Mini Thesis at Institute of General Mechanics at RWTH Aachen University, Germany.
 ”Combine an Artificial Neural Network with an UMAT subroutine to replace a Chaboche Viscoplastic Constitutive Law in Abaqus.” Sequence to sequence learning using LSTM and Neural Networks. Visaulization using TensorBoard.
TensorFlow implementation to decipher sign language
NavierStokes Equations using Python
Optimization: Mini batch gradient descent with momentum and Adam mode
Word2Vec using TensorFlow using dummy data
He et al (2015) Initialization for Neural Networks
Gradient Checking Algorithm
Poppy Humanoid Robot
Football Corporation Goalkeeper Position Recommendation
ABB IRB 7600340 Robot visualization in VR and 3D mode using JavaScript (Ongoing)
Logistic regression to recognize cats
Softmax Linear Classifier using 2 NN for visual recognition
 Cross Entropy Loss Function
 Repository
Recommendation System
 Recommendation System: Collaborative and Contentbased; NumPy,SciPy, LightFM, OpenMP, Weighted ApproximateRank Pairwise, Gradient Descent, Compressed Sparse Row Format; MovieLens: GroupLens Research Site (University of Minnesota)
 Repository

def sample_recommendation(model, data, user_ids): # number of users and movies in training data n_users, n_items = data['train'].shape # generate recommendations for each user we input for user_id in user_ids: # movies they already like known_positives = data['item_labels'][data['train'].tocsr()[user_id].indices] # movies our model predicts they will like scores = model.predict(user_id, np.arange(n_items)) # rank them in order of most liked to the least top_items = data['item_labels'][np.argsort(scores)] # print out the results print('User %s' % user_id) print('Known positives:') for x in known_positives[:3]: print(' %s' % x) print('Recommended:') for x in top_items[:3]: print(' %s' % x)
Supervised learning with 5 layer deep neural network using ReLU for image classification.
Planar data classification
Dymola Systems Simulation of a Washing Machine