Enforce VGG from Start with Patchich – Deep Learning Theory

Visual Geometry Group (VGG) is one of the most influential compulsive nerve networks in computer vision. It is a deep sculpture that is known for the simple, uniform use of small 3×3 filters decorated in continuity, which enables the identification and characteristic of a powerful image.

We just published a course just Freecodecamp.org The YouTube channel, which specializes in the ideology, mathematics, and design principles, will teach you to rebuild the VGG architecture that has created it. Muhammad al -Abrah formed this course.

This course detects the beginning and philosophy of the VGG, breaks the mathematics of the conductivity, and compares VGG’s design with its peer architecture, while building modular, transparent enforcement in the pirate. You will have practical experience with data handling, change, and concept in Google Kolab, and will use tools such as flashlight info, metaphotleb, and CNN statement to analyze and interpret your models. This course includes a complete training loop with direct damage curves, fine toning, and many opportunities to experience and view results.

The full list of parts in the course is:

• Welcome and Review of VGG Atlas

• Philosophy behind VGG: Depth with simplicity

• Historical origin and construction stimulus

• The caravan of the caravan in VGG

• Design Rule: Uniformity and Depth

• Combined comparison: VGG vs modern architecture

• Training Strategy: Improve VGG Model

• Detecting the technique of promoting data

• VGG in transfer learning requests

• The techniques of concept and interpretation

• VIGG Different Conditions: A family of deep nets

• Hand on Walk Throw: Practical Applications

• VGG Economic System and Research Resources

• Kicking practical labs in Google Kolab

• Compiling your coding environment

• Tiny VGG: Model construction from the beginning

• Import of necessary libraries

• Loading and developing data in Google Kolab

• To be familiar with data folders and files

• Directory route for data

• Become one with data

• To imagine sampling images with metadata

• Imagining images in azagar using Napa and Metaplatlib

• Changing data

• To imagine converted data with Petroch

• Changing data with torchvision.transforms

• Loading data using ImageFolder

• Changing filled photos into a datander

• To imagine some sample pictures

• Construction of VGG model and starting a structure using CNN Explaining Tool

• CANNE COTE TOILL VGG Model in Google Kolab using Code

• To present an example from the VGG model

• Exposure and summarizing the VGG model

• Dummy Forward Pass using the same image

• Using torchinfo To understand the input/output shapes in the model

• Summary of the model

• Training and making a test loop

• Create a function to combine training and testing measures

• Calling the training function

• Model Training: Running the training phase

• Reading results, fine toning, and improve hyperpressors

• Plutting damage curves and fine toning with different settings

View the full course Freecodecamp.org YouTube channel (5 hours clock)

https://www.youtube.com/watch?v=rhciu4aw_w