Short series ( derrayable 15mins)

Daniel Russ - Soccer - https://danielruss.github.io/soccerNet

Praful - simpleNN - https://github.com/episphere/tfjs/wiki

```javascript
const { TFJSModel } = await import("https://episphere.github.io/tfjs/modelBuilder.js");
const model = new TFJSModel({});
await model.loadData({
    "datasetURL": "https://episphere.github.io/ai/data/irisOHE.json",
    "labelFieldName": "species",   
    "trainTestRatio": 0.8
});
await model.build();
await model.train();
await model.test();
```

Journal Club

TCGA-Reports: A machine-readable pathology report resource for benchmarking text-based AI models [PDF]
Jenna Kefeli and Nicholas Tatonetti
Patterns (Cell). 2024 Mar 8; 5(3): 100933. PMID: 38487800

Highlights

• Introduction of TCGA-Reports, a machine-readable set of ∼10,000 pathology reports

• Benchmark for researchers interested in utilizing LLMs for pathology applications

• Dataset utility demonstrated through proof-of-principle cancer-type classification

Code: https://github.com/jkefeli/tcga-path-reports
Data: https://github.com/jkefeli/tcga-path-reports/blob/main/TCGA_Reports.csv.zip
Metadata: https://github.com/jkefeli/tcga-path-reports/tree/main/data/tcga_metadata#:~:text=tcga_patient_to_cancer_type.csv 

Hackathon