Applied Deep Learning with TensorFlow – A practical Approach 0.0 (0 ratings) Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
What you’ll learn
- A low-level understanding of how Deep Neural Networks work
- How to implement Neural Networks in TensorFlow
- How to use Neural Networks to solve real-world problems
- Advanced Mathematics behind Deep Learning
“Artificial Intelligence, deep learning, machine learning — whatever you’re doing if you don’t understand it — learn it. Because otherwise, you’re going to be a dinosaur within 3 years.” ~Mark Cuban
How will You benefit from this Free Course?
This course has one goal:Teaching you how Artificial Neural Networks work at a low level and how to implement them from scratch using TensorFlow.
How are We going to do that?
We will address all necessary theory behind artificial neural networks. This involves all processes and mathematical operations that are happening inside a neural network. Still, we won’t focus on mathematics to much – rather on conceptual understanding of the processes.
This is a very practical course. All theories will be put directly into practice. For this, we are going to use Python, some external libraries and TensorFlow – the most popular and advanced Deep Learning library.
TensorFlow* is a popular machine learning framework and open-source library for dataflow programming. In this course, you will learn about: The fundamentals of building models with TensorFlow* Machine learning basics like linear regression, loss functions, and gradient descent; Important techniques like normalization, regularization, and mini
This book is for developers and data scientists who want to master the world of artificial intelligence, with a practical approach to understanding and implementing machine learning, and how to apply the power of deep learning