The Complete Convolutional Neural Network with Python 2022

Name: The Complete Convolutional Neural Network with Python 2022
Rating: 4.3 (16 reviews)

Deep Learning Convolutional Neural Network (CNN) - Keras & TensorFlow 2

Created byHoang Quy La

Last updated 8/2024

English

What you'll 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

Course content

7 sections • 41 lectures • 7h 33m total length

Course structure1:45
Describe the course structure for the complete convolutional neural network with Python 2022, covering introduction, cnn fundamentals, clothing image project, and advanced topics like inception and deep rem.
Tools will be used in this course1:35
How to make the most out of this course1:52
Watch all videos and follow along with the code to grasp the solution steps. Use the Q&A to verify topics and sharpen your understanding as you progress.

Introduction to Convolutional Neural Network Part 117:20
Introduction to Convolutional Neural Network Part 28:22
Implementing Simple CNN model Part 113:09
Implementing Simple CNN model Part 211:32
Implementing Simple CNN model Part 317:37
Implementing Simple CNN model Part 46:30
Build and evaluate a simple cnn model, plot predictions versus actuals on the test set, and demonstrate good accuracy and loss.
Implementing Simple CNN model Final Part4:51
Explore how a simple convolutional neural network model uses convolutions and max pooling to form a feature extractor, followed by a fully connected classifier, achieving 97% accuracy on the dataset.

Clothing image Project Part 112:35
Clothing image Project Part 29:53
Form a cnn model with a feature extractor of convolutional and pooling layers and a classify backend, reshaping grayscale 28x28 images for a TensorFlow sequential model.
Clothing image Project Part 318:28
Clothing image Project Part 418:51
Clothing image Project Part 59:41
Clothing image Project Part 618:45
Clothing image Project Part 78:05
Clothing image Project Final Part17:46
Build a clothing image classifier with a compact CNN featuring two convolutional layers, 32 and 64 filters, max pooling, and a dense output, and apply horizontal flips for data augmentation.

Introduction to Pytorch12:27
Explore PyTorch, an open-source deep learning framework, for building and training neural networks with a dynamic computation graph, tensor operations, autograd, and Pythonic deployment with ONNX.
Introduction to YOLO4:58
What is image segmentation7:32
Understand image segmentation, including semantic, instance, and panoptic approaches, with applications in autonomous vehicles and editing, and methods from thresholding to deep learning models like cnn and mask r-cnn.
Introduction to OpenCV7:02
YOLO Implementation7:02
Introduction to Faster-RCNN9:01
Faster-RCNN Implementation Part 18:52
Faster-RCNN Implementation Final Part19:28
What is Mask-RCNN10:07
Mask R-CNN Implementation9:29

Requirements

Basic Python and machine learning knowledge is required

Description

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.

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

The Complete Convolutional Neural Network with Python 2022

What you'll learn

Explore related topics

Course content

Introduction3 lectures • 5min

Convolutional Neural Network (CNN) Fundamental7 lectures • 1hr 19min

CIFAR-10 Project5 lectures • 55min

Clothing Image Project8 lectures • 1hr 54min

Advanced Implementation of CNN7 lectures • 1hr 43min

Introduction to OpenCV, Mask R-CNN, Faster R-CNN and YOLO (Updated 2024)10 lectures • 1hr 36min

Thank you1 lecture • 1min

Requirements

Description

Who this course is for: