Implementing Deep Learning Algorithms with TensorFlow 2. 0

Implementing Deep Learning Algorithms with TensorFlow 2. 0

Updated Dec 27, 2019

Deep Learning has caused the revival of Artificial Intelligence. It has become the dominant method for speech recognition (Google Assistant), computer vision (search for ""my pictures"" on Google Photos), language translation, and even game-related Artificial Intelligence (think AlphaGo and DeepMind). If you'd like to learn how these systems work and maybe make your own, Deep Learning is for you! 

In this course, you’ll gain a solid understanding of Deep Learning models and use Deep Learning techniques to solve business and other real-world problems to make predictions quickly and easily. You’ll learn various Deep Learning approaches such as CNN, RNN, and LSTM and implement them with TensorFlow 2. 0. You’ll program a model to classify breast cancer, predict stock market prices, process text as part of Natural Language Processing (NLP), and more.  

By the end of this course, you’ll have a complete understanding to use the power of TensorFlow 2. 0 to train Deep Learning models of varying complexities, without any hassle.  

Target Audience 

This course is for Machine Learning engineers, Deep Learning engineers, and other Data Science professionals. No knowledge of TensorFlow 1. x is required. Basic knowledge of Python is assumed.   

Business Outcomes 

  • Gain knowledge of Deep Learning, one of the most powerful technologies to predict, classify, and translate data  

  • Implement various Deep Learning models and study their examples coded in Python using TensorFlow 2. 0  

  • Acquire the knowledge and hands-on skills that can be applied to a range of problems in finance, healthcare, and other areas