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via Udemy |
Go to Course: https://www.udemy.com/course/deeplearning-classification-dimages-avec-tensorflow/
Certainly! Here's a comprehensive review and recommendation for the course based on the provided details: --- **Course Review: Computer Vision and Deep Learning with Practical Projects** If you're interested in mastering computer vision through practical and hands-on projects, this Coursera course is an excellent choice. Designed to help learners understand image classification and deep learning concepts, the course offers a balanced mix of theoretical knowledge and practical application. **Course Content and Highlights:** 1. **Foundational Concepts**: The course begins with an introduction to deep learning, neural networks, and convolutional neural networks (CNNs). This foundational knowledge is crucial for anyone looking to delve into computer vision. 2. **Coding from Scratch**: One of the standout elements of this course is the project where you'll code a perceptron—from scratch—without relying on frameworks like TensorFlow. This project is ideal for grasping how simple neural networks work under the hood. 3. **Multi-Class Image Classification**: You'll work on classifying fashion items (bags, sandals, sneakers, T-shirts, etc.) into 10 categories using the Fashion-MNIST dataset. This project leverages TensorFlow and Keras, achieving impressive accuracy rates of up to 96%. It provides valuable experience in building real-world image classifiers. 4. **Binary Image Classification**: The course also covers classifying images of cats and dogs using a dataset of 3,000 images. This module introduces techniques such as image augmentation and transfer learning, essential for improving model performance in practical scenarios. **Who Should Enroll?** - Beginners in deep learning and computer vision - Anyone interested in understanding how neural networks classify images - Developers looking to expand their skills in TensorFlow, Keras, and CNNs - Enthusiasts eager to work on projects like face recognition, emotion detection, and other computer vision applications **Why Recommend This Course?** This course is highly recommended for its practical approach, clear project-based learning, and comprehensive coverage of essential concepts in computer vision. By the end, you'll have the skills to classify various image categories and the confidence to explore advanced applications in the field. **Final Verdict:** If you're passionate about deep learning and wish to gain concrete experience through engaging projects, this course is a valuable investment. It provides the necessary tools and knowledge to jumpstart your journey in computer vision and opens doors to numerous exciting applications. --- Would you like a shorter summary or assistance with anything else?
Cette formation vous permettra de comprendre la vision par ordinateur en classifiant des images.Donc nous allons découvrir les concepts théoriques et pratiques sur le deep learning, les réseaux de neurones, les réseaux de neurones convolutifs et nous allons réaliser 3 principaux projets: 1 - coder un perceptron de zéro: qui vous permettra de comprendre comment un modèle de deep learning marche, car nous allons coder un perceptron (réseau de neurone à une seule couche) sans utiliser tensorflow 2 - classification d'images multi classes: nous allons classifier des images d'articles de modes(sac, sandale, sneaker, tee-shirt etc..) en 10 classes différentes avec la base de donnée fashion-mnist et cette fois si avec tensorflow et keras et les réseaux de neurones convolutifs(nous obtiendrons une précision de 96%)3 - classification d'images binaire: nous allons classifier des images de chats et de chien avec une base de données de 3000 photos de chien et de chat. dans ce dernier projet nous allons apprendre plusieurs techniques comme l'augmentation d'image, et l'apprentissage par transfert.Avec les connaissances acquises dans ce cours vous allez pouvoir classifier n'importe qu'elle catégorie d'image et vous allez pouvoir vous lancez dans d'autres applications de la vision par ordinateur tels que la reconnaissance faciale, la détection d'émotion etc...