# Julia performance example
using BenchmarkTools

# Matrix multiplication in Julia
A = rand(1000, 1000)
B = rand(1000, 1000)

@benchmark A * B
# Typical result: ~1ms (vs ~10ms in Python with NumPy)

using Flux
using Flux: train!

# Define a simple neural network
model = Chain(
    Dense(784, 128, relu),
    Dense(128, 64, relu),
    Dense(64, 10)
)

# Define loss function
loss(x, y) = Flux.crossentropy(model(x), y)

# Training loop
function train_model!(model, data, epochs)
    opt = ADAM(0.001)
    
    for epoch in 1:epochs
        for (x, y) in data
            gs = gradient(() -> loss(x, y), Flux.params(model))
            Flux.update!(opt, Flux.params(model), gs)
        end
    end
end

using MLJ
using MLJBase

# Load a dataset
X, y = @load_iris

# Define a model
model = @load RandomForestClassifier pkg=ScikitLearn

# Create a machine
mach = machine(model, X, y)

# Train the model
fit!(mach)

# Make predictions
y_pred = predict(mach, X)

using DataFrames
using CSV

# Load data
df = CSV.read("data.csv", DataFrame)

# Data manipulation
df_clean = select(df, :feature1, :feature2, :target)
df_filtered = filter(:feature1 => x -> x > 0, df_clean)

# Group operations
df_grouped = groupby(df, :category)
df_summary = combine(df_grouped, :value => mean, :value => std)

using Plots

# Create visualizations
scatter(df.feature1, df.feature2, group=df.category)
histogram(df.value, bins=20)
plot(model_loss, label="Training Loss")

using Flux
using Images
using ImageIO

# Image preprocessing
function preprocess_image(image_path)
    img = load(image_path)
    img_resized = imresize(img, (224, 224))
    img_array = channelview(img_resized)
    return Float32.(img_array)
end

# CNN model for image classification
function create_cnn_model()
    return Chain(
        Conv((3, 3), 3 => 32, relu),
        MaxPool((2, 2)),
        Conv((3, 3), 32 => 64, relu),
        MaxPool((2, 2)),
        Conv((3, 3), 64 => 128, relu),
        MaxPool((2, 2)),
        flatten,
        Dense(128 * 7 * 7, 512, relu),
        Dense(512, 10)  # 10 classes
    )
end

# Training function
function train_cnn!(model, train_data, epochs)
    opt = ADAM(0.001)
    
    for epoch in 1:epochs
        for (x, y) in train_data
            loss_val = Flux.crossentropy(model(x), y)
            gs = gradient(() -> loss_val, Flux.params(model))
            Flux.update!(opt, Flux.params(model), gs)
        end
    end
end

using TextAnalysis
using WordTokenizers
using Embeddings

# Text preprocessing
function preprocess_text(text)
    # Tokenize
    tokens = tokenize(text)
    
    # Remove stopwords
    stopwords = ["the", "a", "an", "and", "or", "but"]
    filtered_tokens = [t for t in tokens if !(t in stopwords)]
    
    return filtered_tokens
end

# Simple sentiment analysis
function analyze_sentiment(text)
    positive_words = ["good", "great", "excellent", "amazing", "wonderful"]
    negative_words = ["bad", "terrible", "awful", "horrible", "disappointing"]
    
    tokens = preprocess_text(text)
    
    positive_count = sum([word in positive_words for word in tokens])
    negative_count = sum([word in negative_words for word in tokens])
    
    if positive_count > negative_count
        return "positive"
    elseif negative_count > positive_count
        return "negative"
    else
        return "neutral"
    end
end

using TimeSeries
using Plots
using Statistics

# Load time series data
function load_time_series(filename)
    data = CSV.read(filename, DataFrame)
    ts = TimeArray(data, timestamp=:date)
    return ts
end

# Moving average
function moving_average(ts, window)
    values = values(ts)
    ma = [mean(values[max(1, i-window+1):i]) for i in 1:length(values)]
    return TimeArray(timestamp(ts), ma)
end

# ARIMA model (simplified)
function arima_forecast(ts, p, d, q, forecast_periods)
    # This is a simplified version
    # In practice, you'd use a proper ARIMA implementation
    values = values(ts)
    n = length(values)
    
    # Simple linear trend
    trend = [i for i in 1:n]
    coeffs = [ones(n) trend] \ values
    
    # Forecast
    forecast = []
    for i in 1:forecast_periods
        next_val = coeffs[1] + coeffs[2] * (n + i)
        push!(forecast, next_val)
    end
    
    return forecast
end

using Flux
using Random

# DQN (Deep Q-Network) implementation
mutable struct DQNAgent
    q_network::Chain
    target_network::Chain
    replay_buffer::Vector
    epsilon::Float64
    gamma::Float64
    learning_rate::Float64
end

function DQNAgent(state_size, action_size, learning_rate=0.001)
    q_network = Chain(
        Dense(state_size, 128, relu),
        Dense(128, 128, relu),
        Dense(128, action_size)
    )
    
    target_network = deepcopy(q_network)
    
    return DQNAgent(
        q_network,
        target_network,
        [],
        1.0,  # epsilon
        0.99, # gamma
        learning_rate
    )
end

function select_action(agent::DQNAgent, state)
    if rand() < agent.epsilon
        return rand(1:4)  # Random action
    else
        q_values = agent.q_network(state)
        return argmax(q_values)
    end
end

function train_step!(agent::DQNAgent, batch)
    if length(agent.replay_buffer) < 32
        return
    end
    
    # Sample batch from replay buffer
    batch_indices = rand(1:length(agent.replay_buffer), 32)
    batch_data = [agent.replay_buffer[i] for i in batch_indices]
    
    # Training logic here
    # (Simplified for brevity)
end

using Distributed
using SharedArrays

# Add workers
addprocs(4)

# Distributed data processing
@everywhere function process_chunk(data_chunk)
    # Process a chunk of data
    return sum(data_chunk)
end

function distributed_processing(data)
    # Split data into chunks
    chunk_size = length(data) ÷ nworkers()
    chunks = [data[i:i+chunk_size-1] for i in 1:chunk_size:length(data)]
    
    # Process in parallel
    results = pmap(process_chunk, chunks)
    
    return sum(results)
end

# Efficient memory usage
function efficient_matrix_operations()
    # Pre-allocate arrays
    n = 10000
    A = zeros(Float32, n, n)  # Use Float32 instead of Float64
    B = zeros(Float32, n, n)
    
    # In-place operations
    A .= A .* 2.0  # In-place multiplication
    
    # Use views instead of copying
    submatrix = view(A, 1:100, 1:100)
    
    return A
end

using CUDA
using Flux

# Move model to GPU
model = Chain(
    Dense(784, 256, relu),
    Dense(256, 128, relu),
    Dense(128, 10)
) |> gpu

# Move data to GPU
x = rand(Float32, 784, 1000) |> gpu
y = rand(Float32, 10, 1000) |> gpu

# Training on GPU
function train_gpu!(model, x, y, epochs)
    opt = ADAM(0.001)
    
    for epoch in 1:epochs
        loss_val = Flux.crossentropy(model(x), y)
        gs = gradient(() -> loss_val, Flux.params(model))
        Flux.update!(opt, Flux.params(model), gs)
    end
end

using DataFrames
using Statistics
using Plots

# Portfolio optimization
function optimize_portfolio(returns, risk_free_rate=0.02)
    n_assets = size(returns, 2)
    mean_returns = mean(returns, dims=1)[:]
    cov_matrix = cov(returns)
    
    # Markowitz optimization
    # (Simplified implementation)
    weights = ones(n_assets) / n_assets  # Equal weights for now
    
    portfolio_return = dot(weights, mean_returns)
    portfolio_risk = sqrt(weights' * cov_matrix * weights)
    
    sharpe_ratio = (portfolio_return - risk_free_rate) / portfolio_risk
    
    return weights, portfolio_return, portfolio_risk, sharpe_ratio
end

using DifferentialEquations
using Plots

# Solve differential equations
function solve_ode()
    # Define the ODE
    function ode!(du, u, p, t)
        du[1] = -0.5 * u[1]
        du[2] = 0.5 * u[1] - 0.3 * u[2]
    end
    
    # Initial conditions
    u0 = [1.0, 0.0]
    tspan = (0.0, 10.0)
    
    # Solve
    prob = ODEProblem(ode!, u0, tspan)
    sol = solve(prob)
    
    return sol
end

# Module structure
module AIUtils

export preprocess_data, train_model, predict

function preprocess_data(data)
    # Data preprocessing logic
end

function train_model(model, data)
    # Training logic
end

function predict(model, input)
    # Prediction logic
end

end  # module AIUtils

using Test

# Unit tests
@testset "AI Functions" begin
    @test preprocess_data([1, 2, 3]) == [1.0, 2.0, 3.0]
    @test analyze_sentiment("This is great!") == "positive"
end

"""
    train_model(model, data, epochs)

Train a machine learning model on the given data.

# Arguments
- `model`: The model to train
- `data`: Training data
- `epochs`: Number of training epochs

# Returns
- Trained model

# Examples
```julia
model = Chain(Dense(10, 5), Dense(5, 1))
data = [(rand(10), rand(1)) for _ in 1:100]
trained_model = train_model(model, data, 10)

## Conclusion

Julia offers unique advantages for AI development, combining high performance with ease of use. Its growing ecosystem of ML libraries, excellent performance characteristics, and strong scientific computing capabilities make it an excellent choice for AI applications that require both speed and flexibility.

The key to success with Julia for AI development is understanding its performance characteristics, leveraging its multiple dispatch system, and taking advantage of its rich ecosystem. As the Julia AI ecosystem continues to grow, it's becoming an increasingly attractive option for serious AI development work.

Whether you're building computer vision applications, natural language processing systems, or complex scientific simulations, Julia provides the tools and performance you need to create high-quality AI applications efficiently.