EzeniIa

Artificial Intelligence Guide

Master machine learning fundamentals, model training, and deployment techniques.

Guide Outline

1. AI Fundamentals
2. Environment Setup
3. Building Models
4. Best Practices
5. Deployment

1. Introduction to AI

Artificial Intelligence (AI) encompasses systems capable of performing tasks requiring human intelligence. From machine learning to deep learning, AI technologies are revolutionizing industries.

"AI is the new electricity that will power every industry." - Andrew Ng

Why Learn AI?

Build intelligent applications
Analyze massive datasets
Predict future trends

Environment Setup

Install required tools:

python3 -m venv env
source env/bin/activate
pip install tensorflow
pip install torch torchvision

✅

Deep Learning

TensorFlow and PyTorch are popular frameworks
Development Tools

Use Jupyter Notebooks for interactive development

Building Your First Model

{`import tensorflow as tf from tensorflow import keras model = tf.keras.Sequential([ keras.layers.Dense(64, activation='relu', input_shape=(32,)), keras.layers.Dense(64, activation='relu'), keras.layers.Dense(10) ]) model.compile(optimizer='adam', loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True), metrics=['accuracy'])`}

🔧

This simple feed-forward neural network demonstrates basic model architecture.

Best Practices for AI Development

Deploying AI Models

Python Setup

Model training with PyTorch:

import torch
from torch import nn

class NeuralNetwork(nn.Module):
    def __init__(self):
        super().__init__()
        self.flatten = nn.Flatten()
        self.linear_relu_stack = nn.Sequential(
            nn.Linear(28*28, 512),
            nn.ReLU(),
            nn.Linear(512, 512),
            nn.ReLU(),
            nn.Linear(512, 10)
        )

    def forward(self, x):
        x = self.flatten(x)
        logits = self.linear_relu_stack(x)
        return logits

Model Serving

Use Flask for API endpoints:

from flask import Flask, request
import pickle

app = Flask(__name__)
model = pickle.load(open('model.pkl', 'rb'))

@app.route('/predict', methods=['POST'])
def predict():
    data = request.get_json()
    prediction = model.predict([data])
    return {'prediction': prediction.tolist()}

Next Step: Model Optimization

Explore techniques like quantization and pruning for deployment efficiency.