This repo contains all 5 Projects developed during Deep-Learning Foundation Nanodegree. All projects met expectation during project review.

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.

In this project we train our model over an image data set and then predict the content of an image.

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.

In this project, you’re going to take a peek into the realm of neural network machine translation. You’ll be training a sequence to sequence model on a dataset of English and French sentences that can translate new sentences from English to French.

In this project, you'll use generative adversarial networks to generate new images of faces.

1. Create an account on floydhub.com (don't forget to confirm your email). You will automatically receive 100 free GPU hours.

2. Install the floyd command on your computer:

    pip install -U floyd-cli
   

   Do this even if you already installed floyd-cli before, just to make sure you have the most recent version (its peace of development is fast!).

3. Associate the command with your Floyd account:

    floyd login
   

   (a page with authentication token will open; you will need to copy the token into your terminal)

4. Clone this repository:

    git clone `<git repo url>`
   

5. Enter the folder for the image classification project:

    cd `<repo folder>`
   

6. Initiate a Floyd project:

    floyd init `<floyd project name>`
   

7. Run the project:

    floyd run --gpu --env tensorflow --mode jupyter --data `<floyd data id>`
   

   It will be run on a machine with GPU (--gpu), using a Tenserflow environment (--env tensorflow), as a Jupyter notebook (--mode jupyter), with Floyd's built-in cifar-10 dataset available e.g-(--data diSgciLH4WA7HpcHNasP9j).

8. Wait for the Jupyter notebook to become available and then access the URL displayed in the terminal (described as "path to jupyter notebook"). You will see the notebook.

9. Remember to explicitly stop the experiment when you are not using the notebook. As long as it runs (even in the background) it will cost GPU hours. You can stop an experiment in the "Experiments" section on floyd.com or using the floyd stop command:

    floyd stop ID
   

   (where ID is the "RUN ID" displayed in the terminal when you run the project; if you lost it you can also find it in the "Experiments" section on floyd.com)

Important: When you run a project it will always start from scratch (i.e. from the state present locally on your computer). If you made changes in the remote jupiter notebook during a previous run, the changes will not be present in subsequent runs. To make them permanent you need to add the changes to your local project folder. When running the notebook you can download them directly from Jupyter - File / Download / Notebook. After downloading it, just replace your local filename.ipynb file with the newly downloaded one.

Alternatively, If you already stoped the experiment, you can still download the file using the floyd output command:

floyd output ID

(where ID is the "RUN ID" displayed in the terminal when you run the project; if you lost it you can also find it in the "Experiments" section on floyd.com)

Just run the command above, download filename.ipynb and replace your local version with the newly downloaded one.