Deep Learning and Transformers: worked example and design decisions | Complete Generative AI: From Foundations to Production
A self-paced 26-week learning path with 15 modules: AI foundations, Python, machine learning, transformers, LLMs, prompts, APIs, embeddings, RAG, frameworks, agents, customization, multimodal AI, Spring Boot with React, and production LLMOps. Includes 15 practical assignments and three portfolio projects.
Prerequisites: Begin with basic computer skills. Programming is introduced in Weeks 2–3. Use a local Python environment for coding labs; Java and React experience helps in Weeks 21–22. Model downloads, provider accounts and deployment resources are optional learner-managed dependencies. The 26-week schedule is suggested; reading estimates exclude independent project work.
Walk through an example
The example uses normalized toy vectors to make similarity visible. These vectors are hand-written and are not outputs from a trained language model. A dot product between unit vectors equals cosine similarity. When visualizing a real tokenizer, use the tokenizer associated with the selected model, display token IDs and offsets, and compare punctuation, spaces and non-English text. Character splitting is useful as a contrast but must not be labeled a production model tokenizer.
import math
def unit(v):
length = math.sqrt(sum(x*x for x in v))
if not length: raise ValueError("zero vector")
return [x/length for x in v]
def cosine(a, b):
return sum(x*y for x, y in zip(unit(a), unit(b)))
print(round(cosine([1, 0], [1, 1]), 3))
print(round(cosine([1, 0], [-1, 0]), 3))
Expected behavior
0.707
-1.0 Read the example before adapting it. The code is displayed for study and local use; the academy does not execute it or contact an AI provider.
Review the boundary
List the assumptions in this example and identify which inputs come from an untrusted caller. Change one assumption at a time and write the expected behavior before running a local experiment. Preserve both successful and failed cases in your project notes. A demonstration that works once does not establish accuracy, security or production readiness.
Attention Is All You Need — original transformer paper
01. Introduction to Artificial Intelligence · Week 1
02. Python Programming for Gen AI · Weeks 2–3
03. Mathematics and Machine Learning Foundations · Week 4
04. Deep Learning and Transformers · Weeks 5–6
05. Large Language Models (LLMs) · Weeks 7–8
06. Prompt Engineering · Weeks 9–10
07. Generative AI APIs and Application Development · Week 11
08. Embeddings and Vector Databases · Weeks 12–13
09. Retrieval-Augmented Generation (RAG) · Weeks 14–15
10. LangChain, LlamaIndex and AI Frameworks · Week 16
11. AI Agents and Agentic AI · Weeks 17–18
12. Fine-Tuning and Model Customization · Week 19
13. Multimodal Generative AI · Week 20
14. Gen AI with Java Spring Boot and React · Weeks 21–22
15. Production Gen AI, LLMOps and Capstone · Weeks 23–26