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Intro to TensorFlow en Français

Ce cours porte sur la conception et la création d'un pipeline de données d'entrée TensorFlow 2.x, la création de modèles de ML à l'aide de TensorFlow 2.x et Keras, l'amélioration de la précision des modèles de ML, l'écriture de modèles de ML pour une utilisation évolutive et l'écriture de modèles de ML spécialisés.

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What's inside

Syllabus

Présentation du cours

Ce module présente le cours et ses objectifs.

Présentation de l'écosystème TensorFlow

Ce module présente le framework TensorFlow, ses composants principaux ainsi que la hiérarchie globale de l'API.

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

S'adresse aux développeurs qui conçoivent et construisent des pipelines de données d'apprentissage automatique et modélisent des problèmes commerciaux réels

Contient des exercices pratiques qui aident les apprenants à mettre en œuvre les concepts enseignés

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Reviews summary

Introduction à tensorflow en français

Selon les étudiants, ce cours est une excellente initiation à TensorFlow 2.x, particulièrement appréciée pour son approche pratique des pipelines de données et l'utilisation de Keras. Les labs pratiques sont souvent cités comme un point fort, aidant à solidifier la compréhension. Cependant, le cours peut présenter des défis sans connaissances préalables en ML. Certains apprenants ont rencontré des difficultés techniques avec les environnements de lab, bien que ces problèmes semblent être moins fréquents dans les commentaires plus récents. La pertinence de la section sur Vertex AI est un plus notable pour l'application professionnelle.

Couverture essentielle de tf.data.

"La section sur tf.data et les pipelines d'entrée est particulièrement bien faite et très importante pour le traitement des données."

"J'ai appris à gérer et préparer les données de manière efficace avec tf.data grâce à ce module."

"Cette partie du cours est fondamentale pour travailler avec de grands ensembles de données en TensorFlow."

Les labs renforcent la compréhension.

"Les labs et exercices pratiques sont très utiles pour bien comprendre et appliquer les concepts."

"J'ai vraiment apprécié les exercices de codage qui accompagnent les vidéos."

"L'approche pratique avec les Colabs m'a beaucoup aidé à intégrer les concepts montrés dans les cours."

Difficultés occasionnelles avec les labs.

"J'ai parfois eu des problèmes techniques ou de configuration pour faire fonctionner les notebooks Colab fournis."

"L'environnement de lab n'est pas toujours fluide, il faut parfois dépanner un peu."

"Il a fallu un peu de temps et d'efforts pour résoudre certains bugs ou erreurs rencontrés dans les exercices."

Peut demander des prérequis.

"Le cours peut être un peu rapide si l'on n'a aucune base solide en machine learning et Python."

"Il est préférable d'avoir déjà touché à la programmation et aux bases du ML pour suivre facilement."

"Certains concepts sont expliqués rapidement, nécessitant parfois des recherches supplémentaires de mon côté."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Intro to TensorFlow en Français with these activities:

Review the Basics of Machine Learning

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Strengthen your foundation in machine learning concepts.

Browse courses on Machine Learning

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Go over your notes from previous machine learning courses or books
Complete online practice problems or quizzes on machine learning fundamentals

Complete the TensorFlow 2.0 Tutorial

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Build a solid foundation in TensorFlow by following the official tutorial.

Browse courses on TensorFlow

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Complete the Getting Started section
Complete the Building and Training a Model section
Complete the Evaluating a Model section

Read and Review Deep Learning with Python

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Deepen your understanding of TensorFlow's core components and concepts.

View L'apprentissage profond avec Python on Amazon

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Read Chapter 1: Introduction to TensorFlow
Work through the exercises in Chapter 1
Summarize the key concepts in Chapter 1

Five other activities

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Create a TensorFlow Resource Collection

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Organize and expand your knowledge by compiling valuable resources.

Browse courses on TensorFlow

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Gather relevant articles, tutorials, and documentation on TensorFlow
Create a central repository (e.g., a Notion page or Google Doc) to store and organize the resources
Categorize and tag the resources for easy retrieval

Connect with TensorFlow Mentors

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Seek guidance and support from experienced professionals.

Browse courses on TensorFlow

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Identify potential mentors on platforms like LinkedIn or Meetup
Reach out to mentors and express your interest
Schedule regular meetings to discuss your progress and seek advice

Attend a TensorFlow Workshop

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Engage in hands-on learning and connect with experts in the field.

Browse courses on TensorFlow

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Find a TensorFlow workshop near you
Register for the workshop
Attend the workshop and actively participate

Practice Building Neural Networks with TensorFlow

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Develop your skills in designing and constructing neural networks using TensorFlow.

Browse courses on TensorFlow

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Build a simple feedforward neural network
Build a convolutional neural network (CNN)
Build a recurrent neural network (RNN)

Build a Machine Learning Model Using TensorFlow

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Apply your TensorFlow skills to solve a real-world problem.

Browse courses on TensorFlow

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Identify a problem that can be solved with machine learning
Collect and prepare the necessary data
Build a machine learning model with TensorFlow
Evaluate the performance of the model
Deploy the model to production

Career center

Learners who complete Intro to TensorFlow en Français will develop knowledge and skills that may be useful to these careers:

Machine Learning Engineer

Machine Learning Engineers play a critical role in the development and deployment of machine learning models within various industries. This course in TensorFlow on Google Cloud provides a foundation in designing, building, and training ML models, preparing individuals to excel in this field. The syllabus includes essential topics such as creating ML pipelines, leveraging Keras and TensorFlow for model development, and optimizing model accuracy. With the knowledge gained from this course, learners will be well-equipped to pursue a career as a Machine Learning Engineer.

See salaries and explore the career path for Machine Learning Engineer

Data Scientist

Data Scientists are responsible for analyzing large datasets to uncover patterns and insights. This course in TensorFlow on Google Cloud offers a valuable foundation for aspiring Data Scientists, covering key aspects of data management, model building, and evaluation. The syllabus delves into the design and creation of data pipelines, the use of TensorFlow and Keras for ML model development, and techniques to improve model performance. By completing this course, learners will gain essential skills for a successful career as a Data Scientist.

See salaries and explore the career path for Data Scientist

Software Engineer

Software Engineers play a pivotal role in designing, developing, and maintaining software systems. This course in TensorFlow on Google Cloud provides a solid foundation for Software Engineers seeking to specialize in ML-driven software development. The curriculum covers topics such as data preprocessing, model creation with TensorFlow and Keras, and the deployment of ML models for real-world applications. With the knowledge gained from this course, Software Engineers will be well-equipped to build and enhance software systems that leverage ML capabilities.

See salaries and explore the career path for Software Engineer

AI Engineer

AI Engineers drive the design, development, and implementation of AI solutions within various industries. This course in TensorFlow on Google Cloud provides a comprehensive foundation for aspiring AI Engineers. The syllabus covers essential topics such as building and training ML models, optimizing model performance, and deploying ML models for real-world applications. By completing this course, learners will gain the necessary skills to succeed as AI Engineers and contribute to the development of innovative AI-powered solutions.

See salaries and explore the career path for AI Engineer

Deep Learning Engineer

Deep Learning Engineers specialize in developing and maintaining deep learning models for various applications. This course in TensorFlow on Google Cloud provides a solid foundation for individuals seeking to pursue a career in this field. The syllabus covers topics such as designing and building deep learning models, optimizing model performance, and deploying deep learning models for real-world applications. With the knowledge gained from this course, learners will be well-equipped to excel as Deep Learning Engineers and drive innovation in various industries.

See salaries and explore the career path for Deep Learning Engineer

Research Scientist

Research Scientists conduct research in various scientific fields, including machine learning and artificial intelligence. This course in TensorFlow on Google Cloud provides a valuable foundation for aspiring Research Scientists seeking to specialize in ML research. The syllabus covers essential topics such as designing and evaluating ML models, optimizing model performance, and deploying ML models for research purposes. By completing this course, learners will gain the necessary skills to conduct cutting-edge research in the field of ML and contribute to the advancement of ML knowledge.

See salaries and explore the career path for Research Scientist

Data Analyst

Data Analysts play a crucial role in analyzing data to uncover patterns and trends. This course in TensorFlow on Google Cloud provides a solid foundation for aspiring Data Analysts seeking to leverage ML techniques in their work. The syllabus covers topics such as data preprocessing, feature engineering, and the use of ML models for data analysis. By completing this course, learners will gain the necessary skills to effectively analyze data, identify insights, and make data-driven decisions.

See salaries and explore the career path for Data Analyst

Business Analyst

Business Analysts help businesses understand their data and make informed decisions. This course in TensorFlow on Google Cloud may be useful for Business Analysts seeking to incorporate ML techniques into their work. The syllabus covers topics such as data preprocessing, feature engineering, and the use of ML models for data analysis. By completing this course, Business Analysts can gain a better understanding of ML and its potential applications, enabling them to make more informed recommendations and drive business growth.

See salaries and explore the career path for Business Analyst

Product Manager

Product Managers are responsible for defining and managing the development of products. This course in TensorFlow on Google Cloud may be useful for Product Managers seeking to leverage ML in their products. The syllabus covers topics such as data preprocessing, feature engineering, and the use of ML models for data analysis. By completing this course, Product Managers can gain a better understanding of ML and its potential applications, enabling them to make more informed decisions about product development and roadmap.

See salaries and explore the career path for Product Manager

Quantitative Analyst

Quantitative Analysts use mathematical and statistical techniques to analyze financial data. This course in TensorFlow on Google Cloud may be useful for Quantitative Analysts seeking to incorporate ML techniques into their work. The syllabus covers topics such as data preprocessing, feature engineering, and the use of ML models for data analysis. By completing this course, Quantitative Analysts can gain a better understanding of ML and its potential applications, enabling them to develop more sophisticated and accurate financial models.

See salaries and explore the career path for Quantitative Analyst

Consultant

Consultants provide expert advice and solutions to businesses. This course in TensorFlow on Google Cloud may be useful for Consultants seeking to specialize in ML consulting. The syllabus covers topics such as data preprocessing, feature engineering, and the use of ML models for data analysis. By completing this course, Consultants can gain a better understanding of ML and its potential applications, enabling them to provide more valuable advice and services to their clients.

See salaries and explore the career path for Consultant

Technical Writer

Technical Writers create documentation and other materials to explain complex technical concepts. This course in TensorFlow on Google Cloud may be useful for Technical Writers seeking to specialize in ML documentation. The syllabus covers topics such as data preprocessing, feature engineering, and the use of ML models for data analysis. By completing this course, Technical Writers can gain a better understanding of ML and its potential applications, enabling them to create more accurate and informative documentation.

See salaries and explore the career path for Technical Writer

Educator

Educators teach students about various subjects. This course in TensorFlow on Google Cloud may be useful for Educators seeking to incorporate ML into their teaching. The syllabus covers topics such as data preprocessing, feature engineering, and the use of ML models for data analysis. By completing this course, Educators can gain a better understanding of ML and its potential applications, enabling them to develop more engaging and effective lesson plans.

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Marketer

Marketers develop and execute marketing campaigns to promote products and services. This course in TensorFlow on Google Cloud may be useful for Marketers seeking to leverage ML in their marketing campaigns. The syllabus covers topics such as data preprocessing, feature engineering, and the use of ML models for data analysis. By completing this course, Marketers can gain a better understanding of ML and its potential applications, enabling them to develop more targeted and effective marketing campaigns.

See salaries and explore the career path for Marketer

Salesperson

Salespeople sell products and services to customers. This course in TensorFlow on Google Cloud may be useful for Salespeople seeking to leverage ML to improve their sales performance. The syllabus covers topics such as data preprocessing, feature engineering, and the use of ML models for data analysis. By completing this course, Salespeople can gain a better understanding of ML and its potential applications, enabling them to identify and qualify potential customers more effectively.

See salaries and explore the career path for Salesperson

Reading list

We've selected 16 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Intro to TensorFlow en Français.

Probabilistic Machine Learning

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Ce livre fournit une introduction aux principes probabilistes du machine learning. Il aborde les concepts fondamentaux du machine learning, tels que la régression, la classification et la modélisation de densité, du point de vue de la théorie des probabilités.

(English) Probabilistic Machine Learning: Advanced Topics...

Hardcover

$$$$

Interpretable Machine Learning

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Great resource for those who want to learn about interpretable machine learning. It provides a comprehensive overview of the techniques used in interpretable machine learning, and it's written in a clear and concise style.

(English) Interpretable Machine Learning: A Guide For Making...

Intro to TensorFlow en Français

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What's inside

Syllabus

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Reviews summary

Introduction à tensorflow en français

Activities

Career center

Reading list

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