Implementing Deep Learning Algorithms with TensorFlow 2.0
- 3.8
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Brief Introduction
Build and train Deep Learning neural networks with TensorFlow 2.0Description
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.
About the Author
Harveen Singh Chadha is an experienced researcher in Deep Learning and is currently working as a Self-Driving Car Engineer. He is currently focused on creating an Advanced Driver Assistance Systems (ADAS) platform. His passion is to help people who currently want to enter the Data Science universe. He is the author of the video course Hands-On Neural Network Programming with TensorFlow by Packt Publishing.
Requirements
- Requirements
- No knowledge of TensorFlow 1.x is required. Basic knowledge of Python is assumed.
Packt Publishing
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