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Business & Strategy

Investment Law Basics for Founders

This course covers transformer architectures, self-attention mechanics, and autoregressive generation strategies. You will learn to configure causal attention masks, tune temperature parameters for nucleus decoding, and integrate parameter-efficient LoRA adapters. By the end, you will deploy optimized models using quantization and distillation formats.

Created by SkillCite Instructor

Last updated Aug 2026

Free

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This course includes:

4 weeks of structured content
6 video lessons & readings
5 hands-on lab exercises & quizzes
Verified certificate on completion

What you'll learn

Learning Outcomes

By the end of this course, you'll be able to:

Contrast transformer architectures with RNN and CNN frameworks.

Deconstruct self-attention blocks, Positional Encodings, and LayerNorm.

Differentiate encoder-only, decoder-only, and encoder-decoder model tasks.

Calibrate decoding parameters, including temperature and top-p sampling.

Evaluate efficient inference layouts, including quantization and distillation.

How this course opens doors

Transformer Block

Deconstruct self-attention layers, positional encodings, and residuals.

LLM Generation

Configure autoregressive decoding filters like nucleus and top-k sampling.

Efficient Inference

Deploy lightweight transformers using model quantization and distillation.

Access Duration

Course Content

Course curriculum

5 modules · 21 lessons

1. Funding Instruments & Capital Structure

~25 min

Equity, Debt, or Convertibles?~5 min

Anatomy of a Startup Cap Table~5 min

The Rise of Convertibles~5 min

Funding Instruments Check~10 min

2. Valuation and Equity Dilution

~25 min

3. Shareholders Agreements: Economic & Control Rights

~31 min

4. FDI, FEMA, and Regulatory Compliance

~25 min

5. Closing the Deal: Due Diligence & CPs

~25 min

Skills you'll gain

Skills Cloud

Self-Attention BlocksAutoregressive DecodingLoRA AdaptationModel QuantizationHugging Face PipelinesCausal Attention Masks

Target Audience

Who this course is for

AI Developers

Configure transformer model weights, parse tokens, and set pipelines.

Machine Learning Engineers

Train encoders, calibrate decoders, and tune parameters.

Technical Architects

Deploy efficient model inference formats and choose structures.

Offered by

Your instructor

SkillCite Instructor

SkillCite AI

86 courses

SkillCite Industry Expert Instructors bring decades of combined real-world expertise directly to you, making complex concepts simple.

Certification

Earn a Verifiable Certificate

Certificate of Completion

Learner Name

Investment Law Basics for Founders

SC-2026-VERIFIED

Verified

SkillCite Verified Certificate

Complete all modules and pass the final assessment to earn a shareable, verifiable digital credential.

Verifiable via public API with unique credential ID

Shareable on LinkedIn, Twitter, and personal portfolio

Recognized by employers and technology companies

Week-by-Week

Course Timeline

Week 1

Transformer Breakthroughs

Compare RNN/CNN limitations, review sequential attention paths, and map tasks.

Attention paperParallel pathwaysSequence models

Week 2

Transformer Blocks

Assemble scaled dot-product matrices, configure positional vectors, and set residuals.

Query/Key/ValuePositional vectorsLayerNorm blocks

Week 3

Decoder-Only Autoregression

Apply causal attention masks and analyze nucleus and top-k decoding strategies.

Causal masksNucleus samplingLLM decoders

Week 4

Training & Fine-Tuning

Optimize training goals, analyze LoRA parameter adapters, and write prompts.

LoRA parametersInstruction tuningFew-shot prompts

Week 5

Libraries & Quantization

Construct Hugging Face pipelines and apply quantization/distillation tools.

Pipeline setupsQuantizationModel distillation

Common Questions

FAQ

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