The Complete Convolutional Neural Network with Python 2022

The Complete Convolutional Neural Network with Python 2022, available at $44.99, has an average rating of 5, with 41 lectures, based on 13 reviews, and has 95 subscribers.

You will learn about 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. YOLO OpenCV Faster R-CNN Mask R-CNN Pytorch This course is ideal for individuals who are Anyone interested in Deep Learning, Machine Learning and Artificial Intelligence or Students who have at least high school knowledge in math and who want to start learning Machine Learning, Deep Learning, and Artificial Intelligence or Any data analysts who want to level up in Machine Learning, Deep Learning and Artificial Intelligence. or Anyone passionate about Artificial Intelligence or Data Scientists who want to take their AI Skills to the next level It is particularly useful for Anyone interested in Deep Learning, Machine Learning and Artificial Intelligence or Students who have at least high school knowledge in math and who want to start learning Machine Learning, Deep Learning, and Artificial Intelligence or Any data analysts who want to level up in Machine Learning, Deep Learning and Artificial Intelligence. or Anyone passionate about Artificial Intelligence or Data Scientists who want to take their AI Skills to the next level.

Enroll now: The Complete Convolutional Neural Network with Python 2022

Summary

Title: The Complete Convolutional Neural Network with Python 2022

Price: $44.99

Average Rating: 5

Number of Lectures: 41

Number of Published Lectures: 41

Number of Curriculum Items: 41

Number of Published Curriculum Objects: 41

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

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.
YOLO
OpenCV
Faster R-CNN
Mask R-CNN
Pytorch

Who Should Attend

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

Target Audiences

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

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:

Convolutional Neural Network fundamental
CIFAR-10 project
Clothing image project
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.

PART 5: Introduction to OpenCV, Mask R-CNN, Faster R-CNN and YOLO.

In this section, we will learn some of the advanced tools and libraries in CNN which are not covered in any other courses. OpenCV, Mask R-CNN and the Faster R-CNN will be introduced in this section. We will also learn what these tools are and why we need to use them. We will also implement Faster R-CNN, Mask R-CNN and YOLO by doing coding activities.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Course structure

Lecture 2: Tools will be used in this course

Lecture 3: How to make the most out of this course

Chapter 2: Convolutional Neural Network (CNN) Fundamental

Lecture 1: Introduction to Convolutional Neural Network Part 1

Lecture 2: Introduction to Convolutional Neural Network Part 2

Lecture 3: Implementing Simple CNN model Part 1

Lecture 4: Implementing Simple CNN model Part 2

Lecture 5: Implementing Simple CNN model Part 3

Lecture 6: Implementing Simple CNN model Part 4

Lecture 7: Implementing Simple CNN model Final Part

Chapter 3: CIFAR-10 Project

Lecture 1: CIFAR-10 project Implementation Part 1

Lecture 2: CIFAR-10 project Implementation Part 2

Lecture 3: CIFAR-10 project Implementation Part 3

Lecture 4: CIFAR-10 project Implementation Part 4

Lecture 5: CIFAR-10 project Implementation Final Part

Chapter 4: Clothing Image Project

Lecture 1: Clothing image Project Part 1

Lecture 2: Clothing image Project Part 2

Lecture 3: Clothing image Project Part 3

Lecture 4: Clothing image Project Part 4

Lecture 5: Clothing image Project Part 5

Lecture 6: Clothing image Project Part 6

Lecture 7: Clothing image Project Part 7

Lecture 8: Clothing image Project Final Part

Chapter 5: Advanced Implementation of CNN

Lecture 1: Combining 2 images Part 1

Lecture 2: Introduction to VGG (Visual Geometry Group)

Lecture 3: Introduction to inception networks

Lecture 4: Combining 2 images Part 2

Lecture 5: Combining 2 images Final Part

Lecture 6: Improving the result Part 1

Lecture 7: Improving the result Final Part

Chapter 6: Introduction to OpenCV, Mask R-CNN, Faster R-CNN and YOLO (Updated 2024)

Lecture 1: Introduction to Pytorch

Lecture 2: Introduction to YOLO

Lecture 3: What is image segmentation

Lecture 4: Introduction to OpenCV

Lecture 5: YOLO Implementation

Lecture 6: Introduction to Faster-RCNN

Lecture 7: Faster-RCNN Implementation Part 1

Lecture 8: Faster-RCNN Implementation Final Part

Lecture 9: What is Mask-RCNN

Lecture 10: Mask R-CNN Implementation

Chapter 7: Thank you

Lecture 1: Thank You

Instructors

Hoang Quy La
Electrical Engineer

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2 stars: 0 votes
3 stars: 1 votes
4 stars: 0 votes
5 stars: 12 votes

Frequently Asked Questions

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You can view and review the lecture materials indefinitely, like an on-demand channel.

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