TensorFlow, one of the most beloved deep learning frameworks, offers an array of essential tools for machine learning researchers and developers. With its wide range of applications, TensorFlow has become a go-to library for many in the field. If you’re looking to learn TensorFlow or enhance your existing skills, we’ve compiled a list of 10 free resources to help you on your journey. Dive into these educational materials and take your TensorFlow knowledge to the next level.
1. Advanced ML with TensorFlow on Google Cloud Platform Specialization
About: The “Advanced Machine Learning (ML) with TensorFlow on Google Cloud Platform Specialization” is an advanced course offered by Google Cloud on Coursera. Designed for those who have already entered the machine learning arena, this course provides hands-on experience in optimizing, deploying, and scaling production ML models. You’ll learn to build scalable, accurate, and production-ready models for structured data, image data, time-series, and natural language text.
Click here to access the course.
2. Deep Learning With TensorFlow
About: This free course, provided by tech giant IBM, offers a comprehensive introduction to TensorFlow. Starting with a simple “Hello World” example, you’ll delve into the basic concepts of TensorFlow, including its main functions, operations, and execution pipeline. Gain a deeper understanding of TensorFlow’s application in curve fitting, regression, classification, minimization of error functions, and more. The course also covers backpropagation and tuning of weights and biases in neural networks.
Click here to access the course.
3. Deep Learning with TensorFlow 2 and Keras – Notebooks
About: “Deep Learning with TensorFlow 2 and Keras – Notebooks” is a hands-on course that focuses on practical exercises. It provides Jupyter notebooks with exercises and their solutions. To make the most of this course, you should have a good understanding of Python and its libraries such as NumPy, Matplotlib, Jupyter, and TensorFlow. Additionally, Python 3.5 or Python 3.6 is required.
Click here to access the course.
4. Introduction to TensorFlow For AI, ML, and Deep Learning
About: Offered by deeplearning.ai on Coursera, this course guides you through best practices for using TensorFlow. You’ll learn to build a basic neural network in TensorFlow, train it for a computer vision application, and utilize convolutions to improve its performance. Gain a solid foundation in TensorFlow and expand your knowledge in AI, ML, and deep learning.
Click here to access the course.
5. Intro to TensorFlow for Deep Learning by TensorFlow
About: This free course on Udacity, “Intro to TensorFlow for Deep Learning” by TensorFlow, is tailored for software developers who want a practical approach to deep learning. Through hands-on experience, you’ll learn how to build state-of-the-art image classifiers and other deep learning models. Take advantage of this course to expand your skills in TensorFlow.
Click here to access the course.
6. Introduction to TensorFlow Lite by TensorFlow Lite
About: “Introduction to TensorFlow Lite” is a practical course on model deployment for software developers, offered by the TensorFlow Lite team on Udacity. Gain hands-on experience with the TensorFlow Lite framework and learn how to deploy deep learning models on Android, iOS, and embedded Linux platforms. This course is ideal for those looking to enhance their skills in model deployment.
Click here to access the course.
7. Learning TensorFlow
About: “Learning TensorFlow” is an e-book by Tom Hope, Yehezkel S. Resheff, and Itay Lieder. This comprehensive resource covers running TensorFlow, building deep learning models, and training them for computer vision and natural language processing (NLP) tasks. Dive into TensorFlow’s scalability and learn to use clusters for distributing model training and more.
Click here to access the e-book.
8. Machine Learning with TensorFlow on Google Cloud Platform Specialization
About: Offered by Google Cloud on Coursera, the “Machine Learning with TensorFlow on Google Cloud Platform Specialization” course covers the basics of machine learning and the problems it can solve. You’ll learn to write distributed machine learning models that scale in TensorFlow. The course also explores scaling out the training of models and offers high-performance predictions.
Click here to access the course.
9. TensorFlow Tutorial By Stanford
About: Stanford University provides a free tutorial on TensorFlow through GitHub. This course teaches the fundamentals and contemporary usage of the TensorFlow library for deep learning research. Explore TensorFlow’s graphical computational model and its various functions. Additionally, learn how to build and structure models suitable for deep learning projects and more.
Click here to access the tutorial.
10. TensorFlow Tutorials
About: The TensorFlow team offers a range of tutorials on their official website. These tutorials are presented as Jupyter notebooks and can be run directly in Google Colab. Whether you’re a beginner or an advanced practitioner, these tutorials cater to your needs, starting from the basics and progressing to advanced customization and distributed training.
Click here to access the tutorials.
Take advantage of these 10 free resources to master TensorFlow in 2020 and unlock the potential of this powerful machine learning library. Remember to explore each resource thoroughly and practice your newfound knowledge to enhance your skills.