├── All-Codes--master.zip ├── LICENSE ├── README.md ├── SECTION 4 ├── Gradient-Descent-Excel-Simulation.xlsx └── Weird Results.docx ├── SECTION 11 ├── 2nd Optional Paper to Read.txt ├── Images-2.zip └── Images.zip ├── SECTION 12 ├── CIFAR 10.docx ├── Convolutional Neural Networks.docx ├── Deep Neural Networks.docx ├── Linear Regression.docx ├── Logistic Regression.docx ├── MNIST Classification.docx ├── Style Transfer.docx └── Transfer Learning.docx ├── SECTION 14 ├── Appendix C01 Intro.docx └── NOTE.txt ├── SECTION 15 └── NOTE.txt ├── SECTION 7 ├── Important Update - Bug fix.txt └── Test Links.txt └── SECTION 8 └── ants_and_bees-master.zip /All-Codes--master.zip: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/PyTorch-for-Deep-Learning-and-Computer-Vision/8bad829e7f67ffec102659a421e43359c243493d/All-Codes--master.zip -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2019 Packt 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | # PyTorch-for-Deep-Learning-and-Computer-Vision 5 | Code Repository for PyTorch for Deep Learning and Computer Vision, published by Packt 6 | -------------------------------------------------------------------------------- /SECTION 4/Gradient-Descent-Excel-Simulation.xlsx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/PyTorch-for-Deep-Learning-and-Computer-Vision/8bad829e7f67ffec102659a421e43359c243493d/SECTION 4/Gradient-Descent-Excel-Simulation.xlsx -------------------------------------------------------------------------------- /SECTION 4/Weird Results.docx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/PyTorch-for-Deep-Learning-and-Computer-Vision/8bad829e7f67ffec102659a421e43359c243493d/SECTION 4/Weird Results.docx -------------------------------------------------------------------------------- /SECTION 11/2nd Optional Paper to Read.txt: -------------------------------------------------------------------------------- 1 | 2nd Optional Paper to Read 2 | The following paper outlines key topics behind style extraction, a concept that is introduced in the next video. Feel free to scan through it if you wish to learn about this phenomenal research! 3 | 4 | Texture Synthesis using Convolutional Neural Networks -------------------------------------------------------------------------------- /SECTION 11/Images-2.zip: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/PyTorch-for-Deep-Learning-and-Computer-Vision/8bad829e7f67ffec102659a421e43359c243493d/SECTION 11/Images-2.zip -------------------------------------------------------------------------------- /SECTION 11/Images.zip: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/PyTorch-for-Deep-Learning-and-Computer-Vision/8bad829e7f67ffec102659a421e43359c243493d/SECTION 11/Images.zip -------------------------------------------------------------------------------- /SECTION 12/CIFAR 10.docx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/PyTorch-for-Deep-Learning-and-Computer-Vision/8bad829e7f67ffec102659a421e43359c243493d/SECTION 12/CIFAR 10.docx -------------------------------------------------------------------------------- /SECTION 12/Convolutional Neural Networks.docx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/PyTorch-for-Deep-Learning-and-Computer-Vision/8bad829e7f67ffec102659a421e43359c243493d/SECTION 12/Convolutional Neural Networks.docx -------------------------------------------------------------------------------- /SECTION 12/Deep Neural Networks.docx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/PyTorch-for-Deep-Learning-and-Computer-Vision/8bad829e7f67ffec102659a421e43359c243493d/SECTION 12/Deep Neural Networks.docx -------------------------------------------------------------------------------- /SECTION 12/Linear Regression.docx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/PyTorch-for-Deep-Learning-and-Computer-Vision/8bad829e7f67ffec102659a421e43359c243493d/SECTION 12/Linear Regression.docx -------------------------------------------------------------------------------- /SECTION 12/Logistic Regression.docx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/PyTorch-for-Deep-Learning-and-Computer-Vision/8bad829e7f67ffec102659a421e43359c243493d/SECTION 12/Logistic Regression.docx -------------------------------------------------------------------------------- /SECTION 12/MNIST Classification.docx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/PyTorch-for-Deep-Learning-and-Computer-Vision/8bad829e7f67ffec102659a421e43359c243493d/SECTION 12/MNIST Classification.docx -------------------------------------------------------------------------------- /SECTION 12/Style Transfer.docx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/PyTorch-for-Deep-Learning-and-Computer-Vision/8bad829e7f67ffec102659a421e43359c243493d/SECTION 12/Style Transfer.docx -------------------------------------------------------------------------------- /SECTION 12/Transfer Learning.docx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/PyTorch-for-Deep-Learning-and-Computer-Vision/8bad829e7f67ffec102659a421e43359c243493d/SECTION 12/Transfer Learning.docx -------------------------------------------------------------------------------- /SECTION 14/Appendix C01 Intro.docx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/PyTorch-for-Deep-Learning-and-Computer-Vision/8bad829e7f67ffec102659a421e43359c243493d/SECTION 14/Appendix C01 Intro.docx -------------------------------------------------------------------------------- /SECTION 14/NOTE.txt: -------------------------------------------------------------------------------- 1 | Note 2 | Throughout the PyTorch course, we will rarely work with NumPy arrays. However, I've included this NumPy crash course in case you ever get stuck on something that includes NumPy. 3 | 4 | Cheers, 5 | 6 | Rayan -------------------------------------------------------------------------------- /SECTION 15/NOTE.txt: -------------------------------------------------------------------------------- 1 | Intro 2 | This section of the appendix is purely optional, as it simply explains the softmax function that was mentioned in the MNIST Image Recognition Section (Section 7). -------------------------------------------------------------------------------- /SECTION 7/Important Update - Bug fix.txt: -------------------------------------------------------------------------------- 1 | Important Update - Bug fix 2 | Hello all, 3 | 4 | There's been an update to the code which applies to the MNIST section of this course. When normalizing the images, make sure to write transforms.Normalize((0.5,), (0.5,)) , rather than what we had earlier, transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)) . Ultimately, this is done to ensure a single channel of (1, 28, 28), rather than (3, 28, 28). 5 | 6 | 7 | 8 | Let me know if you have any questions about this. 9 | 10 | Best, 11 | 12 | Rayan and Team. -------------------------------------------------------------------------------- /SECTION 7/Test Links.txt: -------------------------------------------------------------------------------- 1 | Test Links 2 | Image link required for next video: 3 | 4 | https://images.homedepot-static.com/productImages/007164ea-d47e-4f66-8d8c-fd9f621984a2/svn/architectural-mailboxes-house-letters-numbers-3585b-5-64_1000.jpg 5 | -------------------------------------------------------------------------------- /SECTION 8/ants_and_bees-master.zip: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/PyTorch-for-Deep-Learning-and-Computer-Vision/8bad829e7f67ffec102659a421e43359c243493d/SECTION 8/ants_and_bees-master.zip --------------------------------------------------------------------------------