Deep Dive into DeepSeek
Log in

Go beyond the hype. Master DeepSeek.

From architecture internals and local deployment to fine-tuning, RAG pipelines, and production-ready apps — this is the complete technical deep dive into DeepSeek's open-source model family that no blog post or Twitter thread can give you.

25 lessonsAI-adaptiveCancel anytimeLearn anywhere
Deep Dive into DeepSeek

I don't care about impressing you with complexity — I care about you leaving here able to build things you couldn't build before.— Freddy Foster

What you'll learn

What you'll be able to do

  • Understand DeepSeek's model architecture and how it differs from other leading LLMs
  • Run DeepSeek models locally and via API with full configuration control
  • Prompt DeepSeek's reasoning models effectively for complex, multi-step tasks
  • Fine-tune and customize DeepSeek models for specific use cases and datasets
  • Build and deploy a real-world AI application powered by DeepSeek
  • Evaluate DeepSeek's performance benchmarks and choose the right model variant for any project

How it works

A school that adapts to you

This isn't a set of static videos. Every lesson is generated live and tuned to where you actually are.

We learn your level

A quick placement check tailors your starting point so you're never bored or lost.

Lessons adapt as you go

Each lesson is written for your pace and your goal, adjusting as your skills grow.

Your AI coach keeps you moving

Checkpoints, feedback, and gentle nudges turn progress into a real result.

The curriculum

What's inside your school

6 modules · 25 lessons

1

DeepSeek Demystified: Architecture & Model Landscape

Understand what DeepSeek is, how its model family is structured, and what sets it apart from other leading LLMs.

  • 1.1The DeepSeek Story: Origins, Mission & Open-Source PhilosophyIncluded
  • 1.2Meet the Model Family: V2, V3, R1 & BeyondIncluded
  • 1.3Under the Hood: MoE Architecture & Key Technical InnovationsIncluded
  • 1.4Reasoning Models Unpacked: How DeepSeek-R1 ThinksIncluded
  • 1.5Benchmarks & Model Selection: Picking the Right VariantIncluded
2

Getting Hands-On: Local Setup & API Access

Get DeepSeek models running in your own environment — both locally and through the API — with full configuration control.

  • 2.1Running DeepSeek Locally with Ollama & LM StudioIncluded
  • 2.2Hardware Requirements, Quantization & Performance TuningIncluded
  • 2.3Accessing DeepSeek via API: Authentication, Endpoints & Rate LimitsIncluded
  • 2.4Configuration Deep Dive: Temperature, Top-P, Max Tokens & MoreIncluded
3

Prompting DeepSeek for Maximum Performance

Master prompting techniques specifically tuned for DeepSeek's chat and reasoning models to unlock their full capability.

  • 3.1Prompting Fundamentals: System Prompts, Roles & Context WindowsIncluded
  • 3.2Advanced Prompting: Chain-of-Thought, Few-Shot & Self-ConsistencyIncluded
  • 3.3Prompting R1: Triggering and Steering Deep ReasoningIncluded
  • 3.4Avoiding Pitfalls: Hallucinations, Refusals & Prompt InjectionIncluded
4

Fine-Tuning & Customization

Adapt DeepSeek models to your own data and domain through fine-tuning, alignment techniques, and efficient training methods.

  • 4.1Fine-Tuning Fundamentals: When, Why & What Data You NeedIncluded
  • 4.2Supervised Fine-Tuning with LoRA & QLoRAIncluded
  • 4.3Alignment & Preference Tuning with DPOIncluded
  • 4.4Evaluating Your Fine-Tuned Model: Metrics & Human ReviewIncluded
5

Building Real-World Applications with DeepSeek

Integrate DeepSeek into production-grade application patterns including RAG pipelines, agents, and multi-model systems.

  • 5.1Connecting DeepSeek to Your Stack: LangChain & LlamaIndex IntegrationsIncluded
  • 5.2Building a RAG Pipeline: Retrieval-Augmented Generation from ScratchIncluded
  • 5.3Agentic DeepSeek: Tool Use, Function Calling & Multi-Step WorkflowsIncluded
  • 5.4Capstone Project: Design & Build a DeepSeek-Powered ApplicationIncluded
6

Deployment, Scaling & Production Best Practices

Deploy DeepSeek applications reliably at scale while managing cost, latency, security, and ongoing performance.

  • 6.1Cloud Deployment Options: AWS, GCP, Azure & Dedicated GPU HostsIncluded
  • 6.2Serving at Scale: vLLM, TGI & Inference OptimizationIncluded
  • 6.3Security, Privacy & Data Governance for DeepSeek AppsIncluded
  • 6.4Monitoring, Logging & Continuous Model Evaluation in ProductionIncluded

Who it's for

Is this you?

Backend Developers

You're comfortable with APIs and Python and want to integrate a powerful, self-hostable LLM into your stack without being locked into OpenAI.

ML Engineers

You want to go beyond inference and get hands-on with fine-tuning, LoRA/QLoRA, DPO alignment, and rigorous model evaluation on your own datasets.

AI Product Builders

You're building an AI-powered product and need to understand which DeepSeek model variant is right for your use case, and how to deploy it reliably at scale.

Curious Tech Leads

You're responsible for evaluating AI tooling for your team and need a rigorous, technically grounded understanding of what DeepSeek can and can't do.

Open-Source AI Enthusiasts

You care deeply about open-source AI and want to run, understand, and contribute to cutting-edge models without depending on closed, proprietary systems.

LLM Prompt Engineers

You've mastered basic prompting and want to level up with chain-of-thought, self-consistency, and the specific techniques that unlock R1's reasoning capabilities.

Questions

Frequently asked

Your teacher

A note from your teacher

Freddy Foster

Freddy Foster

If you've been watching the DeepSeek story unfold and thinking "I need to actually get my hands on this" — you're in the right place.\n\nI built this course because the gap between the hype and the practical reality was too wide. The headlines told you DeepSeek was revolutionary. The Twitter threads gave you benchmarks without context. The blog posts showed you a curl command and called it a tutorial. None of that gets you to the point where you can actually build something serious, make an informed decision about which model variant to use, or explain to a technical stakeholder why DeepSeek's MoE architecture changes the cost-performance equation. That's what this course is for.\n\nWe go end-to-end and we go deep. Architecture first — not because it's academic, but because understanding how the model family is structured (and how R1's reasoning process actually works) changes how you prompt, fine-tune, and deploy. Then we get hands-on immediately: local setup, API access, configuration control. From there, prompting with precision, fine-tuning with LoRA and QLoRA, building RAG pipelines and agentic workflows, and finally shipping to production with the infrastructure, security, and monitoring practices that real deployments demand.\n\nI don't believe in padding courses with content that sounds impressive but doesn't change what you can do. Every section of this course exists because it unlocks a real capability. The capstone project isn't a checkbox — it's the point where everything you've learned becomes something you've actually built.\n\nIf you're a developer, ML engineer, or technically serious AI practitioner who wants to work with one of the most important open-source model families in the field right now, I'd be glad to have you in the course. Let's get into it.

— Freddy Foster

Start your journey today

Join get instant access — learn at your own pace with an AI coach in your corner.

$59/mo

Recurring billing · cancel anytime

Secure checkout · Instant access

  • 6 modules, 25 lessons
  • AI-adaptive lessons tuned to your level
  • Quizzes & checkpoints to lock in progress
  • Your own AI learning coach
  • Learn on any device, at your pace
  • Full access for as long as you're subscribed