Implementing Deep Learning Algorithms with TensorFlow 2.0

Implementing Deep Learning Algorithms with TensorFlow 2.0 - Printable Version

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Implementing Deep Learning Algorithms with TensorFlow 2.0 - aretr - 07-09-2019

[Image: c7221e09adf01fa4516ada9d6b3a0919.jpg]

Implementing Deep Learning Algorithms with TensorFlow 2.0
MP4 | Video: AVC 1280x720 | Audio: AAC 44KHz 2ch | Duration: 2 Hours 16M | 785 MB
Genre: eLearning | Language: English

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.
All the code and supporting files for this course are available on GitHub at

Code:
https://github.com/PacktPublishing/Implementing-Deep-Learning-Algorithms-with-TensorFlow-2.0

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