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Articles by Aurélien Géron
Hands-on machine learning with Scikit-learn, Keras & Tensorflow
Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how.
Deep Learning avec Keras et TensorFlow
Mise en oeuvre et cas concrets Existe au format livre et ebook L’objectif de cet ouvrage est de vous expliquer les concepts fondamentaux du Deep Learning et de vous montrer, grâce à de nombreux exemples de code accessibles en ligne, comment les mettre en pratique. La 3e édition de cet ouvrage de...
Machine Learning avec Scikit-Learn
Sommaire de l'ouvrage Vue d’ensemble du Machine Learning. Qu’est-ce que l’apprentissage automatique ? Pourquoi utiliser l’apprentissage automatique ? Exemples d’applications. Types de systèmes d’apprentissage automatique. Principales difficultés de l’apprentissage automatique. Test et validation. Exercices. Un projet de Machine Learning de bout en bout. Travailler avec des données réelles. Prendre du recul pour une vision d’ensemble. Récupérer les données.
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• Hbr's 10 Must Reads on AI (with Bonus Article How to Win with Machine Learning by Ajay Agrawal, Joshua Gans, and AVI Goldfarb) (Paperback) Availability: Not Currently In-Store; May require 2-3 days before shipping • Hands-On Machine Learning with Scikit-Learn, Keras, and Tensorflow: Concepts, Tools, and Techniques to Build Intelligent Systems (Paperback) Availability: Out of Print - Sorry • Hands-On Machine Learning with Scikit-Learn, Keras, and Tensorflow: Concepts, Tools, and Techniques...
Top Data Science and Machine Learning Books to Read in 2023
Stay ahead of the curve: learn and grow with these distinguished data science and machine learning books As a recent Forbes publication highlighted, continuous learning is one of the most effective strategies to propel your career forward. This advice rings especially true in the evolving realms of data science and machine learning. Over the past year, we’ve witnessed amazing advancement in the field of artificial intelligence (AI), most notably with the release of ChatGPT.
Top Data Science and Machine Learning Books to Read in 2023
As a recent Forbes publication highlighted, continuous learning is one of the most effective strategies to propel your career forward. This advice rings especially true in the evolving realms of data science and machine learning. Over the past year, we've witnessed amazing advancement in the field of artificial intelligence (AI), most notably with the release of ChatGPT. These breakthroughs are a constant reminder of the industry's fast pace and the importance of continuous personal development.
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 3rd Edition
Through a recent series of breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data.
5 Must-Have Machine Learning Books in 2022
Kickstart and enhance your machine learning career with these expert-recommended titles Photo by Tom Hermans on Unsplash Why are books worth it? To become an expert in machine learning, you first need to develop a strong foundation in three areas: coding, machine learning theory and maths. There is an overwhelming amount of resources and courses out there dedicated to teaching these skills.
Free Data Science books that every beginner must read
Data Science is an interdisciplinary field between fields such as Computer Science, Mathematics, Statistics, and Machine Learning. It can be difficult to know where to begin if you are new to Data Science. Books are a terrific source of learning for novices, and fortunately, there are numerous that are free to read and cover the fundamentals you'll need to get started. Here’s a list that covers essential Data Science principles, check them: 1.
Best Machine Learning Books - Free and Paid - Editorial Recommendations for 2022
Machine Learning, Editorial For the past year, we have looked at over 8,371 machine learning (ML) books [1], and we have picked what we consider to be the best paid and free books on ML in terms of technicality, ability to explain complex subjects, depth, and verified reviews. Last updated January 1, 2022 Nowadays, we know that machine learning and its applications have become inevitable [5] for most (if not all) businesses. Hence, there is a surge of proficient machine learning engineers.
You Need To Learn This One Skill as A Data Scientist or Machine Learning Practitioner
Getting Started Photo by Cathryn Lavery on Unsplash Data Science as a discipline and profession demands its practitioners possess various skills, ranging from soft skills such as communication, leadership to hard skills such as deductive reasoning, algorithmic thinking, programming, and so on. But there’s a crucial skill that should be attained by Data Scientists, irrespective of their experience, and that is writing.
What’s the Best Way to Prepare for a Career in Data Science/Machine Learning?
So many ways to learn data science/machine learning what’s the best bang for the buck. Of the many routes, you can pursue to learn data science and Machine Learning (grad school, MOOC, boot camps, self-learning, etc…), which may be the right one for you? Getting involved in the realm of data science is a huge leap and there are so many different paths that it can be overwhelming (and discouraging) just to get started.
My Recommendations to Learn Machine Learning in Production
For the last couple of months, I have been doing some research on the topic of machine learning (ML) in production. I have shared a few resources about the topic on Twitter, ranging from courses to books. In terms of the ML in production, I have found some of the best content in books, repositories, and a few courses. Here are my recommendations for learning machine learning in production.
Best Resources for Deep Learning
Deep Learning Educational Resources Source Deep learning is a machine learning method that uses neural networks for prediction tasks. Deep learning methods can be used for a variety of tasks including object detection, synthetic data generation, user recommendation, and much more. In this post, I will walk through some of the best resources for getting started with deep learning.
Data Science Books You Must Read in 2020
Have a look, why you should read them? Photo by Rafael Cosquiere from Pexels “We’re entering a new world in which data may be more important than software.” — - Tim O’Reilly The Data Science industry is seeing a rapid increase in its application and offers a very promising future. To be able to enter this domain, one must be equipped with the various concepts, techniques and have sufficient experience with a wide range of tools available for the job.
Outstanding Python books published in October, 2019
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Aurélien Géron The second edition came out recently and immediately became a bestseller in its category. It is impressive in coverage. Classical supervised learning, unsupervised learning, deep learning (CNN, RNN, GAN), reinforcement learning - it covers everything under the sun systematically. Published on : Oct.
Introducing capsule networks
Capsule networks (CapsNets) are a hot new neural net architecture that may well have a profound impact on deep learning, in particular for computer vision. Wait, isn't computer vision pretty much solved already? Haven't we all seen fabulous examples of convolutional neural networks (CNNs) reaching super-human level in various computer vision tasks, such as classification, localization, object detection, semantic segmentation or instance segmentation (see Figure 1)?
Hands-On Machine Learning with Scikit-Learn and TensorFlow
Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how.
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