Did my own display of SASS (shown in blog) and linked personal website where I implicated SASS lesson fundamentals (Also linked in blog) . Note: Even though I did extra I got the 0.89 because my reflection was not not reflective enough. I do believe the websites that I created with SASS fundamentals from their lesson should count for extra.
No Extras, but I would like to make Note : that the reason I got such a low score is because I misunderstood the expectations for the Hashmaps hacks as the lesson was cut short. I did some of my own exploration in hashmaps with a sample application but I didn't do the sports program. I asked if my own code could count for the points but they were only willing to give me up to 0.8
My contributions in java was setting up and creating the backend api. This is meant to contain all of the cards, or data in our project.
CardGeneration, Service , controller, main, jpa repository, classes
One improvement that was suggested in an earlier review was to create a larger dataset to demonstrate the varying complexities of the sorting algorithms better, so I changed the dataset from containing 52 records (like a deck) to 500 cards.
Generation and Service Classes:
@Configuration
public class CardGeneration {
@Bean
CommandLineRunner commandLineRunner(
CardJpaRepository repository) {
return args -> {
List<Card> cards = new ArrayList<>();
for (int rank = 1; rank <= 500; rank++) {
Card card = new Card(rank);
cards.add(card);
}
repository.saveAll(cards);
};
}
@Service
public class CardService {
private final CardJpaRepository cardRepository;
@Autowired
public CardService(CardJpaRepository cardRepository) {
this.cardRepository = cardRepository;
}
public List<List<Card>> splitCardsRandomly(List<Card> cards) {
// Shuffle the cards randomly
Collections.shuffle(cards);
// Split the cards into two halves
int halfSize = cards.size() / 2;
List<Card> firstHalf = new ArrayList<>(cards.subList(0, halfSize));
List<Card> secondHalf = new ArrayList<>(cards.subList(halfSize, cards.size()));
List<List<Card>> splitCards = new ArrayList<>();
splitCards.add(firstHalf);
splitCards.add(secondHalf);
return splitCards;
}
// commit change
public void saveCards(List<Card> cards) {
cardRepository.saveAll(cards);
}
}
Java Fundamental: class extension can be seen in JPA repository file. JPA repository is an interface which is a part of the Spring Data repository support and provides methods for common database operations (like save, delete, findById, etc.) without the need for manual implementation. The use of this class extension was very helpful!
I plan to add something like ranks so I could add a compareto method as mentioned by Mort in indicator tech talk. Additionally I would create cards as objects with rank and suit variables instead of using an imperative generation method. However we were sort of in a time crunch because we did ideation incorrectly.
Overall
Commits
Reflection (Bulleted because who wants to read a paragraph)
I need to commit more sporadically
Work on collaboration and task assignment. Take on the role of scrum master and help my team stay on track
I want to learn automated deployment more in depth (why deploy with the same commands over and over again if I can just learn how to make a deployment script)
I want to work with more complicated APIs like AWS or Github to do some interesting data analysis
Maybe something like commits/contributions to predict success on live reviews
I want to try crowd sourcing data for atleast one project this year
I want to update my personal website with any cool projects I do in this class
I really want to do the student linkden project on the side
I would like to push myself to explore UI, since I spent most of this year focused on using Spring/Flask/Django for backend
pocketTherapist work
Sub ticket Tanisha CNN implementation
Week 1: Planning and Data Preparation
Day 0: Sprint Planning
Day 1: Data Collection
[x] Gather a dataset of facial images labeled with emotions ( Kaggle's FER-2013 dataset)
[x] Clean the dataset by removing irrelevant or low-quality images.
Day 2: Data Preprocessing
[x] Resize images to a consistent format (e.g., 224x224 pixels).
[x] Normalize pixel values to a common scale (e.g., [0, 1]).
[x] Split the dataset into training, validation, and test sets.
[x] Augment the training dataset (e.g., rotate, flip, zoom) to increase diversity. ENSURE EQUAL DIST.
Day 3: Model Architecture Design
Day 4: Model Implementation
Day 5: Training
(CURRENT)
Day 6: Model Evaluation
Week 2: Model Optimization and Deployment
Day 7: Hyperparameter Tuning
Day 8: Error Analysis
Day 9: Model Interpretability
Day 10: Model Optimization
Day 11: Deployment Setup
Day 12: Testing and Validation
Day 13: Documentation and Training
Day 14: Final Review and Retrospective
ww3 work
Demo/Summary Video
Youtube Link
Creating Backend Endpoints - Backend
Generation and Service Classes:
Endpoint:
Postman:
Key Commit Backend
Creating Test Analysis File - Frontend
Key Commit Frontend
Commit History For ww3
Frontend:
Backend:
Retrospective
I plan to add something like ranks so I could add a compareto method as mentioned by Mort in indicator tech talk. Additionally I would create cards as objects with rank and suit variables instead of using an imperative generation method. However we were sort of in a time crunch because we did ideation incorrectly.
Overall
Commits
Reflection (Bulleted because who wants to read a paragraph)