PART 3: Clothing image project

This is another project for you to practice.  Similar to the CIFAR-10 project, this project will have you hands-on practice with detailed explanations step-by-step.

PART 4: Advanced implementation of CNN.

In this section, we will learn some of the advanced tools and libraries in CNN which are not covered in any other courses.  VGG, Inception network and the deep dream network will be introduced in this section. We will also implement  VGG, Inception network, and the deep dream network in the project “combining two images”.  Furthermore we will also learn how to improve the result in this section.

Who this course is for:

• Anyone interested in Deep Learning, Machine Learning and Artificial Intelligence
• Students who have at least high school knowledge in math and who want to start learning Machine Learning, Deep Learning, and Artificial Intelligence
• Any data analysts who want to level up in Machine Learning, Deep Learning and Artificial Intelligence.
• Anyone passionate about Artificial Intelligence
• Data Scientists who want to take their AI Skills to the next level