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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

tfsign

Navier-Stokes Equations using Python

ns

Optimization: Mini batch gradient descent with momentum and Adam mode

Word2Vec using TensorFlow using dummy data

w2v

He et al (2015) Initialization for Neural Networks

Gradient Checking Algorithm

Poppy Humanoid Robot

Football Corporation Goalkeeper Position Recommendation

ABB IRB 7600-340 Robot visualization in VR and 3D mode using JavaScript (Ongoing)

Logistic regression to recognize cats

Softmax Linear Classifier using 2 NN for visual recognition

Vision

Recommendation System

  • Recommendation System: Collaborative and Content-based; NumPy,SciPy, LightFM, OpenMP, Weighted Approximate-Rank Pairwise, Gradient Descent, Compressed Sparse Row Format; MovieLens: GroupLens Research Site (University of Minnesota)
  • Repository
  • https://movielens.org/

    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

pdc

Dymola Systems Simulation of a Washing Machine

  • Video:
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