Machine Learning

Last week, we first designed our CNN model with four convolution layers. The CNN model we designed has a dropout value of 0.2 within the convolution layers. As an optimizer, it had the ‘adam’ optimizer. Our goal this week is to find the most successful general CNN model by changing

Our aim this week was to finalize the code in which we performed certain operations on the csv file in the third week and to calculate an accuracy with our initial values. First, we added the photos in our dataset to our list called “data” by going through some processes.

This week, we focused on learning about convolutional neural networks (CNNs) and examining code examples written in CNN. We reached a point in our code development where we need to design a CNN model for our project. We utilized the resources mentioned in the sources section to gain a comprehensive

We are in midterm week, so there is no update this week 🙂

Our aim this week was to perform certain operations on the ‘style.csv’ file in our dataset and bring it to the format we wanted. Procedures we apply: 1-) Deleting column containing Nan values 2-) Determining the column we will use (“articleType” and “baseColour”) 3-) See visually what contents these columns

After determining our project, our aim this week was to examine other studies on this subject and make notes for ourselves from these studies. In this context, we examined 3-4 studies we found, the links of which can be found below. 1-) “Multi-Label Clothing Recommendation with Deep Learning” by Seong

We determined our project and the purpose of our project. We divided our project into two stages. First stage: This is the main stage of our project, our aim here is to complete the training of the model and enable it to extract two features from the given clothes photos.