Problem statement: Emotion recognition has many potential applications in fields such as psychology, marketing, and human-computer interaction. A deep learning-based emotion recognition system could accurately detect and classify human emotions from images or speech.
Conduct a literature review of existing research on emotion recognition and deep learning algorithms.
Develop a dataset of images or speech samples with labeled emotions to train the emotion recognition system.
Select an appropriate deep learning algorithm, such as convolutional neural networks or recurrent neural networks, and tune the hyperparameters using cross-validation.
Implement the emotion recognition system using Python and a deep learning library such as TensorFlow or PyTorch.
Evaluate the performance of the emotion recognition system using metrics such as accuracy and F1-score.
Write a report summarizing your research, methodology, and findings.
Problem statement: Emotion recognition has many potential applications in fields such as psychology, marketing, and human-computer interaction. A deep learning-based emotion recognition system could accurately detect and classify human emotions from images or speech.
Conduct a literature review of existing research on emotion recognition and deep learning algorithms. Develop a dataset of images or speech samples with labeled emotions to train the emotion recognition system. Select an appropriate deep learning algorithm, such as convolutional neural networks or recurrent neural networks, and tune the hyperparameters using cross-validation. Implement the emotion recognition system using Python and a deep learning library such as TensorFlow or PyTorch. Evaluate the performance of the emotion recognition system using metrics such as accuracy and F1-score. Write a report summarizing your research, methodology, and findings.