GAN Implementation: Data Prep & Training

I'm looking for an expert in Python to assist with implementing a GAN model for my small dataset, under 1,000 samples. The project involves three key components: - Data Preparation: You will be responsible for the initial preparation and cleaning of the dataset. This includes handling missing values, outliers, and ensuring the data is in a suitable format for the model. - Model Training: You will need to develop and train the Generative Adversarial Network (GAN) model using Python. The training process should be well-optimized for a small dataset to ensure the best possible results. - Model Evaluation: Following the training process, you will evaluate the GAN model's performance. This involves assessing metrics such as accuracy, precision, and recall. It's crucial that you're able to interpret these results and suggest improvements if necessary. Ideal candidates for this project will have: - Proficiency in Python and experience with GAN implementation. - Strong data preprocessing skills, particularly with small datasets. - A solid understanding of model training and evaluation processes. - Excellent problem-solving and analytical abilities. If you have experience in these areas and are confident in your ability to deliver high-quality results, I'd like to hear from you. Please include relevant examples of your previous work.