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| |
| |
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| font-size: 0.65rem; |
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| } |
| |
| |
| .pipeline { |
| display: flex; |
| flex-wrap: wrap; |
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| margin: var(--space-xl) 0; |
| padding: var(--space-lg); |
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| |
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| flex: 1; |
| min-width: 120px; |
| text-align: center; |
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| |
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| content: '->'; |
| position: absolute; |
| right: -15px; |
| top: 50%; |
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| font-size: 1rem; |
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| |
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| width: 36px; |
| height: 36px; |
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| justify-content: center; |
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| font-size: 0.9rem; |
| font-weight: 700; |
| color: white; |
| margin: 0 auto var(--space-sm); |
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| |
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| font-size: 0.85rem; |
| font-weight: 600; |
| color: var(--text-primary); |
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| |
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| font-size: 0.75rem; |
| color: var(--text-muted); |
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| |
| |
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| list-style: none; |
| padding: 0; |
| margin: var(--space-lg) 0; |
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| |
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| align-items: flex-start; |
| gap: var(--space-sm); |
| padding: var(--space-sm) 0; |
| font-size: 0.95rem; |
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| } |
| |
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| content: '[v]'; |
| color: var(--success); |
| font-family: 'JetBrains Mono', monospace; |
| font-weight: 700; |
| flex-shrink: 0; |
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| |
| |
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| font-size: 0.7rem; |
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| |
| |
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| gap: var(--space-md); |
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| |
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| |
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| font-size: 0.9rem; |
| font-weight: 600; |
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| |
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| margin: 0; |
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| |
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| |
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| content: '>'; |
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| margin-right: var(--space-sm); |
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| |
| |
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| grid-template-columns: repeat(auto-fit, minmax(200px, 1fr)); |
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| margin: var(--space-lg) 0; |
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| |
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| padding: var(--space-lg); |
| text-align: center; |
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| } |
| |
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| border-color: var(--primary); |
| transform: translateY(-3px); |
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| |
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| |
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| font-weight: 600; |
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| |
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| font-size: 0.75rem; |
| color: var(--text-muted); |
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| |
| |
| @media (max-width: 768px) { |
| .sidebar { |
| transform: translateX(-100%); |
| transition: transform var(--transition-base); |
| } |
| |
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| transform: translateX(0); |
| } |
| |
| .main-content { |
| margin-left: 0; |
| padding: var(--space-lg); |
| } |
| |
| .comparison-grid { |
| grid-template-columns: 1fr; |
| } |
| |
| .pipeline-step:not(:last-child)::after { |
| display: none; |
| } |
| } |
| |
| |
| .back-to-top { |
| position: fixed; |
| bottom: var(--space-xl); |
| right: var(--space-xl); |
| width: 44px; |
| height: 44px; |
| background: var(--primary); |
| border: none; |
| border-radius: 50%; |
| color: white; |
| font-size: 1.2rem; |
| cursor: pointer; |
| opacity: 0; |
| visibility: hidden; |
| transition: all var(--transition-base); |
| z-index: 1000; |
| display: flex; |
| align-items: center; |
| justify-content: center; |
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| |
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| opacity: 1; |
| visibility: visible; |
| } |
| |
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| background: var(--primary-light); |
| transform: translateY(-3px); |
| } |
| |
| |
| .author-footer { |
| background: var(--bg-secondary); |
| border-top: 1px solid var(--border-color); |
| padding: var(--space-xl); |
| text-align: center; |
| } |
| |
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| display: flex; |
| justify-content: center; |
| gap: var(--space-xl); |
| flex-wrap: wrap; |
| margin-bottom: var(--space-md); |
| } |
| |
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| display: flex; |
| align-items: center; |
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| text-decoration: none; |
| font-size: 0.9rem; |
| transition: color var(--transition-base); |
| } |
| |
| .author-link:hover { |
| color: var(--primary-light); |
| } |
| |
| .author-link svg { |
| width: 18px; |
| height: 18px; |
| fill: currentColor; |
| } |
| </style> |
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| <div class="brand-logo">ML</div> |
| <span>ML Academy</span> |
| </a> |
| <div class="navbar-nav"> |
| <a href="index.html" class="nav-link"> |
| <span class="nav-icon">[H]</span> |
| <span>Accueil</span> |
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| <a href="cours.html" class="nav-link active"> |
| <span class="nav-icon">[C]</span> |
| <span>Cours</span> |
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| <a href="tp.html" class="nav-link"> |
| <span class="nav-icon">[T]</span> |
| <span>TPs</span> |
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| <span>Contact</span> |
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| <div class="sidebar-header"> |
| <div class="sidebar-badge">Formation 2025/2026</div> |
| <h1 class="sidebar-title">Machine Learning</h1> |
| <p class="sidebar-subtitle">Cours theoriques complets</p> |
| <a href="cours.pdf" class="pdf-download" download> |
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| <div class="nav-section-title">Introduction</div> |
| <a href="#intro" class="nav-item active"> |
| <span class="nav-icon">[1]</span> |
| <span>Qu'est-ce que le ML ?</span> |
| </a> |
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| |
| <div class="nav-section"> |
| <div class="nav-section-title">Algorithmes Supervises</div> |
| <a href="#regression" class="nav-item"> |
| <span class="nav-icon">[2]</span> |
| <span>Regression Lineaire</span> |
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| <a href="#logistic" class="nav-item"> |
| <span class="nav-icon">[3]</span> |
| <span>Regression Logistique</span> |
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| <span class="nav-icon">[4]</span> |
| <span>Random Forest</span> |
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| <div class="nav-section-title">Deep Learning</div> |
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| <span>Reseaux de Neurones</span> |
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| <a href="#lstm" class="nav-item"> |
| <span class="nav-icon">[6]</span> |
| <span>LSTM & Series Temp.</span> |
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| <div class="nav-section-title">Evaluation</div> |
| <a href="#metrics" class="nav-item"> |
| <span class="nav-icon">[7]</span> |
| <span>Metriques</span> |
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| <span>Optimisation</span> |
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| <span>Accueil</span> |
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| <span>Travaux Pratiques</span> |
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| <span>Questions</span> |
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| <div class="progress-fill" id="progress-fill" style="width: 0%"></div> |
| </div> |
| </div> |
| </aside> |
|
|
| |
| <main class="main-content"> |
| |
| <div class="course-hero scroll-animate"> |
| <div class="course-hero-badge"> |
| <div class="dot"></div> |
| <span>Pret a executer sur Google Colab</span> |
| </div> |
| <h1 class="course-hero-title"> |
| Cours de <span class="gradient-text">Machine Learning</span> |
| </h1> |
| <p class="course-hero-subtitle"> |
| Formation complete couvrant les algorithmes fondamentaux jusqu'aux techniques avancees. |
| </p> |
| <div class="course-hero-stats"> |
| <div class="course-hero-stat"> |
| <div class="course-hero-stat-value">8</div> |
| <div class="course-hero-stat-label">Chapitres</div> |
| </div> |
| <div class="course-hero-stat"> |
| <div class="course-hero-stat-value">25+</div> |
| <div class="course-hero-stat-label">Equations</div> |
| </div> |
| <div class="course-hero-stat"> |
| <div class="course-hero-stat-value">50+</div> |
| <div class="course-hero-stat-label">Exemples de code</div> |
| </div> |
| </div> |
| </div> |
|
|
| |
| <section class="section" id="intro"> |
| <div class="section-header scroll-animate"> |
| <div class="section-badge">Partie 1 · 20 min</div> |
| <h2 class="section-title"> |
| <small>Fondamentaux</small> |
| Qu'est-ce que le Machine Learning ? |
| </h2> |
| </div> |
| |
| <div class="section-content scroll-animate"> |
| <p> |
| Le <strong>Machine Learning (ML)</strong> est une branche de l'intelligence artificielle |
| qui permet aux machines d'apprendre a partir de donnees <em>sans etre explicitement programmees</em> |
| pour chaque tache. Au lieu de coder des regles a la main, on montre des exemples au modele |
| et il decouvre lui-meme les patterns. |
| </p> |
|
|
| <div class="callout callout-info scroll-animate"> |
| <div class="callout-icon">[i]</div> |
| <div class="callout-content"> |
| <div class="callout-title">Idee fondamentale</div> |
| <div class="callout-text"> |
| On cherche a approximer une fonction inconnue $f$ telle que $\hat{y} = f(x_1, x_2, \ldots, x_n)$. |
| Le modele ML apprend cette fonction a partir d'exemples $(x, y)$ connus. |
| </div> |
| </div> |
| </div> |
|
|
| <h3 style="font-size: 1.2rem; font-weight: 600; color: var(--text-primary); margin: var(--space-xl) 0 var(--space-md);"> |
| Types d'apprentissage |
| </h3> |
|
|
| <div class="cards-grid scroll-animate"> |
| <div class="info-card"> |
| <div class="info-card-icon">S</div> |
| <h4 class="info-card-title">Supervise</h4> |
| <p class="info-card-text">Donnees labellisees $(x, y)$ : regression et classification.</p> |
| <span class="info-card-tag">Predictions</span> |
| </div> |
| <div class="info-card"> |
| <div class="info-card-icon">N</div> |
| <h4 class="info-card-title">Non supervise</h4> |
| <p class="info-card-text">Pas de labels : clustering, reduction de dimension.</p> |
| <span class="info-card-tag">Patterns</span> |
| </div> |
| <div class="info-card"> |
| <div class="info-card-icon">R</div> |
| <h4 class="info-card-title">Par renforcement</h4> |
| <p class="info-card-text">Agent apprend via actions-recompenses.</p> |
| <span class="info-card-tag">Strategies</span> |
| </div> |
| </div> |
|
|
| <h3 style="font-size: 1.2rem; font-weight: 600; color: var(--text-primary); margin: var(--space-xl) 0 var(--space-md);"> |
| Pipeline ML typique |
| </h3> |
|
|
| <div class="pipeline scroll-animate"> |
| <div class="pipeline-step"> |
| <div class="pipeline-num">1</div> |
| <div class="pipeline-label">Donnees</div> |
| <div class="pipeline-desc">Collecte & nettoyage</div> |
| </div> |
| <div class="pipeline-step"> |
| <div class="pipeline-num">2</div> |
| <div class="pipeline-label">Features</div> |
| <div class="pipeline-desc">Engineering</div> |
| </div> |
| <div class="pipeline-step"> |
| <div class="pipeline-num">3</div> |
| <div class="pipeline-label">Split</div> |
| <div class="pipeline-desc">Train / Test</div> |
| </div> |
| <div class="pipeline-step"> |
| <div class="pipeline-num">4</div> |
| <div class="pipeline-label">Modele</div> |
| <div class="pipeline-desc">Entrainement</div> |
| </div> |
| <div class="pipeline-step"> |
| <div class="pipeline-num">5</div> |
| <div class="pipeline-label">Evaluation</div> |
| <div class="pipeline-desc">Metriques</div> |
| </div> |
| <div class="pipeline-step"> |
| <div class="pipeline-num">6</div> |
| <div class="pipeline-label">Production</div> |
| <div class="pipeline-desc">Deploiement</div> |
| </div> |
| </div> |
| </div> |
| </section> |
|
|
| |
| <section class="section" id="regression"> |
| <div class="section-header scroll-animate"> |
| <div class="section-badge">Partie 2 · 25 min</div> |
| <h2 class="section-title"> |
| <small>Algorithmes de base</small> |
| Regression Lineaire |
| </h2> |
| </div> |
| |
| <div class="section-content scroll-animate"> |
| <p> |
| La <strong>regression lineaire</strong> modelise la relation entre les features et la cible |
| par une fonction affine. C'est l'algorithme le plus simple mais souvent tres efficace |
| comme baseline. |
| </p> |
|
|
| <div class="equation-block"> |
| $$\hat{y} = w_0 + w_1 x_1 + w_2 x_2 + \cdots + w_n x_n = \mathbf{w}^T \mathbf{x}$$ |
| <span class="equation-label">Modele lineaire avec coefficients $\mathbf{w}$</span> |
| </div> |
|
|
| <p> |
| L'objectif est de minimiser l'erreur quadratique moyenne (MSE) : |
| </p> |
|
|
| <div class="equation-block"> |
| $$\text{MSE} = \frac{1}{m} \sum_{i=1}^{m} (y_i - \hat{y}_i)^2$$ |
| <span class="equation-label">Fonction de cout : moyenne des erreurs quadratiques</span> |
| </div> |
|
|
| <div class="callout callout-info scroll-animate"> |
| <div class="callout-icon">[i]</div> |
| <div class="callout-content"> |
| <div class="callout-title">Solution analytique</div> |
| <div class="callout-text"> |
| La regression lineaire admet une solution fermee : |
| $\mathbf{w}^* = (X^T X)^{-1} X^T Y$. Pas besoin d'iterations ! |
| </div> |
| </div> |
| </div> |
|
|
| <div class="comparison-grid scroll-animate"> |
| <div class="comparison-box"> |
| <h4>[+] Avantages</h4> |
| <ul> |
| <li>Tres rapide a entrainer</li> |
| <li>Interpretable (coefficients)</li> |
| <li>Pas d'hyperparametres</li> |
| <li>Excellent baseline</li> |
| </ul> |
| </div> |
| <div class="comparison-box"> |
| <h4>[-] Limitations</h4> |
| <ul> |
| <li>Relation lineaire uniquement</li> |
| <li>Sensible aux outliers</li> |
| <li>Performance decroit en haute dimension</li> |
| </ul> |
| </div> |
| </div> |
| </div> |
| </section> |
|
|
| |
| <section class="section" id="logistic"> |
| <div class="section-header scroll-animate"> |
| <div class="section-badge">Partie 3 · 20 min</div> |
| <h2 class="section-title"> |
| <small>Classification</small> |
| Regression Logistique |
| </h2> |
| </div> |
| |
| <div class="section-content scroll-animate"> |
| <p> |
| Malgre son nom, la <strong>regression logistique</strong> est un algorithme de <em>classification</em>. |
| Elle predit la probabilite d'appartenance a une classe en utilisant la fonction sigmoide. |
| </p> |
|
|
| <div class="equation-block"> |
| $$P(y=1|\mathbf{x}) = \sigma(\mathbf{w}^T \mathbf{x}) = \frac{1}{1 + e^{-\mathbf{w}^T \mathbf{x}}}$$ |
| <span class="equation-label">Fonction sigmoide pour la classification binaire</span> |
| </div> |
|
|
| <div class="callout callout-success scroll-animate"> |
| <div class="callout-icon">[v]</div> |
| <div class="callout-content"> |
| <div class="callout-title">Cas d'usage : Dataset Titanic</div> |
| <div class="callout-text"> |
| Predire la survie des passagers du Titanic a partir de leur age, sexe, |
| classe de billet, etc. Un classique du ML pour debuter ! |
| </div> |
| </div> |
| </div> |
| </div> |
| </section> |
|
|
| |
| <section class="section" id="randomforest"> |
| <div class="section-header scroll-animate"> |
| <div class="section-badge">Partie 4 · 30 min</div> |
| <h2 class="section-title"> |
| <small>Ensemble Learning</small> |
| Random Forest |
| </h2> |
| </div> |
| |
| <div class="section-content scroll-animate"> |
| <p> |
| <strong>Random Forest</strong> est un ensemble d'arbres de decision qui votent pour predire. |
| C'est l'un des algorithmes les plus populaires en ML applique : performant, robuste, |
| peu sensible au tuning. |
| </p> |
|
|
| <h3 style="font-size: 1.2rem; font-weight: 600; color: var(--text-primary); margin: var(--space-xl) 0 var(--space-md);"> |
| Algorithm : Bagging + Random Splits |
| </h3> |
|
|
| <div class="pipeline scroll-animate"> |
| <div class="pipeline-step"> |
| <div class="pipeline-num">1</div> |
| <div class="pipeline-label">Bootstrap</div> |
| <div class="pipeline-desc">Echantillons aleatoires</div> |
| </div> |
| <div class="pipeline-step"> |
| <div class="pipeline-num">2</div> |
| <div class="pipeline-label">Splits</div> |
| <div class="pipeline-desc">Features aleatoires</div> |
| </div> |
| <div class="pipeline-step"> |
| <div class="pipeline-num">3</div> |
| <div class="pipeline-label">Arbres</div> |
| <div class="pipeline-desc">N arbres independants</div> |
| </div> |
| <div class="pipeline-step"> |
| <div class="pipeline-num">4</div> |
| <div class="pipeline-label">Vote</div> |
| <div class="pipeline-desc">Moyenne ou mode</div> |
| </div> |
| </div> |
|
|
| <div class="metric-row scroll-animate"> |
| <div class="metric-card"> |
| <div class="metric-name">n_estimators</div> |
| <div class="metric-formula">100 - 500</div> |
| <div class="metric-desc">Nombre d'arbres</div> |
| </div> |
| <div class="metric-card"> |
| <div class="metric-name">max_depth</div> |
| <div class="metric-formula">10 - 30</div> |
| <div class="metric-desc">Profondeur max</div> |
| </div> |
| <div class="metric-card"> |
| <div class="metric-name">min_samples_split</div> |
| <div class="metric-formula">2 - 10</div> |
| <div class="metric-desc">Min pour splitter</div> |
| </div> |
| </div> |
|
|
| <div class="callout callout-success scroll-animate"> |
| <div class="callout-icon">[*]</div> |
| <div class="callout-content"> |
| <div class="callout-title">Feature Importance</div> |
| <div class="callout-text"> |
| Random Forest fournit automatiquement l'importance de chaque feature, |
| ce qui aide a comprendre quelles variables influencent le plus les predictions. |
| </div> |
| </div> |
| </div> |
| </div> |
| </section> |
|
|
| |
| <section class="section" id="neuralnets"> |
| <div class="section-header scroll-animate"> |
| <div class="section-badge">Partie 5 · 35 min</div> |
| <h2 class="section-title"> |
| <small>Deep Learning</small> |
| Reseaux de Neurones |
| </h2> |
| </div> |
| |
| <div class="section-content scroll-animate"> |
| <p> |
| Les <strong>reseaux de neurones</strong> sont inspires du cerveau humain : des couches de neurones |
| interconnectes executent des transformations non-lineaires. Ils excellent pour les patterns complexes. |
| </p> |
|
|
| <h3 style="font-size: 1.2rem; font-weight: 600; color: var(--text-primary); margin: var(--space-xl) 0 var(--space-md);"> |
| Fonctionnement : Forward + Backprop |
| </h3> |
|
|
| <div class="cards-grid scroll-animate"> |
| <div class="info-card"> |
| <div class="info-card-icon">F</div> |
| <h4 class="info-card-title">Forward Pass</h4> |
| <p class="info-card-text">Donnees traversent les couches : $\mathbf{h}_1 = \sigma(W_1 \mathbf{x} + b_1)$</p> |
| </div> |
| <div class="info-card"> |
| <div class="info-card-icon">L</div> |
| <h4 class="info-card-title">Loss Computation</h4> |
| <p class="info-card-text">Compare prediction vs realite : $L = \frac{1}{m} \sum (y - \hat{y})^2$</p> |
| </div> |
| <div class="info-card"> |
| <div class="info-card-icon">B</div> |
| <h4 class="info-card-title">Backpropagation</h4> |
| <p class="info-card-text">Calcule les gradients via la chaine de derivation</p> |
| </div> |
| <div class="info-card"> |
| <div class="info-card-icon">G</div> |
| <h4 class="info-card-title">Gradient Descent</h4> |
| <p class="info-card-text">Met a jour les poids : $W \leftarrow W - \alpha \nabla_W L$</p> |
| </div> |
| </div> |
|
|
| <h3 style="font-size: 1.2rem; font-weight: 600; color: var(--text-primary); margin: var(--space-xl) 0 var(--space-md);"> |
| Fonctions d'activation |
| </h3> |
|
|
| <div class="metric-row scroll-animate"> |
| <div class="metric-card"> |
| <div class="metric-name">ReLU</div> |
| <div class="metric-formula">$f(x) = \max(0, x)$</div> |
| <div class="metric-desc">Couches cachees</div> |
| </div> |
| <div class="metric-card"> |
| <div class="metric-name">Sigmoid</div> |
| <div class="metric-formula">$f(x) = \frac{1}{1 + e^{-x}}$</div> |
| <div class="metric-desc">Classification binaire</div> |
| </div> |
| <div class="metric-card"> |
| <div class="metric-name">Softmax</div> |
| <div class="metric-formula">$f(x_i) = \frac{e^{x_i}}{\sum_j e^{x_j}}$</div> |
| <div class="metric-desc">Classification multi-classe</div> |
| </div> |
| </div> |
| </div> |
| </section> |
|
|
| |
| <section class="section" id="lstm"> |
| <div class="section-header scroll-animate"> |
| <div class="section-badge">Partie 6 · 30 min</div> |
| <h2 class="section-title"> |
| <small>Series Temporelles</small> |
| LSTM & Reseaux Recurrents |
| </h2> |
| </div> |
| |
| <div class="section-content scroll-animate"> |
| <p> |
| <strong>LSTM (Long Short-Term Memory)</strong> est un type de reseau neuronal pour series temporelles. |
| Il peut "retenir" l'information sur de longues periodes — crucial pour les predictions temporelles. |
| </p> |
|
|
| <div class="callout callout-warning scroll-animate"> |
| <div class="callout-icon">[!]</div> |
| <div class="callout-content"> |
| <div class="callout-title">Probleme des RNN vanilla</div> |
| <div class="callout-text"> |
| Les gradients disparaissent (vanishing) ou explosent (exploding) sur de longues sequences. |
| Le LSTM resout ce probleme avec son <strong>cell state</strong>. |
| </div> |
| </div> |
| </div> |
|
|
| <h3 style="font-size: 1.2rem; font-weight: 600; color: var(--text-primary); margin: var(--space-xl) 0 var(--space-md);"> |
| Les trois portes du LSTM |
| </h3> |
|
|
| <div class="metric-row scroll-animate"> |
| <div class="metric-card"> |
| <div class="metric-name">Forget Gate</div> |
| <div class="metric-formula">$f_t = \sigma(W_f [h_{t-1}, x_t] + b_f)$</div> |
| <div class="metric-desc">Quoi oublier ?</div> |
| </div> |
| <div class="metric-card"> |
| <div class="metric-name">Input Gate</div> |
| <div class="metric-formula">$i_t = \sigma(W_i [h_{t-1}, x_t] + b_i)$</div> |
| <div class="metric-desc">Quoi ajouter ?</div> |
| </div> |
| <div class="metric-card"> |
| <div class="metric-name">Output Gate</div> |
| <div class="metric-formula">$o_t = \sigma(W_o [h_{t-1}, x_t] + b_o)$</div> |
| <div class="metric-desc">Quoi exposer ?</div> |
| </div> |
| </div> |
|
|
| <div class="callout callout-info scroll-animate"> |
| <div class="callout-icon">[i]</div> |
| <div class="callout-content"> |
| <div class="callout-title">Cas d'usage</div> |
| <div class="callout-text"> |
| Prediction de prix boursiers, meteo, consommation energetique, |
| traitement du langage naturel (NLP)... |
| </div> |
| </div> |
| </div> |
| </div> |
| </section> |
|
|
| |
| <section class="section" id="metrics"> |
| <div class="section-header scroll-animate"> |
| <div class="section-badge">Partie 7 · 25 min</div> |
| <h2 class="section-title"> |
| <small>Evaluation</small> |
| Metriques de Performance |
| </h2> |
| </div> |
| |
| <div class="section-content scroll-animate"> |
| <p> |
| Evaluer correctement un modele est crucial. Les bonnes metriques dependent du type de probleme |
| (regression vs classification) et des objectifs metier. |
| </p> |
|
|
| <h3 style="font-size: 1.2rem; font-weight: 600; color: var(--text-primary); margin: var(--space-xl) 0 var(--space-md);"> |
| Regression |
| </h3> |
|
|
| <div class="metric-row scroll-animate"> |
| <div class="metric-card"> |
| <div class="metric-name">MAE</div> |
| <div class="metric-formula">$\frac{1}{n}\sum|y_i - \hat{y}_i|$</div> |
| <div class="metric-desc">Robuste aux outliers</div> |
| </div> |
| <div class="metric-card"> |
| <div class="metric-name">RMSE</div> |
| <div class="metric-formula">$\sqrt{\frac{1}{n}\sum(y_i - \hat{y}_i)^2}$</div> |
| <div class="metric-desc">Penalise les grandes erreurs</div> |
| </div> |
| <div class="metric-card"> |
| <div class="metric-name">R2</div> |
| <div class="metric-formula">$1 - \frac{SS_{res}}{SS_{tot}}$</div> |
| <div class="metric-desc">% variance expliquee</div> |
| </div> |
| </div> |
|
|
| <h3 style="font-size: 1.2rem; font-weight: 600; color: var(--text-primary); margin: var(--space-xl) 0 var(--space-md);"> |
| Classification |
| </h3> |
|
|
| <div class="table-container scroll-animate"> |
| <table> |
| <thead> |
| <tr> |
| <th>Metrique</th> |
| <th>Formule</th> |
| <th>Usage</th> |
| </tr> |
| </thead> |
| <tbody> |
| <tr> |
| <td><strong>Accuracy</strong></td> |
| <td>$(TP + TN) / Total$</td> |
| <td>Classes equilibrees</td> |
| </tr> |
| <tr> |
| <td><strong>Precision</strong></td> |
| <td>$TP / (TP + FP)$</td> |
| <td>Minimiser faux positifs</td> |
| </tr> |
| <tr> |
| <td><strong>Recall</strong></td> |
| <td>$TP / (TP + FN)$</td> |
| <td>Minimiser faux negatifs</td> |
| </tr> |
| <tr> |
| <td><strong>F1-Score</strong></td> |
| <td>$2 \cdot \frac{P \cdot R}{P + R}$</td> |
| <td>Classes desequilibrees</td> |
| </tr> |
| </tbody> |
| </table> |
| </div> |
| </div> |
| </section> |
|
|
| |
| <section class="section" id="optimization"> |
| <div class="section-header scroll-animate"> |
| <div class="section-badge">Partie 8 · 20 min</div> |
| <h2 class="section-title"> |
| <small>Amelioration</small> |
| Optimisation & Regularisation |
| </h2> |
| </div> |
| |
| <div class="section-content scroll-animate"> |
| <p> |
| Pour eviter le <strong>surapprentissage (overfitting)</strong> et ameliorer la generalisation, |
| plusieurs techniques existent. |
| </p> |
|
|
| <div class="cards-grid scroll-animate"> |
| <div class="info-card"> |
| <div class="info-card-icon">D</div> |
| <h4 class="info-card-title">Dropout</h4> |
| <p class="info-card-text">Desactive aleatoirement des neurones pendant l'entrainement.</p> |
| </div> |
| <div class="info-card"> |
| <div class="info-card-icon">E</div> |
| <h4 class="info-card-title">Early Stopping</h4> |
| <p class="info-card-text">Arrete l'entrainement quand la validation stagne.</p> |
| </div> |
| <div class="info-card"> |
| <div class="info-card-icon">L</div> |
| <h4 class="info-card-title">L2 Regularization</h4> |
| <p class="info-card-text">Penalise les grands poids : $L_{total} = L_{data} + \lambda \sum w^2$</p> |
| </div> |
| <div class="info-card"> |
| <div class="info-card-icon">C</div> |
| <h4 class="info-card-title">Cross-Validation</h4> |
| <p class="info-card-text">K-fold pour une evaluation plus robuste.</p> |
| </div> |
| </div> |
|
|
| <div class="callout callout-tip scroll-animate"> |
| <div class="callout-icon">[*]</div> |
| <div class="callout-content"> |
| <div class="callout-title">Regle d'or</div> |
| <div class="callout-text"> |
| Toujours comparer les metriques sur <strong>train</strong> ET <strong>test</strong>. |
| Un grand ecart = overfitting. Objectif : R2 train ≈ R2 test. |
| </div> |
| </div> |
| </div> |
| </div> |
| </section> |
|
|
| |
| <footer class="author-footer" style="margin-top: var(--space-3xl);"> |
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