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🧮 The Model

class SrinjoyRoy:
    def __init__(self):
        self.name        = "Srinjoy Roy"
        self.username    = "srinjoy356"
        self.pronouns    = "he/him"
        self.location    = "India 🇮🇳"
        self.interests   = ["Data Science", "Machine Learning", "Statistics", "Neural Networks"]
        self.learning    = ["ML Algorithms", "Deep Learning", "Real-world Projects"]
        self.collaborate = ["Python", "ML", "Software Development"]
        self.philosophy  = "I don't learn traditionally — I learn by doing."

    def loss_fn(self, knowledge: float) -> float:
        """Minimize ignorance. Maximize understanding."""
        return 1 / (1 + knowledge)   # → 0 as knowledge → ∞

    def gradient_step(self):
        self.knowledge += self.curiosity * self.learning_rate

    def __repr__(self):
        return f"∂(impact)/∂(effort) > 0  →  Still optimizing 🚀"

me = SrinjoyRoy()

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📐 About Me

$$\hat{y} = \sigma\left(\sum_{i=1}^{n} w_i x_i + b\right)$$

Where $x_i$ = experience, $w_i$ = curiosity, $b$ = stubbornness to give up, $\sigma$ = the real world

• 🔭 Currently: Training models, debugging loss curves, building projects
• 📊 Domain: ML · GenAI · Computer Vision · NLP · MLOps · Statistics
• 🌐 Portfolio: srinjoyroy356.qd.je
• 🌱 Gradient Descent: Always descending toward deeper understanding
• 💡 Philosophy: loss → 0 only when you stop following the textbook
• 🤝 Open to: Collaborations in Python, ML, or any software project worth building
• ⚡ Fun fact: My learning rate is adaptive — I skip the boring parts

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🛠️ Tech Stack

Languages & Core

ML / Data Science

MLOps & Experiment Tracking

Deployment & Infrastructure

Tools & Platforms

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📊 GitHub Activity

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🧠 The Learning Curve

Epoch 1  : "What is a neural network?"       Loss: ████████████  0.98
Epoch 10 : "I can train a model!"            Loss: ████████░░░░  0.72
Epoch 50 : "Hyperparameter tuning hurts..."  Loss: ████░░░░░░░░  0.41
Epoch ∞  : Still running...                  Loss: Converging → 0

$\mathcal{L}(\theta) = -\frac{1}{m}\sum_{i=1}^{m}\left[y^{(i)}\log\hat{y}^{(i)} + (1-y^{(i)})\log(1-\hat{y}^{(i)})\right]$

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📫 Connect with Me

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✍️ Random Dev Quote

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"All models are wrong, but some are useful." — George Box