https://github.com/mxagar/deep_learning_udacity
These are my personal notes taken while following the Udacity Deep Learning Nanodegree.
https://github.com/mxagar/deep_learning_udacity
cnn computer-vision convolutional-neural-networks deep-learning deployment gans image-classification natural-language-processing neural-networks object-detection pytorch
Last synced: 2 months ago
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These are my personal notes taken while following the Udacity Deep Learning Nanodegree.
- Host: GitHub
- URL: https://github.com/mxagar/deep_learning_udacity
- Owner: mxagar
- Created: 2022-05-05T15:14:28.000Z (about 4 years ago)
- Default Branch: main
- Last Pushed: 2024-11-01T17:03:01.000Z (over 1 year ago)
- Last Synced: 2025-10-26T07:34:57.690Z (8 months ago)
- Topics: cnn, computer-vision, convolutional-neural-networks, deep-learning, deployment, gans, image-classification, natural-language-processing, neural-networks, object-detection, pytorch
- Language: Jupyter Notebook
- Homepage:
- Size: 65.5 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Udacity Deep Learning Nanodegree: Personal Notes
These are my personal notes taken while following the [Udacity Deep Learning Nanodegree](https://www.udacity.com/course/deep-learning-nanodegree--nd101).
The nanodegree is composed of six modules:
1. [Introduction to Deep Learning](01_Intro_Deep_Learning)
2. [Neural Networks and Pytorch/Keras Guides](02_Neural_Networks)
3. [Convolutional Neural Networks (CNN)](03_CNN)
4. [Recurrent Neural Networks (RNN)](04_RNN)
5. [Generative Adversarial Networks (GAN)](05_GAN)
6. [Deploying a Model with AWS SageMaker](06_Deployment)
Additionally, I have added an extra module/subfolder which I will extend with *new* architectures, applications and tools that appeared post 2018: [Extra](07_Extra).
Each module has a folder with its respective notes; **you need to go to each module folder and follow the Markdown file in them**.
Finally, note that:
- I have also notes on the [Udacity Computer Vision Nanodegree](https://www.udacity.com/course/computer-vision-nanodegree--nd891) in my repository [computer_vision_udacity](https://github.com/mxagar/computer_vision_udacity); that MOOC is strongly related and has complementary material.
- In addition to the [Pytorch guide](02_Pytorch_Guide), I have a [Keras guide](02_Keras_Guide); both condense the most important features of both frameworks. Currently, the Pytorch guide is more detailed.
- I have many hand-written notes you can check, too (see the PDFs).
- I have a specific repository for **object detection** and **semantic segmentation**, where additionally **labeling** tools are covered: [detection_segmentation_pytorch](https://github.com/mxagar/detection_segmentation_pytorch).
- The exercises are commented in the Markdown files and linked to their location; most of the exercises are located in other repositories, originally forked from Udacity and extended/completed by me:
- [deep-learning-v2-pytorch](https://github.com/mxagar/deep-learning-v2-pytorch)
- [CVND_Exercises](https://github.com/mxagar/CVND_Exercises)
- [DL_PyTorch](https://github.com/mxagar/DL_PyTorch)
- [CVND_Localization_Exercises](https://github.com/mxagar/CVND_Localization_Exercises)
- [sagemaker-deployment](https://github.com/mxagar/sagemaker-deployment)
## Projects
Udacity requires the submission of a project for each module; these are the repositories of the projects I submitted:
1. Predicting Bike Sharing Patterns with Neural Networks Written from Scratch with Numpy: [project-bikesharing](https://github.com/mxagar/deep-learning-v2-pytorch/tree/master/project-bikesharing).
2. Dog Breed Classification with Convolutional Neural Networks (CNNs) and Transfer Learning: [project-dog-classification](https://github.com/mxagar/deep-learning-v2-pytorch/tree/master/project-dog-classification).
3. Text Generation: TV Script Creation with a Recurrent Neural Network (RNN): [text_generator](https://github.com/mxagar/text_generator).
4. Face Generation with a Convolutional Generative Adversarial Network (GAN): [face_generator_gan](https://github.com/mxagar/face_generator_gan).
5. Sentiment Analysis RNN Deployed Using AWS SageMaker: [sentiment_rnn_aws_deployment](https://github.com/mxagar/sentiment_rnn_aws_deployment).
## Practical Installation Notes
I basically followed the installation & setup guide from [deep-learning-v2-pytorch](https://github.com/mxagar/deep-learning-v2-pytorch), which can be summarized with the following commands:
```bash
# Create new conda environment to be used for the nanodegree
conda create -n dlnd python=3.6
conda activate dlnd
conda install pytorch torchvision -c pytorch
conda install pip
# Go to the folder where the Udacity DL exercises are cloned, after forking the original repo
cd ~/git_repositories/deep-learning-v2-pytorch
pip install -r requirements.txt
```
Mikel Sagardia, 2022.
No guarantees.
If you find this repository helpful and use it, please link to the original source.