⚡TalentMe Proprietary Industrial Deep Learning & LLM Science Coding Engine

Deep Learning & LLM Core Science Coding Engine

A comprehensive repository of 69 essential industrial kernels covering foundation activations, Transformer mechanics, alignment RL, parallel architectures, and inference engines. Complete with math derivations, tensor flows, stability checklists, and runnable test assertions.

🔥 69 Comprehensive Kernels
🛡️ 100% Numerically Stable
🧪 Runnable Test Assertions
🌐 Bilingual CN / EN
🎯

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Showing 69 matching core algorithm kernels
A1★ Core Essential · Industrial Bedrock
Easy

Numerically Stable Softmax

Part A · Core Kernels & Activation Functions

Industrial-grade implementation subtracting max to prevent exponent overflow while preserving dimensional broadcasting.

💡 Subtract max to guard against overflow; keepdims ensures broadcasting; denominator has 1 to avoid zero-division
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A2★ Core Essential · Industrial Bedrock
Easy

Numerically Stable Sigmoid & Log-Sigmoid

Part A · Core Kernels & Activation Functions

Piecewise formulation avoiding overflow on large negatives; Log-Sigmoid powered by logaddexp for robust BCE training.

💡 Standard form for positives, multiply exp(z) for negatives; use logaddexp for log-sigmoid
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A3★ Core Essential · Industrial Bedrock
Medium

Gaussian Error Linear Unit (GELU)

Part A · Core Kernels & Activation Functions

Stochastic regularizer gating activation based on standard Gaussian CDF, standard in BERT and GPT-2.

💡 0.5x times (1 + tanh), cubic term scaled by 0.044715
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A4★ Core Essential · Industrial Bedrock
Medium

SwiGLU Gated Feed-Forward Network

Part A · Core Kernels & Activation Functions

Flagship FFN operator powering LLaMA and DeepSeek, combining SiLU gated branch with up-projection and down-projection.

💡 Gate and up run in parallel; SiLU modulates via Hadamard product; down-projection restores dimension
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A5
Easy

Softplus & LeakyReLU with Overflow Guards

Part A · Core Kernels & Activation Functions

Softplus with threshold clipping against overflow, alongside LeakyReLU negative slope leak.

💡 log1p for small inputs; threshold cutoff to identity for large values
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B1★ Core Essential · Industrial Bedrock
Easy

Layer Normalization with Affine Transformation

Part B · Normalization Family

Industrial-grade implementation and mathematical foundations of Layer Normalization with Affine Transformation.

💡 Master Layer Normalization with Affine Transformation: enforce numerical stability, check tensor shapes, and eliminate redundant memory allocations.
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B2★ Core Essential · Industrial Bedrock
Easy

Root Mean Square Normalization (RMSNorm)

Part B · Normalization Family

Industrial-grade implementation and mathematical foundations of Root Mean Square Normalization (RMSNorm).

💡 Master Root Mean Square Normalization (RMSNorm): enforce numerical stability, check tensor shapes, and eliminate redundant memory allocations.
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B3★ Core Essential · Industrial Bedrock
Medium

BatchNorm1d with Train vs Eval Distinction

Part B · Normalization Family

Industrial-grade implementation and mathematical foundations of BatchNorm1d with Train vs Eval Distinction.

💡 Master BatchNorm1d with Train vs Eval Distinction: enforce numerical stability, check tensor shapes, and eliminate redundant memory allocations.
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B4
Medium

Group Normalization & Instance Normalization

Part B · Normalization Family

Industrial-grade implementation and mathematical foundations of Group Normalization & Instance Normalization.

💡 Master Group Normalization & Instance Normalization: enforce numerical stability, check tensor shapes, and eliminate redundant memory allocations.
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B5★ Core Essential · Industrial Bedrock
Easy

Inverted Dropout with Train/Eval Scaling

Part B · Normalization Family

Industrial-grade implementation and mathematical foundations of Inverted Dropout with Train/Eval Scaling.

💡 Master Inverted Dropout with Train/Eval Scaling: enforce numerical stability, check tensor shapes, and eliminate redundant memory allocations.
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C1★ Core Essential · Industrial Bedrock
Easy

Sinusoidal Positional Encoding

Part C · Positional Encoding Evolution

Industrial-grade implementation and mathematical foundations of Sinusoidal Positional Encoding.

💡 Master Sinusoidal Positional Encoding: enforce numerical stability, check tensor shapes, and eliminate redundant memory allocations.
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C2★ Core Essential · Industrial Bedrock
Medium

Rotary Position Embedding (RoPE)

Part C · Positional Encoding Evolution

Industrial-grade implementation and mathematical foundations of Rotary Position Embedding (RoPE).

💡 Master Rotary Position Embedding (RoPE): enforce numerical stability, check tensor shapes, and eliminate redundant memory allocations.
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