TensorFlow
- https://www.tensorflow.org/learn
- https://github.com/tensorflow/tensorflow
- Versions: https://www.tensorflow.org/versions
TensorFlow is an open-source software library developed by Google for machine learning and artificial intelligence. It was first released in 2015.
TensorFlow is primarily designed for deep learning, and usually referred to as a library for deep learning, but it can also be used for traditional machine learning tasks.
It supports a variety of programming languages, including Python, C++, and Java, and can be used for a range of machine learning tasks, including image and speech recognition, natural language processing, and predictive analytics.
[ChatGPT] TensorFlow is an end-to-end platform for developing and deploying machine learning/deep learning models. End-to-end means that TensorFlow provides a comprehensive solution for the entire process of building and using machine learning models, from data preparation to model training and deployment:
- Data Preparation: TensorFlow provides tools and libraries for preprocessing and cleaning data, preparing it for use in machine learning models.
- Model Building: TensorFlow provides a wide range of tools and libraries for building different types of machine learning models, including deep learning models, reinforcement learning models, and more.
- Model Training: TensorFlow provides a powerful distributed training framework that enables users to train models on large datasets using multiple GPUs or TPUs.
- Model Deployment: TensorFlow provides tools and libraries for deploying machine learning models to a wide range of platforms, including mobile devices, web servers, and more.
- Monitoring and Maintenance: TensorFlow provides tools for monitoring and maintaining deployed models, including tools for debugging, profiling, and performance tuning.
Other learning materials apart from https://www.tensorflow.org/learn include:
- Coursera: DeepLearning.AI TensorFlow Developer Professional Certificate: (Recommended on https://www.tensorflow.org/certificate)
- https://www.coursera.org/professional-certificates/tensorflow-in-practice
- https://github.com/golamSaroar/tensorflow-in-practice-specialization
- Udacity: Intro to TensorFlow for Deep Learning: (Recommended on https://www.tensorflow.org/certificate)
- Udemy: TensorFlow Developer Certificate in 2023: Zero to Mastery
TensorFlow certification:
https://www.tensorflow.org/certificate
Before you take the exam, please review our Candidate Handbook: https://www.tensorflow.org/static/extras/cert/TF_Certificate_Candidate_Handbook.pdf
Google provides a certification program for TensorFlow called the "TensorFlow Developer Certificate". This certification program is designed to test and validate your knowledge and skills in developing and deploying TensorFlow models for real-world applications.
The TensorFlow Developer Certificate exam is a computer-based exam that includes a combination of multiple-choice and coding questions.
To take the exam and earn the certification, you must pay a fee. As of my knowledge cut-off in September 2021, the fee for the exam was $100 USD.
Udemy: TensorFlow Developer Certificate in 2023: Zero to Mastery
https://www.udemy.com/course/tensorflow-developer-certificate-machine-learning-zero-to-mastery/
Private online classroom: https://discord.com/channels/423464391791476747/423466983208779776
All Course Resources
Throughout the course there will be various links to different resources, however, one will be your source of truth.
- The course GitHub repo: https://github.com/mrdbourke/tensorflow-deep-learning
- All of the course notebooks are also available as a beautiful online book: https://www.learntensorflow.io
Inside the GitHub/book will find a "Course materials" table that contains all of the materials you'll need for the course, including:
- Lessons in code & text: all videos are based on these notebooks, refer to them for code and text-based explanations of different deep learning and TensorFlow concepts.
- Lessons in code & text (from the videos): notebooks containing the exact code we'll be writing together during the videos.
- Slides: all slides and images used to explain different concepts throughout the course.
- Exercises & extra-curriculum: each section comes with a series of challenges and extra resources to help you practice your skills and learn more.
But possibly the most important out of all of the above will be the GitHub Discussions tab: https://github.com/mrdbourke/tensorflow-deep-learning/discussions
If you have any questions, ideas, feedback or concerns, feel free to start a discussion thread, that way Daniel (the one writing this), Andrei and other students can read and offer help.
Daniel (@mrdbourke on Discord)
Other resources:
- https://zerotomastery.io/
- https://www.linkedin.com/groups/12121940/
- https://www.youtube.com/@ZeroToMastery
Python + Machine Learning Monthly:
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