AI Onboarding & Python Essentials

Course Introduction and Philosophy

Setting Up Accountability and Tools

Python, Google Colab & Jupyter Notebooks for AI

Arrays, Vectors & Tensors in Practice for Foundational Knowledge

Mathematical Foundations for ML

Statistics and Data Preprocessing

Probability and Tensor Basics

Onboarding & Tooling

Understanding LLM Projects and Modalities

LLM Use Cases Across Industries

Limitations of LLMs

LLM Inference Basics

Building Your First LLM Application

Introduction to AI-Centric Evaluation

Mini-Project: Synthetic Data Generation with Evaluation

AI Projects and Use Cases

Foundational Prompt Engineering

Building and Evaluating Prompts

Advanced Prompting with Context Engineering

Text to Tokens to Embeddings

Working with Embeddings

Multimodal Embeddings for Retrieval

Prompt Engineering & Embeddings

Introduction to CLIP and Multimodal Embeddings

Prompt Engineering with Images

Advanced Multimodal Tasks

RAG Pipeline Overview

Vector Databases and Query Optimization

RAG Evaluation & Implementation

Mini-Project: Incremental RAG Evaluation for PDF Lectures

Multimodal + Retrieval-Augmented Systems

Introduction to N-Gram Models

Building and Sampling N-Gram Models

Evaluating N-Gram Models

Neural N-Gram Models

Biplet Loss for Embeddings

Classical Language Models

Motivation for Attention Mechanisms

Mechanics of Self-Attention

Implementing Self-Attention

Multi-Head Attention & Mixture of Experts (MoE)

Instructional Finetuning & LoRA

Finetuning Case Studies

Attention & Finetuning

Feedforward Networks in Transformers

FFN Components & Training

Multimodal Finetuning with CLIP

Advanced Multimodal Applications

Mini-Project: Generating Synthetic Data & Finetuning for Evaluation

Architectures & Multimodal Systems

Building a Full Transformer

Debugging & Testing Transformers

Monkeywrenching into LLaMA

Advanced RAG Systems

RAG Evaluation and Optimization

Assembling & Training Transformers

CLIP Finetuning for Insurance

Deploying Finetuned CLIP Models

Math Reasoning with SymPy

Tool-Augmented Finetuning

Specialized Finetuning Projects

Preference-Based Finetuning

Evaluating Preference Alignment

Reverse Engineering Vibe Coding Agents

Designing AI Code Agents

Advanced RLHF & Engineering Architectures

Agent Design Patterns

Agent Architectures and Toolkits

Text-to-SQL Systems

Text-to-Voice Pipelines

Agents & Multimodal Code Systems

Positional Encoding in Transformers

DeepSeek-V3 Architecture

LLM Production Chain

LLMOps & Scalable Serving

Deep Internals & Production Pipelines

RAG in Enterprise Settings

Evaluating Model Trustworthiness

AI Career Roles and Preparation

Bonus Content

Enterprise LLMs, Hallucinations & Career Growth

In this bootcamp, we dive deep into Large Language Models (LLMs) to help you understand, build, and optimize their architecture for real-world applications. LLMs are revolutionizing industries—from customer support to content creation—but understanding how these models work and optimizing them for specific tasks presents unique challenges.

Over an intensive, multi-week curriculum, we cover:

The technical foundations of LLMs, including autoregressive decoding, positional encoding, and multi-head attention.
The LLM lifecycle—from large-scale pretraining to fine-tuning and instruction tuning for niche applications.
Industry best practices for model evaluation, identifying performance bottlenecks, and employing cutting-edge architectures to balance efficiency and scalability.
This bootcamp includes hours of in-depth instruction, hands-on coding sessions, and access to a dedicated community for ongoing support and discussions. Additionally, you’ll have exclusive access to code templates, an expansive reference library, and downloadable resources for continuous learning.

Your expert guides through this bootcamp are:

[Alvin Wan](https://www.linkedin.com/in/alvinwan/): Alvin specializes in large language models and efficient AI design. Previously, he was a Senior Research Scientist at Apple, working on AI and large language models for Apple Intelligence. Alvin also worked on Tesla’s AutoPilot and holds a PhD from UC Berkeley, where his research has garnered over 3,000 citations. He brings a unique combination of industry expertise and cutting-edge research to this course, guiding you through the technical aspects of building, optimizing, and deploying LLMs.

[Zao Yang](https://www.linkedin.com/in/zaoyang/): Zao is a co-founder of Newline, a platform used by 150k professionals from companies like Salesforce, Adobe, Disney, and Amazon. Zao has a rich history in the tech industry, co-creating Farmville (200 million users, $3B revenue) and Kaspa ($3B market cap). Self-taught in deep learning, generative AI, and machine learning, Zao is passionate about empowering others to develop practical AI applications. His extensive knowledge of both the technical and business sides of AI projects will be invaluable as you work on your own.

With Alvin and Zao's guidance, you’ll gain practical insights into building and deploying advanced AI models, preparing you for the most challenging and rewarding roles in the AI field.

You’ll receive a comprehensive set of resources to help you master large language models.

Resources

Unlock exclusive bonuses to accelerate your AI journey.

Bonus

Everyone’s heard of ChatGPT, but what truly powers these modern large language models? It all starts with the transformer architecture. This bootcamp demystifies LLMs, taking you from concept to code and giving you a full, hands-on understanding of how transformers work. You’ll gain intuitive insights into the core components—autoregressive decoding, multi-head attention, and more—while bridging theory, math, and code. By the end, you’ll be ready to understand, build, and optimize LLMs, with the skills to read research papers, evaluate models, and confidently tackle ML interviews.

AI Bootcamp

Be able to build large language models, which can increase your salaries by $50k a year. Worth $500k over 10 years

Cheatsheet on generative AI interviews for FANGs, a $50k a year over a $500k value

A complete course on end to end streaming Langchain with a fully functional application for startups. $15k in value

Be able run consulting in AI $100k in annual value. Over 10 years. $1m

Be able to build an AI company $1M in annual value

Technical and business design review from Alvin and Zao about your project. $25000 dollars in value

$3.4M in value. This will be a $10k to $15k bootcamp in the future

What You will Gain

Understand the lifecycle of large language models, from training to inference

Build and deploy a fully functional LLM Inference API

Master tokenization techniques, including byte-pair encoding and word embeddings

Develop foundational models like n-grams and transition to transformer-based models

Implement self-attention and feed-forward neural networks in transformers

Evaluate LLM performance using metrics like perplexity

Deploy models using modern tools like Huggingface, Modal, and TorchScript

Adapt pre-trained LLMs through fine-tuning and retrieval-augmented generation (RAG)

Leverage state-of-the-art tools for data curation and adding ethical guardrails

Apply instruction-tuning techniques with low-rank adapters

Explore multi-modal LLMs integrating text, voice, images, and robotics

Understand machine learning operations, from project scoping to deployment

Design intelligent agents with planning, reflection, and collaboration capabilities

Keep up-to-date with AI trends, tools, and industry best practices

Receive technical reviews and mentorship to refine your projects

Create a robust portfolio showcasing real-world AI applications

---
title: Biplet Loss for Embeddings
privateVideoUrl: 
isPublicLesson: true
---
