We build an API for predicting the popularity of Mandopop/C-pop song. You can simply upload a song, and the model will tell you the estimated popularity of it. The model is based on 1. an inception CNN which takes mel-spectrogram of the song as input and 2. a fully-connected DNN which takes auto-tagging information as input.
Here is the note I took for the second course of the Machine Learning Engineering for Production (MLOps) Specialization. In this course, I learned how to accomplish data collection for ML production system, implement feature engineering, transformation, and selection with TensorFlow Extended, and establish the data lifecycle by leveraging data lineage and provenance metadata tools and follow data evolution with enterprise data schemas.
Here is the note I took for the course 'Introduction to Machine learning in Production (MLOps)' at Coursera/Deeplearning.ai. This is the first course of the Machine Learning Engineering for Production (MLOps) Specialization. In this course, I learned about the key components of ML life cycle and pipeline in production settings and how to solve problems for structured, unstructured, small, and big data.
Using ridge regression, we found memory-related neural activity in parietal cortex can successfully predict the memory content (which is vectorized by the final fully connected layer's output from VGG16.)
An fMRI study with MVPA and SEM analysis that reveal how our brains actively reduce interference caused by similarity between memories to achieve for better memory performance.
In this notebook, I am going to show you How to extract the features for a set of images at a certain layer of VGG16 pretrained model with PyTorch. How to use PCA(Principle component analysis) to reduce the feature dimension for better visualization.
Although the theory basis of my major, cognitive neuroscience, is based on psychology and biology, the skills I need to tackle with the research work of this field are most data science related.