Intro to TensorFlow for Deep Learning (Udacity)

Intro to TensorFlow for Deep Learning (Udacity)
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Intro to TensorFlow for Deep Learning (Udacity)
This course is a practical approach to deep learning for software developers. Learn how to build deep learning applications with TensorFlow. This course was developed by the TensorFlow team and Udacity as a practical approach to deep learning for software developers.

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You'll get hands-on experience building your own state-of-the-art image classifiers and other deep learning models. You'll also use your TensorFlow models in the real world on mobile devices, in the cloud, and in browsers. Finally, you'll use advanced techniques and algorithms to work with large datasets. By the end of this course, you'll have all the skills necessary to start creating your own AI applications.

Learn how to build deep learning applications with TensorFlow. You'll get hands-on experience building your own state-of-the-art image classifiers and other deep learning models. You'll also use your TensorFlow models in the real world on mobile devices, in the cloud, and in browsers. By the end of this course, you'll have all the skills necessary to start creating your own AI applications.


What you will learn


Introduction to Machine Learning

- Get a high-level overview of artificial intelligence and machine learning

- Learn how machine learning and deep learning have revolutionized software


Your First Model: Fashion MNIST

- Build a neural network that can recognize images of articles of clothing


Introduction to Convolutional Neural Networks ("CNNs")

- Use a convolutional network to build more efficient models for Fashion MNIST


Going Further with CNNs

- Expand your image classifiers into models that can predict from multiple classes

- Use a convolutional network to build a classifier for more detailed color images


Transfer Learning

- Use a pre-trained network to build powerful state-of-the-art classifiers


Saving and Loading Models

- Look at the new SAVEDMODEL format in TensorFlow 2.0 and take advantage of it for TensorFlow-Lite and TensorFlow-Serving


Time Series Forecasting

- Learning from sequential data with recurrent neural networks


Natural Language Processing

- Tokenize words and create embeddings for using text data with neural networks

- Build recurrent neural networks, such as LSTMs, for improved NLP models

- Generate new text for tasks like novel song lyrics


Introduction to TensorFlow Lite

- Learn how you can use TensorFlow lite to build machine learning apps on Android, iOS and iOT devices


Prerequisites and requirements

To get the most out of your experience, we recommend the following:

Beginning Python syntax, including: variables, functions, classes, and object-oriented programming

Basic algebra



MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

Free Course

MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.