AIND CODE

Code developed by jotavaladouro during the Artificial Intelligence (AIND) nanodegree of Udacity.

List of projects

1 Term

AIND-Constraint-Satisfaction

Solve the N-Queens Problem

Algorithms: constraint satisfaction and backtracking search.

Library: SymPy.

AIND-Planning

Air cargo problem solved using different algorithms and compare their results:

Algorithms : breadth-first,depth-first search, heuristic, Planning Graph.

AIND-Simulated_Annealing

The travel salesman problem.

Algorithms: simulated annealing

Aind-isolation

Develop a game playing agent for the isolation game.

Algorithms: minimax search with alpha-beta pruning, fixed-depth, and iterative deepening search.

Aind-sudoku

Solve sudoku.

Algorithms:constraint propagation.

AIND-Recognizer

Recognize words for American Sign Language video sequences.

Algorithms: hidden Markov models (HMM's).

Libraries: scikit-learn,hmmlearn.

2 Term

Aind2-cnn

Train a Deep Network on images from the CIFAR-10 database.Test transfer learning from vgg16.

Algorithms: Using Deep Neural Network, CNN, CNN + augmentation.

Library: Keras

aind2-dl

Analyze sentiments in IMDM and Predicting Student Admissions.

Algorithms: deep learning.

Library: Keras

aind2-rnn

Perform time series prediction and create a sequence generator. Algorithms: recurrent neural network (RNN).

Library: Keras

Deep-learning

• rnn: Build a character-wise RNN trained on Anna Karenina. Use LSTM.
• Autoencoder: Train an autoencoder in the MNIST database, using it for denoising images.
• GAN : Build a generative adversarial network (GAN) trained on the MNIST dataset.
• Sentiment Classification: Sentiment Analysis on the IMDB database, building a simple neural network from scratch.
• Sentiment Analysis with a RNN: Implement a recurrent neural network that performs sentiment analysis. Use embedding and LSTM.

Algorithms: LSTM, Autoencoder, GAN,embedding.

Library: tensor-flow

Dog-project

Algorithms: **Haar feature-based cascade classifiers,pre-trained ResNet-50, CNN,Transfer Learning **

Library: OpenCV,Keras

NLP

AIND-NLP-Bookworm

Use IBM Watson's NLP Services to create a simple question-answering system.

AIND-NLP

Sentiment Analysis on the IMDB database. Testing different algorithm:

Algorithms: Bag-of-Words and Gaussian Naive Bayes; Bag-of-Words and Gradient-Boosted Decision Tree; LSTM.

Libraries: Keras, BeautifulSoup

aind2-nlp-capstone

Build a deep neural network that functions as part of an end-to-end machine translation pipeline.

Algorithms: RNN, embedding, bidirectional RNN, Encoder-Decoder RNN.

Libraries: Keras

TODO

• AIND-CV-FacialKeypoints
• AIND-VUI-Capstone
• opencv