Udemy - Convolution Neural Network with Data Augmentation
- CategoryOther
- TypeTutorials
- LanguageEnglish
- Total size693.5 MB
- Uploaded Byfreecoursewb
- Downloads26
- Last checkedFeb. 20th '22
- Date uploadedFeb. 18th '22
- Seeders 3
- Leechers7
Infohash : 9284BB78D578D65B1241A467C081B648A45A11BF
Convolution Neural Network with Data Augmentation 
https://DevCourseWeb.com
Genre: eLearning | MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 693 MB | Duration: 2h 22m
In this course, you will learn about Convolution Neural Network, Various CNN Architectures, Object Detection and HoG
What you'll learn
CNN
Requirements
Knowledge of Basic Python
Description
Neural Networks is the general term that is used for brain like connections. Convolutional Neural Network are the Networks that are specially designed for reading pixel values from Images and learn from it. CNN are the subset of Neural Networks. just like all types of water are liquid but not every liquid is water.
Files:
[ DevCourseWeb.com ] Udemy - Convolution Neural Network with Data Augmentation- Get Bonus Downloads Here.url (0.2 KB) ~Get Your Files Here ! 1. Introduction
- 1. Introduction.mp4 (52.2 MB)
- 1. Introduction.srt (11.1 KB)
- 1.1 Part1.mp4 (23.5 MB)
- 2. Introduction Part2.mp4 (23.3 MB)
- 2. Introduction Part2.srt (6.0 KB)
- 3. Flatten.mp4 (11.1 MB)
- 3. Flatten.srt (2.9 KB)
- 1. LeNET.mp4 (16.4 MB)
- 1. LeNET.srt (5.4 KB)
- 2. AlexNET.mp4 (29.1 MB)
- 2. AlexNET.srt (7.3 KB)
- 3. GoogLeNet.mp4 (46.7 MB)
- 3. GoogLeNet.srt (10.3 KB)
- 4. Resnet.mp4 (34.9 MB)
- 4. Resnet.srt (9.2 KB)
- 1. Object Detection Introduction and History.mp4 (14.1 MB)
- 1. Object Detection Introduction and History.srt (2.6 KB)
- 2. Histogram of Gradients (HoGs).mp4 (30.1 MB)
- 2. Histogram of Gradients (HoGs).srt (4.4 KB)
- 3. Applications of Object Detection, Algorithm of OD and Localization.mp4 (13.9 MB)
- 3. Applications of Object Detection, Algorithm of OD and Localization.srt (3.0 KB)
- 1. MNIST Case Study - Number Classification.mp4 (54.4 MB)
- 1. MNIST Case Study - Number Classification.srt (7.3 KB)
- 1. Kernel, Depth, Stride and Padding.mp4 (57.2 MB)
- 1. Kernel, Depth, Stride and Padding.srt (18.5 KB)
- 2. Pooling Layer.mp4 (35.3 MB)
- 2. Pooling Layer.srt (9.4 KB)
- 1. Condition 1 - No Stride and No Padding.mp4 (7.4 MB)
- 1. Condition 1 - No Stride and No Padding.srt (2.4 KB)
- 2. Condition 2 - Padding.mp4 (5.4 MB)
- 2. Condition 2 - Padding.srt (2.3 KB)
- 3. Condition 3 - Padding and Stride.mp4 (4.5 MB)
- 3. Condition 3 - Padding and Stride.srt (2.1 KB)
- 1. CNN Architectures.mp4 (10.6 MB)
- 1. CNN Architectures.srt (3.4 KB)
- 1. Dropout Layer.mp4 (27.4 MB)
- 1. Dropout Layer.srt (7.7 KB)
- 1. Practical Exercise 1.mp4 (20.4 MB)
- 1. Practical Exercise 1.srt (3.9 KB)
- 2. Practical Exercise 2.mp4 (69.5 MB)
- 2. Practical Exercise 2.srt (11.2 KB)
- 1. Training CNN - Introduction.mp4 (7.5 MB)
- 1. Training CNN - Introduction.srt (2.3 KB)
- 2. Activation Layers & ReLU.mp4 (4.2 MB)
- 2. Activation Layers & ReLU.srt (1.7 KB)
- 3. Softmax Activation Layer.mp4 (4.8 MB)
- 3. Softmax Activation Layer.srt (1.5 KB)
- 4. Pooling Layer.mp4 (3.7 MB)
- 4. Pooling Layer.srt (1.4 KB)
- 5. Fully Connected Layer.mp4 (3.0 MB)
- 5. Fully Connected Layer.srt (0.9 KB)
- 1. Designing CNN.mp4 (9.4 MB)
- 1. Designing CNN.srt (3.3 KB)
- 1. Data Augmentation.mp4 (4.4 MB)
- 1. Data Augmentation.srt (1.9 KB)
- 2. Research Paper 1.mp4 (21.0 MB)
- 2. Research Paper 1.srt (1.8 KB)
- 3. Research Paper 2.mp4 (24.0 MB)
- 3. Research Paper 2.srt (2.0 KB)
- 4. Research Paper 3.mp4 (23.9 MB)
- 4. Research Paper 3.srt (2.3 KB)
- Bonus Resources.txt (0.4 KB)
Code:
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