In this course, I worked with Philips in a group of students to help them become more entrepreneurial. As part of the corporate strategy team, we evaluated new business ideas to boost their market share and revenue. This involved analyzing customer personas, market size, MAP planning, and value innovation, as well as developing skills for pitching to the C-suite.

The focus of this group project is to implement, evaluate, operate and monitor a movie streaming recommendation service in production, which entails many concerns, including deployment, scaling, reliability, drift and feedback loops. The streaming service has about 1 million customers and 27,000 movies.

In this Kaggle competition, I learned how to build an encoder to effectively extract features from a speech signal, how to construct a decoder to sequentially spell out the transcription of the audio, and how to implement an attention mechanism between the decoder and the encoder.

In this Kaggle competition, the task was to build a face classifier that can extract feature vectors from face images and a face verification system that computes the similarity between feature vectors of images. I used a CNN architecture to build this model in order to achieve high accuracy on this classification and verification task.

In this Kaggle competition, the task was to predict the phoneme label for each frame in the test set of the speech recordings, which are raw mel spectrogram frames. I used a multi-layer perceptron model and explored various hyperparameters to improve the accuracy of the prediction of the phoneme state labels for each frame in the test set.

I implemented an RNN-based language model, text prediction and degeneration, greedy decoding, and regularization techniques for RNNs. Here, I go over these concepts and a demonstration of language modeling for machine translation (English to French).

I implemented RNNs, GRUs, and CTC from scratch. Here, I go over a binary sentiment prediction task using the TensorFlow IMDB dataset, give an overview of these architectures and compare RNNs, GRUs, and LSTMs.

I implemented convolutional neural networks (CNN) from scratch. Here, I go over a traffic sign identification task and describe the significance of convolutional layers, pooling layers, downsampling and upsampling layers, classification layer, forward pass and backpropagation through layers.

I implemented an MLP from scratch with 0, 1, and 4 hidden layers. Here, I go over an MLP sentiment analysis task and describe the significance of linear layers, activation functions, forward inference, criterion functions (MSE and CELoss), SGD optimization, batch normalization regularization, and backpropagation.

This project aims to investigate the effectiveness of using sentiment analysis techniques on data obtained from multiple news sources, namely Webull, Twitter, and Reddit, to predict stock price differences during the COVID-19 pandemic. We focus on two companies, one large-cap (Zoom) and one small-cap (AMC), to explore the correlation between market sentiment and stock price movement.

The objective of this project is to develop a social agent that is capable of performing a wide range of tasks by accurately mapping human language instructions to actions, behaviors, and objects in interactive visual environments. To achieve this goal, we utilized the ALFRED benchmark dataset, which consists of tasks related to home automation and assistive robots for people with disabilities and the elderly.