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TensorFlow on Google Cloud - Italiano
Google Cloud Training
Google Cloud Training
Questo corso tratta la progettazione e la creazione di una pipeline di dati di input TensorFlow, la realizzazione di modelli ML con TensorFlow e Keras, il miglioramento dell'accuratezza dei modelli ML, la scrittura di modelli ML per l'uso su larga scala e la scrittura di modelli ML specializzati.
Register for this course and see more details by visiting:
OpenCourser.com/course/zpg248/tensorflow
What's inside
Syllabus
Riepilogo
Questo modulo è un riepilogo del corso TensorFlow on Google Cloud.
Introduzione al corso
Questo modulo fornisce una panoramica del corso e dei suoi obiettivi.
Read more
Syllabus
Riepilogo
Questo modulo è un riepilogo del corso TensorFlow on Google Cloud.
Introduzione al corso
Questo modulo fornisce una panoramica del corso e dei suoi obiettivi.
Read more
Introduzione all'ecosistema TensorFlow
Questo modulo introduce il framework di TensorFlow e ne presenta in anteprima i componenti principali, nonché la gerarchia generale delle API.
Progettazione e creazione di una pipeline di dati di input
I dati sono un componente fondamentale di un modello di machine learning. Non basta, però, raccogliere i dati giusti. È necessario anche assicurarsi di mettere in atto i processi giusti per pulire, analizzare e trasformare i dati, se necessario, in modo che il modello possa sfruttarli al meglio. In questo modulo parleremo dell'addestramento su set di dati di grandi dimensioni con tf.data, dell'utilizzo dei file in memoria e di come preparare i dati per l'addestramento. Quindi discuteremo degli incorporamenti e termineremo con una panoramica sulla scalabilità dei dati con gli strati di pre-elaborazione di tf.keras.
Creazione di reti neurali con TensorFlow e l'API Keras
In questo modulo esaminiamo le funzioni di attivazione e come vengono utilizzate per permettere alle reti neurali profonde di acquisire le non linearità dei dati. Quindi forniamo una panoramica delle reti neurali profonde utilizzando le API Keras Sequential e Functional. Successivamente, descriviamo la suddivisione in sottoclassi dei modelli, in modo da avere una maggiore flessibilità nella creazione dei modelli. Il modulo termina con una lezione sulla regolarizzazione.
Addestramento su larga scala con Vertex AI
In questo modulo viene descritto come addestrare i modelli TensorFlow su larga scala utilizzando Vertex AI.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Taught by Google Cloud Training, who are recognized for their work in the topic that the course teaches
Develops skills and tools that are highly relevant to industry
Provides a comprehensive study of science, math, and technology
Explores Deep Learning, which is the core of modern AI
May be overambitious for learners new to the topic
Teaches about libraries that are difficult to find tutorials and support for
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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 TensorFlow on Google Cloud - Italiano with these
activities:
Review fundamental concepts in machine learning
Show steps
Strengthen your foundation by refreshing your knowledge of key machine learning concepts before the course begins.
Browse courses on
Machine Learning
Show steps
-
Read introductory books or articles
-
Complete online courses or tutorials
-
Review previous coursework or notes
-
Practice solving basic machine learning problems
Organize and review course materials
Show steps
Enhance your retention by actively organizing, reviewing, and summarizing the course materials.
Show steps
-
Review lectures, assignments, and quizzes
-
Create summaries and notes
-
Organize materials into folders or databases
Follow TensorFlow tutorials
Show steps
Reinforce your understanding of TensorFlow concepts by working through hands-on tutorials provided by TensorFlow.
Show steps
-
Choose a tutorial that aligns with your learning objectives
-
Follow the step-by-step instructions
-
Experiment with different parameters and settings
Five other activities
Expand to see all activities and additional details
Show all eight activities
Discuss concepts with peers
Show steps
Deepen your understanding by engaging in discussions with other students, exchanging ideas, and answering each other's questions.
Show steps
-
Find a study partner or group
-
Schedule regular meetings
-
Prepare discussion topics
-
Take turns explaining concepts and solving problems
Leggere il white paper di TensorFlow 2.0
Show steps
Aiuterà a comprendere le ultime funzionalità e miglioramenti di TensorFlow.
Browse courses on
TensorFlow 2.0
Show steps
-
Scaricare il white paper di TensorFlow 2.0 dal sito web di TensorFlow.
-
Leggere il documento attentamente e prendere appunti sugli aggiornamenti principali.
Practice building neural networks
Show steps
Apply the concepts covered in the course by building different types of neural networks and experimenting with their architectures.
Show steps
-
Choose a neural network type (e.g., convolutional, recurrent, etc.)
-
Design the network architecture
-
Train the network on a dataset
-
Evaluate the network's performance
Seguire un corso online su TensorBoard
Show steps
Aiuterà a comprendere come utilizzare TensorBoard per visualizzare e monitorare i modelli di TensorFlow.
Browse courses on
TensorBoard
Show steps
-
Iscriversi a un corso online su TensorBoard, come quello offerto su Coursera o edX.
-
Seguire il corso e completare gli esercizi.
Create a machine learning model for a specific problem
Show steps
Apply your knowledge by developing a complete machine learning model to address a real-world problem.
Show steps
-
Define the problem statement and gather data
-
Choose appropriate machine learning algorithms
-
Build and train the model
-
Evaluate and deploy the model
Review fundamental concepts in machine learning
Show steps
Strengthen your foundation by refreshing your knowledge of key machine learning concepts before the course begins.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Read introductory books or articles
- Complete online courses or tutorials
- Review previous coursework or notes
- Practice solving basic machine learning problems
Organize and review course materials
Show steps
Enhance your retention by actively organizing, reviewing, and summarizing the course materials.
Show steps
Show steps
Show steps
- Review lectures, assignments, and quizzes
- Create summaries and notes
- Organize materials into folders or databases
Follow TensorFlow tutorials
Show steps
Reinforce your understanding of TensorFlow concepts by working through hands-on tutorials provided by TensorFlow.
Show steps
Show steps
Show steps
- Choose a tutorial that aligns with your learning objectives
- Follow the step-by-step instructions
- Experiment with different parameters and settings
Five other activities
Expand to see all activities and additional details
Show all eight activities
Discuss concepts with peers
Show steps
Deepen your understanding by engaging in discussions with other students, exchanging ideas, and answering each other's questions.
Show steps
Show steps
Show steps
- Find a study partner or group
- Schedule regular meetings
- Prepare discussion topics
- Take turns explaining concepts and solving problems
Leggere il white paper di TensorFlow 2.0
Show steps
Aiuterà a comprendere le ultime funzionalità e miglioramenti di TensorFlow.
Browse courses on
TensorFlow 2.0
Show steps
Show steps
Show steps
- Scaricare il white paper di TensorFlow 2.0 dal sito web di TensorFlow.
- Leggere il documento attentamente e prendere appunti sugli aggiornamenti principali.
Practice building neural networks
Show steps
Apply the concepts covered in the course by building different types of neural networks and experimenting with their architectures.
Show steps
Show steps
Show steps
- Choose a neural network type (e.g., convolutional, recurrent, etc.)
- Design the network architecture
- Train the network on a dataset
- Evaluate the network's performance
Seguire un corso online su TensorBoard
Show steps
Aiuterà a comprendere come utilizzare TensorBoard per visualizzare e monitorare i modelli di TensorFlow.
Browse courses on
TensorBoard
Show steps
Show steps
Show steps
- Iscriversi a un corso online su TensorBoard, come quello offerto su Coursera o edX.
- Seguire il corso e completare gli esercizi.
Create a machine learning model for a specific problem
Show steps
Apply your knowledge by developing a complete machine learning model to address a real-world problem.
Show steps
Show steps
Show steps
- Define the problem statement and gather data
- Choose appropriate machine learning algorithms
- Build and train the model
- Evaluate and deploy the model
Career center
Learners who complete TensorFlow on Google Cloud - Italiano will develop knowledge and skills
that may be useful to these careers:
For more career information including salaries, visit:
OpenCourser.com/course/zpg248/tensorflow
Reading list
We haven't picked any books for this reading list yet.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/zpg248/tensorflow
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Effort
4 weeks of study, 8-10 hours/week
Level
Intermediate
Via
Coursera
Institution
Google Cloud
Instructor
Google Cloud Training
Session
August 19, 2024
- October 7, 2024
Language
Italiano
Transcript
English
Good to know
Taught by Google Cloud Training, who are recognized for their work in the topic that the course teaches
Develops skills and tools that are highly relevant to industry
Provides a comprehensive study of science, math, and technology
Explores Deep Learning, which is the core of modern AI
May be overambitious for learners new to the topic
Teaches about libraries that are difficult to find tutorials and support for
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