Kania Prasasta

Eksplorasi tools digital, ide kreatif, dan cara bikin aktivitas harian lebih praktis ✨

[Kania Prasasta @kaniaprasasta32]

@AriaWestcott ini kepake bgt buat ngecek ide desain sendiri, kadang kita terlalu yakin sama konsep yg dibuat sampe lupa lihat celahnya wkwk

[Kania Prasasta @kaniaprasasta32]

@AriaWestcott kadang prompt yg spesifik emg bikin hasilnya beda jauh sih, gw sering dapet output yg lebih kepake pas ngasih konteks dan batasan yg jelas wkwk menarik buat dicoba satu satu

[Kania Prasasta @kaniaprasasta32]

@Hey_davidmax kadang prompt yg kita pake emg terlalu basic wkwk padahal cara ngasih konteks tuh ngaruh bgt ke hasilnya, jd pengen nyobain satu satu buat tugas visual gw

[Dhanian 🗯️ @e_opore]

LARGE LANGUAGE MODELS (LLMs) ROADMAP
│
├── 1. Programming Foundations
│ ├── Python Fundamentals
│ ├── Data Structures & Algorithms
│ ├── Object-Oriented Programming
│ ├── Functional Programming
│ ├── NumPy & Pandas
│ ├── Git & GitHub
│ ├── Linux & Command Line
│ └── APIs & JSON
│
├── 2. Mathematics for LLMs
│ ├── Linear Algebra
│ │ ├── Vectors & Matrices
│ │ ├── Matrix Multiplication
│ │ ├── Dot Products
│ │ └── Eigenvalues & Eigenvectors
│ ├── Calculus
│ │ ├── Derivatives
│ │ ├── Gradients
│ │ └── Chain Rule
│ ├── Probability Theory
│ ├── Statistics
│ ├── Probability Distributions
│ ├── Optimization
│ └── Gradient Descent
│
├── 3. Machine Learning Fundamentals
│ ├── Supervised Learning
│ ├── Unsupervised Learning
│ ├── Classification & Regression
│ ├── Training & Testing
│ ├── Feature Engineering
│ ├── Overfitting & Underfitting
│ ├── Loss Functions
│ ├── Model Evaluation
│ └── Scikit-learn
│
├── 4. Deep Learning Foundations
│ ├── Neural Networks
│ ├── Perceptrons
│ ├── Activation Functions
│ ├── Forward Propagation
│ ├── Backpropagation
│ ├── Gradient Descent
│ ├── Optimizers (Adam, SGD, AdamW)
│ ├── Loss Functions
│ ├── Regularization
│ ├── GPU Computing
│ └── PyTorch & TensorFlow
│
├── 5. Natural Language Processing (NLP)
│ ├── Text Preprocessing
│ ├── Tokenization
│ ├── Stemming & Lemmatization
│ ├── Bag of Words
│ ├── TF-IDF
│ ├── Word Embeddings
│ ├── Word2Vec & GloVe
│ ├── Sequence Modeling
│ ├── RNNs & LSTMs
│ ├── GRUs
│ └── Language Modeling
│
├── 6. Transformer Architecture
│ ├── Attention Mechanism
│ ├── Self-Attention
│ ├── Multi-Head Attention
│ ├── Scaled Dot-Product Attention
│ ├── Query, Key & Value
│ ├── Positional Encoding
│ ├── Feed-Forward Networks
│ ├── Layer Normalization
│ ├── Residual Connections
│ ├── Encoder Architecture
│ ├── Decoder Architecture
│ └── Encoder-Decoder Architecture
│
├── 7. Understanding Large Language Models
│ ├── What Are LLMs?
│ ├── How LLMs Work
│ ├── LLM Architecture
│ ├── Parameters & Model Weights
│ ├── Pretraining
│ ├── Next-Token Prediction
│ ├── Context Windows
│ ├── Inference & Generation
│ ├── Temperature & Sampling
│ ├── Top-k & Top-p Sampling
│ ├── Hallucinations
│ └── Open-Source vs Closed-Source Models
│
├── 8. Tokenization & Embeddings
│ ├── Tokenization Fundamentals
│ ├── Byte Pair Encoding (BPE)
│ ├── WordPiece
│ ├── SentencePiece
│ ├── Token IDs
│ ├── Embedding Spaces
│ ├── Semantic Similarity
│ ├── Cosine Similarity
│ ├── Positional Embeddings
│ └── Contextual Embeddings
│
├── 9. Popular LLM Architectures & Models
│ ├── GPT Models
│ ├── BERT
│ ├── T5
│ ├── Llama
│ ├── Mistral
│ ├── Qwen
│ ├── Gemma
│ ├── DeepSeek
│ ├── Phi
│ ├── Mixture of Experts (MoE)
│ ├── Encoder-Only Models
│ ├── Decoder-Only Models
│ └── Multimodal LLMs
│
├── 10. Prompt Engineering
│ ├── Zero-Shot Prompting
│ ├── Few-Shot Prompting
│ ├── Chain-of-Thought Prompting
│ ├── Role-Based Prompting
│ ├── System & User Instructions
│ ├── Context Engineering
│ ├── Structured Outputs
│ ├── JSON Mode
│ ├── Prompt Templates
│ ├── Prompt Optimization
│ └── Prompt Injection Defense
│
├── 11. LLM APIs & Development Tools
│ ├── OpenAI API
│ ├── Anthropic API
│ ├── Google Gemini API
│ ├── Hugging Face Transformers
│ ├── Ollama
│ ├── vLLM
│ ├── LiteLLM
│ ├── LangChain
│ ├── LlamaIndex
│ ├── Vercel AI SDK
│ └── Model Context Protocol (MCP)
│
├── 12. Fine-Tuning & Model Adaptation
│ ├── Pretraining vs Fine-Tuning
│ ├── Supervised Fine-Tuning (SFT)
│ ├── Instruction Tuning
│ ├── Parameter-Efficient Fine-Tuning
│ ├── LoRA
│ ├── QLoRA
│ ├── Adapters
│ ├── Quantization
│ ├── RLHF
│ ├── Direct Preference Optimization (DPO)
│ ├── Dataset Preparation
│ └── Fine-Tuning Evaluation
│
├── 13. Retrieval-Augmented Generation (RAG)
│ ├── RAG Fundamentals
│ ├── Document Loading
│ ├── Text Extraction
│ ├── Document Chunking
│ ├── Embedding Generation
│ ├── Vector Databases
│ │ ├── Pinecone
│ │ ├── Chroma
│ │ ├── Weaviate
│ │ ├── Qdrant
│ │ └── Milvus
│ ├── Semantic Search
│ ├── Hybrid Search
│ ├── Metadata Filtering
│ ├── Reranking
│ ├── Query Transformation
│ ├── Advanced RAG
│ └── RAG Evaluation
│
├── 14. AI Agents & Tool Calling
│ ├── AI Agent Fundamentals
│ ├── Function Calling
│ ├── Tool Use
│ ├── Agent Planning
│ ├── Memory Systems
│ ├── Short-Term & Long-Term Memory
│ ├── Reflection & Self-Correction
│ ├── ReAct Pattern
│ ├── Agentic Workflows
│ ├── Multi-Agent Systems
│ ├── LangGraph
│ ├── CrewAI
│ ├── AutoGen
│ └── Agent Evaluation & Safety
│
├── 15. Multimodal LLMs
│ ├── Vision-Language Models
│ ├── Image Understanding
│ ├── Image-to-Text
│ ├── Audio Understanding
│ ├── Speech-to-Text
│ ├── Text-to-Speech
│ ├── Video Understanding
│ ├── Multimodal Embeddings
│ ├── Vision Transformers (ViT)
│ └── Multimodal RAG
│
├── 16. LLM Evaluation & Observability
│ ├── Model Evaluation Metrics
│ ├── Perplexity
│ ├── BLEU & ROUGE
│ ├── Benchmarking
│ ├── Human Evaluation
│ ├── LLM-as-a-Judge
│ ├── RAGAS
│ ├── DeepEval
│ ├── Hallucination Detection
│ ├── Tracing & Logging
│ ├── Prompt Versioning
│ └── Production Monitoring
│
├── 17. LLM Inference & Optimization
│ ├── Model Serving
│ ├── GPU & Memory Management
│ ├── Quantization (INT8, INT4)
│ ├── Model Pruning
│ ├── Knowledge Distillation
│ ├── KV Cache
│ ├── Continuous Batching
│ ├── Speculative Decoding
│ ├── FlashAttention
│ ├── Tensor Parallelism
│ ├── Pipeline Parallelism
│ └── Distributed Inference
│
├── 18. LLM Security & Responsible AI
│ ├── Prompt Injection
│ ├── Jailbreak Attacks
│ ├── Data Leakage
│ ├── Sensitive Information Protection
│ ├── Output Validation
│ ├── Guardrails
│ ├── Content Safety
│ ├── Model Bias & Fairness
│ ├── Adversarial Testing
│ └── AI Governance
│
├── 19. LLMOps & Production Deployment
│ ├── Docker
│ ├── Kubernetes
│ ├── FastAPI
│ ├── Model Serving APIs
│ ├── MLflow
│ ├── Weights & Biases
│ ├── CI/CD Pipelines
│ ├── Cloud Deployment
│ ├── AWS Bedrock & SageMaker
│ ├── Google Vertex AI
│ ├── Azure AI Foundry
│ ├── Cost & Latency Optimization
│ └── Production Reliability
│
├── 20. Build Real-World LLM Projects
│ ├── AI Chatbot
│ ├── PDF Question-Answering System
│ ├── Semantic Search Engine
│ ├── RAG-Based Knowledge Assistant
│ ├── AI Code Assistant
│ ├── AI Research Assistant
│ ├── Customer Support AI Agent
│ ├── Multimodal AI Assistant
│ ├── Fine-Tuned Domain-Specific LLM
│ ├── Multi-Agent Research System
│ ├── Local LLM Deployment Platform
│ └── Production-Ready LLM SaaS
│
└── 21. Career & Professional Growth
├── Build an LLM Engineering Portfolio
├── Publish Projects on GitHub
├── Master Python & SQL Interviews
├── Learn AI System Design
├── Study LLM Research Papers
├── Reproduce Research Implementations
├── Contribute to Open Source
├── Participate in AI Communities
├── Write Technical Articles
└── Prepare for LLM Engineer Interviews

FINAL GOAL:
Become a Production-Ready LLM Engineer
Capable of Building, Fine-Tuning, Evaluating,
Optimizing & Deploying Large Language Models.

[Kania Prasasta @kaniaprasasta32]

@fun_math21 fitur adaptive sama smart analysis-nya menarik sih, tp bug-nya agak ganggu yaa apalagi kalo lagi serius latihan soal wkwk

[Kania Prasasta @kaniaprasasta32]

@fun_math21 penasaran sama perbandingan fiturnya, biasanya tiap app punya cara nyusun materi yg beda dan bisa jadi refrensi buat nemuin workflow belajar yg paling cocok jugaa

[Kania Prasasta @kaniaprasasta32]

@fun_math21 belajar dari 0 emg paling bingung tuh nentuin alurnya 😭 pengen coba bikin rangkuman pake AI biar materi yg numpuk lebih gampang dipahami