Intro to TensorFlow en Français from Coursera

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.

Concevoir et créer un pipeline de données d'entrée

Les données sont essentielles aux modèles de machine learning, mais collecter les bonnes ne suffit pas. Vous devez également vous assurer de mettre en place les processus adéquats pour nettoyer, analyser et transformer ces données si nécessaire, pour que les modèles puissent les exploiter pleinement. Dans ce module, nous verrons comment entraîner un modèle avec des ensembles de données volumineux grâce à tf.data, travailler avec des fichiers en mémoire et préparer les données pour l'entraînement. Pour terminer, nous évoquerons les représentations vectorielles continues et le scaling des données effectué à l'aide de couches de prétraitement tf.keras.

Créer des réseaux de neurones avec l'API Keras et TensorFlow

Dans ce module, nous aborderons les fonctions d'activation et expliquerons en quoi elles sont nécessaires pour permettre aux réseaux de neurones profonds d'identifier les cas de non-linéarité dans les données. Ensuite, nous présenterons les réseaux de neurones profonds avec les API Keras Sequential et Keras Functional avant d'évoquer le sous-classement, qui offre une plus grande flexibilité pour la création de modèles. Enfin, nous parlerons de la régularisation.

Entraîner des modèles à grande échelle avec Vertex AI

Dans ce module, nous verrons comment entraîner des modèles TensorFlow à grande échelle avec Vertex AI.

Résumé

Ce module résume le cours "TensorFlow on Google Cloud".

Good to know

Know what's good

, what to watch for

, 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

Reviews summary

Mediocre tensorflow

This course receives mixed reviews with most reviewers mentioning that they experienced issues with labs and quizzes. Some reviewers suggest that this may not be an appropriate course for beginners.

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

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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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