Deep Learning with TensorFlow and Google Cloud AI: 2-in-1

About this course

Deep learning is the intersection of statistics, artificial intelligence, and data to build accurate models. With deep learning going mainstream, making sense of data and getting accurate results using deep networks is possible. Tensorflow is Google’s popular offering for machine learning and deep learning. It has become a popular choice of tool for performing fast, efficient, and accurate deep learning. TensorFlow is one of the most comprehensive libraries for implementing deep learning.

This comprehensive 2-in-1 course is your step-by-step guide to exploring the possibilities in the field of deep learning, making use of Google's TensorFlow. You will learn about convolutional neural networks, and logistic regression while training models for deep learning to gain key insights into your data with the help of insightful examples that you can relate to and show how these can be exploited in the real world with complex raw data. You will also learn how to scale and deploy your deep learning models on the cloud using tools and frameworks such as asTensorFlow, Keras, and Google Cloud MLE. This learning path presents the implementation of practical, real-world projects, teaching you how to leverage TensorFlow’s capabilities to perform efficient deep learning.

This training program includes 2 complete courses, carefully chosen to give you the most comprehensive training possible.

The first course, Hands-on Deep Learning with TensorFlow, is designed to help you overcome various data science problems by using efficient deep learning models built in TensorFlow. You will begin with a quick introduction to TensorFlow essentials. You will then learn deep neural networks for different problems and explore the applications of convolutional neural networks on two real datasets. You will also learn how autoencoders can be used for efficient data representation. Finally, you will understand some of the important techniques to implement generative adversarial networks.

The second course,

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