Unlocking the Power of Large Language Models (LLMs) for Business Applications
•
1
Muhammad Imran Zaman PRO
ImranzamanML
AI & ML interests
Results-driven Machine Learning Engineer with 7+ years of experience leading teams and delivering advanced AI solutions that increased revenue by up to 40%. Proven track record in enhancing business performance through consultancy and expertise in NLP, Computer Vision, LLM models and end-to-end ML pipelines. Skilled in managing critical situations and collaborating with cross-functional teams to implement scalable, impactful solutions. Kaggle Grandmaster and top performer in global competitions, dedicated to staying at the forefront of AI advancements.
Recent Activity
posted
an
update
12 days ago
Deep understanding of (C-index) evaluation measure for better model
Lets start with three patients groups:
Group A
Group B
Group C
For each patient, we will predict risk score (higher score means higher risk of early event).
Step 1: Understanding Concordance Index
The Concordance Index (C-index) evaluate that how well the model ranks survival times.
Understand with sample data:
Group A has 3 patients with actual survival times and predicted risk scores:
Patient Actual Survival Time Predicted Risk Score
P1 5 months 0.8
P2 3 months 0.9
P3 10 months 0.2
Comparable pairs:
(P1, P2): P2 has a shorter survival time and a higher risk score → Concordant ✅
(P1, P3): P3 has a longer survival time and a lower risk score → Concordant ✅
(P2, P3): P3 has a longer survival time and a lower risk score → Concordant ✅
Total pairs = 3
Total concordant pairs = 3
C-index for Group A = Concordant pairs/Total pairs= 3/3 = 1.0
Step 2: Calculate C-index for All Groups
Repeat the process for all groups. For now we can assume:
Group A: C-index = 1.0
Group B: C-index = 0.8
Group C: C-index = 0.6
Step 3: Stratified Concordance Index
The Stratified Concordance Index combines the C-index scores of all groups and focusing on the following:
Average performance across groups (mean of C-indices).
Consistency across groups (low standard deviation of C-indices).
Formula:
Stratified C-index = Mean(C-index scores) - Standard Deviation(C-index scores)
Calculate the mean:
Mean=1.0 + 0.8 + 0.6/3 = 0.8
Calculate the standard deviation:
Standard Deviation= sqrt((1.0-0.8)^2 + (0.8-0.8)^2 + (0.6-0.8)^/3) = 0.16
Stratified C-index:
Stratified C-index = 0.8 - 0.16 = 0.64
Step 4: Interpret the Results
A high Stratified C-index means:
The model predicts well overall (high mean C-index).
updated
a model
about 1 month ago
ImranzamanML/arabert_finetuned_model
updated
a model
about 1 month ago
ImranzamanML/arabic_finetuned_model
Articles
Organizations
ImranzamanML's activity
upvoted
an
article
3 months ago
Article
Fine-tuning LLMs to 1.58bit: extreme quantization made easy
•
207
upvoted
an
article
4 months ago
Article
Llama-3.1-Storm-8B: Improved SLM with Self-Curation + Model Merging
By
•
•
74
upvoted
a
collection
4 months ago
upvoted
12
articles
8 months ago
Article
CodeGemma - an official Google release for code LLMs
•
99
Article
Making thousands of open LLMs bloom in the Vertex AI Model Garden
•
18
Article
Vision Language Models Explained
•
231
Article
Introducing Idefics2: A Powerful 8B Vision-Language Model for the community
•
170
Article
Ryght’s Journey to Empower Healthcare and Life Sciences with Expert Support from Hugging Face
•
6
Article
Running Privacy-Preserving Inference on Hugging Face Endpoints
•
18
Article
Introducing the LiveCodeBench Leaderboard - Holistic and Contamination-Free Evaluation of Code LLMs
•
14
Article
AI Apps in a Flash with Gradio's Reload Mode
•
24
Article
Introducing the Open Chain of Thought Leaderboard
•
26
Article
Jack of All Trades, Master of Some, a Multi-Purpose Transformer Agent
•
78
Article
Improving Prompt Consistency with Structured Generations
•
58
Article
Getting Started With Embeddings
•
39