RNN ----------
Project Submission Generate TV Scripts Introduction In this project, you'll generate your own Simpsons TV scripts using RNNs. You'll be using part of the Simpsons dataset of scripts from 27 seasons. The Neural Network you'll build will generate a new TV script for a scene at Moe's Tavern.
Getting the project files The project files can be found in our public GitHub repo, in the tv-script-generation folder. You can download the files from there, but it's better to clone the repository to your computer
git clone https://github.com/udacity/deep-learning.git This way you can stay up to date with any changes we make by pulling the changes to your local repository with git pull.
Submission Ensure you've passed all the unit tests in the notebook. Ensure you pass all points on the rubric. When you're done with the project, please save the notebook as an HTML file. You can do this by going to the File menu in the notebook and choosing "Download as" > HTML. Ensure you submit both the Jupyter Notebook and it's HTML version together. Package the "dlnd_tv_script_generation.ipynb", "helper.py", "problem_unittests.py", and the HTML file into a zip archive, or push the files from your GitHub repo. Hit Submit Project below! Advanced Projects After completing this project, try applying what you learned to one of these problems.
Generate your own Bach music using like DeepBach. Predict seizures in intracranial EEG recordings on Kaggle.
CNN ------------
Project Overview Welcome to the Convolutional Neural Networks (CNN) project! In this project, you will learn how to build a pipeline to process real-world, user-supplied images. Given an image of a dog, your algorithm will identify an estimate of the canine’s breed. If supplied an image of a human, the code will identify the resembling dog breed.
Along with exploring state-of-the-art CNN models for classification, you will make important design decisions about the user experience for your app. Our goal is that by completing this lab, you understand the challenges involved in piecing together a series of models designed to perform various tasks in a data processing pipeline. Each model has its strengths and weaknesses, and engineering a real-world application often involves solving many problems without a perfect answer. Your imperfect solution will nonetheless create a fun user experience!
Project Instructions Clone the project from the GitHub repository. Follow the instructions in the README to complete the project.
Evaluation Your project will be reviewed by a Udacity reviewer against the CNN project rubric. Review this rubric thoroughly, and self-evaluate your project before submission. All criteria found in the rubric must meet specifications for you to pass.
Project Submission When you are ready to submit your project, collect the following files and compress them into a single archive for upload:
The dog_app.ipynb file with fully functional code, all code cells executed and displaying output, and all questions answered. An HTML or PDF export of the project notebook with the name report.html or report.pdf. Any additional images used for the project that were not supplied to you for the project. Please do not include the project data sets in the dogImages/ or lfw/ folders. Likewise, please do not include the bottleneck_features/ folder. Alternatively, your submission could consist of the GitHub link to your repository.
Ready to submit your project? Click on the "Submit Project" button and follow the instructions to submit!
First ANN ------------
Your First Neural Network Introduction In this project, you'll get to build a neural network from scratch to carry out a prediction problem on a real dataset! By building a neural network from the ground up, you'll have a much better understanding of gradient descent, backpropagation, and other concepts that are important to know before we move to higher level tools such as Tensorflow. You'll also get to see how to apply these networks to solve real prediction problems!
The data comes from the UCI Machine Learning Database.
Instructions Download the project materials from our GitHub repository. You can get download the repository with git clone https://github.com/udacity/deep-learning.git. Our files in the GitHub repo are the most up to date, so it's the best place to get the project files. cd into the first-neural-network directory. Download anaconda or miniconda based on the instructions in the Anaconda lesson. Create a new conda environment: conda create --name dlnd python=3 Enter your new environment: Mac/Linux: >> source activate dlnd Windows: >> activate dlnd Ensure you have numpy, matplotlib, pandas, and jupyter notebook installed by doing the following: conda install numpy matplotlib pandas jupyter notebook Run the following to open up the notebook server: jupyter notebook In your browser, open Your_first_neural_network.ipynb Follow the instructions in the notebook; they will lead you through the project. You'll ultimately be editing the my_answers.py python file, whose components are imported into the notebook at various places. Ensure you've passed the unit tests in the notebook and have taken a look at the rubric before you submit the project! If you need help running the notebook file, check out the Jupyter notebook lesson.
Submission Before submitting your solution to a reviewer, you are required to submit your project to Udacity's Project Assistant, which will provide some initial feedback. It will give you feedback within a minute or two on whether your project will meet all specifications. It is possible to submit projects which do not pass all tests; you can expect to get feedback from your Udacity reviewer on these within 3-4 days.
The setup for the project assistant is simple. If you have not installed the client tool from a different Nanodegree program already, then you may do so with the command pip install udacity-pa.
To submit your code to the project assistant, run udacity submit from within the top-level directory of the project. You will be prompted for a username and password. If you login using google or facebook, visit this link for alternate login instructions.
This process will create a zipfile in your top-level directory named first_neural_network-result-.zip, where there will be a number between result- and .zip. This is the file that you should submit to the Udacity reviews system.
Upload that file into the system and hit Submit Project below!
If you run into any issues using the project assistant, please check this page to troubleshoot; feel free to post your problem in Knowledge if it isn't covered by one of the displayed cases!
What to do afterwards If you're waiting for new content or to get the review back, here's a great video from Frank Chen about the history of deep learning. It's a 45 minute video, sort of a short documentary, starting in the 1950s and bringing us to the current boom in deep learning and artificial intelligence.
GAN ----------------
Project Submission Face Generation Introduction In this project, you'll use generative adversarial networks to generate new images of faces.
Getting the project files The project files can be found in our public GitHub repo, in the face_generation folder. You can download the files from there, but it's better to clone the repository to your computer
This way you can stay up to date with any changes we make by pulling the changes to your local repository with git pull.
Submission Ensure you've passed all the unit tests in the notebook. Ensure you pass all points on the rubric. When you're done with the project, please save the notebook as an HTML file. You can do this by going to the File menu in the notebook and choosing "Download as" > HTML. Ensure you submit both the Jupyter Notebook and it's HTML version together. Package the "dlnd_face_generation.ipynb", "helper.py", "problem_unittests.py", and the HTML file into a zip archive, or push the files from your GitHub repo. Hit Submit Project below! Congratulations! You've completed this project
Deep Reinforcemnet Learning
Project Submission In this project, you will design an agent that can fly a quadcopter, and then train it using a reinforcement learning algorithm of your choice! Try to apply the techniques you have learnt in this module to find out what works best, but also feel free to come up with innovative ideas and test them.
You are encouraged to use the workspace in the next concept to complete the project. Alternatively, you can clone the project from the GitHub repository.
If you have questions about this project, ask them in the forums. If this is your first time using the forums, first enter them through the forums link in the resources tab (on the upper left of your screen), as this will generate the necessary permissions for your forums account.
Submit your project for review once you meet the project specifications as per this rubric. Congratulations! You've completed this project