Intelligence artificielle et données

The document delves into the use of Correspondence Analysis (AFC) as a method within multidimensional data analysis. It emphasizes reducing dimensionality while preserving data correspondence. The methodology involves calculating inertia using eigenvalues derived from statistical observations, along with visualizing results in reduced axes. An ANOVA test and Fisher statistic are applied to determine the relevance of variables, further refining models aimed at predicting solvency or preferences.

This project demonstrates the implementation of a MapReduce methodology using Apache Spark by processing sales data from a text file. The environment setup involved configuring a virtual machine with Ubuntu 20.04, installing required software like Apache Hadoop, Spark, and Docker, and ensuring system compatibility. A Spark-based code was developed to analyze sales data, leveraging RDD transformations including map and reduce functions. The project successfully showcased foundational knowledge of Spark, Big Data tools, and data processing techniques.

Computer vision is an interdisciplinary field that enables computers to interpret visual data through tasks like classification, object detection, and segmentation. The methodology highlights techniques such as convolutional filters for feature extraction and the use of convolutional neural networks (CNNs) for classification and spatial feature learning. It also discusses advanced architectures like ResNet, DenseNet, and YOLO for enhanced detection and classification. Important applications include image semantics, instance segmentation, and the training of models via backpropagation to opt...

This document explores the limits of machine intelligence, emphasizing various approaches like unsupervised learning and density estimation. It delves into dimensionality reduction techniques such as Principal Component Analysis, auto-encoders, and clustering methods. Generative models like Variational Auto-Encoders and Generative Adversarial Networks are highlighted for their innovative contributions to feature learning and data generation. The author provides insights into advanced methodologies driving progress in engineering and artificial intelligence.

This document provides an overview of multi-layer perceptrons, focusing on concepts such as forward propagation and activation functions. It explains different loss functions, including binary cross-entropy and mean squared error, along with optimization techniques such as stochastic gradient descent and momentum. Emphasis is placed on training challenges like overfitting and solutions such as dropout and early stopping. Detailed discussions on backpropagation and its role in training neural networks are also included.

Logistic regression is a supervised learning algorithm for binary classification problems, where outputs are either 0 or 1. It predicts probabilities using weights, features, and a bias term processed through a sigmoid function that outputs values constrained between 0 and 1. The algorithm evaluates the probability of a specific class given the input features. Key observations include the sigmoid function’s behavior when input is positive, negative, or zero.

The document provides an explanation of logistic regression, focusing on the cost function used for parameter optimization. It outlines the use of the sigmoid function for predictions, explains loss functions for individual training examples, and demonstrates the derivation of the cost function as the average error across training data. Additionally, it emphasizes the goal of minimizing the cost function to optimize parameters 𝑤 and 𝑏.

Deep learning has gained momentum due to the increasing availability of digitized data, innovations in neural network algorithms, and improvements in computational speed. Achieving high performance requires training large neural networks and accessing substantial labeled datasets. Neural network training is an iterative process that can be time-intensive, making faster computation key to enhancing productivity. These advancements collectively contribute to the scalability and adoption of deep learning techniques.

Binary classification deals with problems where the output is a discrete value, typically 0 or 1, such as identifying a cat image versus a non-cat image. Images are represented as three separate RGB matrices, each with pixel intensities defining the feature vector. These values are unrolled into a one-dimensional vector of size 12,288 for machine learning purposes. The goal is to train a classifier that predicts labels accurately based on input feature vectors.

The document discusses supervised learning, focusing on regression and classification problems, outlining their predictive methodologies and categorization of outputs. Specific neural networks like Convolution Neural Networks (CNN) and Recurrent Neural Networks (RNN) are highlighted, emphasizing their application domains such as image processing, sequence data, and autonomous driving. It also differentiates structured and unstructured data types, providing examples of each. Key applications and insights into neural networks' use cases are described to showcase their versatility in handling...

This document provides an introduction to neural networks, detailing how they are inspired by the functioning of the brain. It discusses the key methodology of using neural networks in predictive tasks like house pricing, with a focus on single and multi-layer networks. A specific activation function known as ReLU (Rectified Linear Unit) is explained to address constraints like non-negative output prices. The tutorial illustrates how multiple inputs and hidden layers enhance the predictive capacity of neural networks by automatically generating relationships between features.

This document introduces a proposed standard for mathematical notations in deep learning, aimed at offering clarity and uniformity in representations. It covers notations for neural network components such as weight matrices, biases, activations, layers, and cost functions. Examples of forward propagation equations and cost functions are discussed to solidify the understanding of the structure and function of neural networks. Additionally, it includes graphical representations of neural networks for better visualization, emphasizing key elements like nodes, edges, weights, and biases.