Скачать или смотреть Building AI Framework from Scratch SetUp Part 5

Creating an offline AI from scratch is a complex task, but here's a practical roadmap to get you started:

1. Foundational Knowledge First
Mathematics: Linear algebra, calculus, probability, statistics

Programming: Python is standard (learn NumPy, SciPy, pandas)

Core CS: Algorithms, data structures, optimization

2. Start with Simple Models
Don't jump to LLMs or complex neural networks immediately:

Classical Machine Learning (implement these first):
Linear/Logistic Regression (from scratch with NumPy)

Decision Trees/Random Forests

k-Nearest Neighbors

Naive Bayes Classifier

Sample project progression:
Digit recognition using MNIST dataset

Text classification (spam detection)

Recommendation system

3. Build Your Own Neural Network
Create from absolute scratch (no frameworks):
4. Local AI Stack
Set up offline development environment:

Essential Tools:
Python with Jupyter Notebook

ONNX Runtime for inference (works offline)

OpenBLAS for optimized math operations

SQLite for local data storage

Small Models to Run Locally:
TinyBERT or DistilBERT for NLP

MobileNet for computer vision

Whisper tiny for speech recognition

Phi-2 or TinyLlama (requires ~3-5GB RAM)

5. Practical Implementation Steps
Week 1-4: Math & Basic ML
Implement gradient descent from scratch

Build a perceptron

Create a full logistic regression system

Week 5-8: Neural Networks
Implement backpropagation manually

Build a 2-layer neural network

Add activation functions (ReLU, sigmoid, tanh)

Week 9-12: Computer Vision Basics
Implement CNN layers (convolution, pooling)

Train on CIFAR-10 dataset

Add dropout, batch normalization

Week 13-16: NLP Fundamentals
Build a simple RNN/LSTM

Implement word embeddings (Word2Vec from scratch)

Create a text generation model

6. Resources for Learning
Books: "Deep Learning" by Goodfellow, Bengio, Courville

Courses: Fast.ai (practical), Andrew Ng's ML/Deep Learning

Code: Implement papers from arXiv with explanations

Datasets: UCI Repository, Kaggle datasets (download first)

7. Optimization for Offline Use
Quantization: Reduce model precision (FP32 → INT8)

Pruning: Remove unimportant neurons

Knowledge Distillation: Train smaller student models

Embeddings: Use pre-computed embeddings offline

8. What "From Scratch" Really Means
Level 1: Using NumPy but no ML frameworks

Level 2: Implementing even matrix operations yourself

Level 3: C++/Rust implementation for performance

Level 4: Hardware-aware optimization

Realistic Expectations:
A useful offline AI assistant with basic Q&A: 6-12 months of serious effort

State-of-the-art performance: Not realistic solo in reasonable time

Focus areas: Specialized tasks (document search, personal assistant, image classification)

Quick Start Project:
Build a document search system:

Download Wikipedia dump

Create embeddings with Word2Vec (your implementation)

Build nearest-neighbor search with FAISS (compiled locally)

Create simple web interface

Would you like me to elaborate on any specific area or help you with a particular implementation step?