Caption Images with Machine Learning
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Updated
Jun 23, 2022 - Python
Caption Images with Machine Learning
Sentiment analysis performed using a pre-trained BERT model on Mac Miller's complete discography.
Computer vision tools for analyzing behavioral data, including complex event detection in videos.
A TensorFlow implementation of "Sequence Modeling with Hierarchical Deep Generative Models with Dual Memory" (published in CIKM2017).
A Generative Model for Audio in the Frequency Domain
Sequence classification and generation using LSTMs and RNNs
Applied Ai (Papers, Articles & Videos, applied in production with results)
Implementation of Smith-Waterman local alignment model- find closest local alignments in two given amino acid sequences. BLOSUM was used as the scoring matrix.
Transformer/BERT models trained on the Breakthrough Listen Kaggle dataset.
VOGUE: Variable Order HMM with Duration
TinyML stuff done on my Arduino Nano 33 BLE Sense
The course studies fundamentals of distributed machine learning algorithms and the fundamentals of deep learning. We will cover the basics of machine learning and introduce techniques and systems that enable machine learning algorithms to be efficiently parallelized.
An unofficial implementation of "TransAct: Transformer-based Realtime User Action Model for Recommendation at Pinterest" in Tensorflow
Generate music with LSTM model
Human Activity Recognition using Deep Learning on Spatio-Temporal Graphs
Multiple EM for Motif Elicitation for discovering motifs in a group of related DNA or protein sequences.
Audio and Music Synthesis with Machine Learning
Deep, sequential, transductive divergence metric and domain adaptation for time-series classifiers
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