Implementing Deep Learning Algorithms with TensorFlow 2.0: Dive into deep learning with the newest and most improved version of TensorFlow 2.0

Harveen Singh Chadha

€8.99 ~~€117.99~~

1 (1 Ratings)

Video May 2019 2hrs 16mins 1st Edition

Harveen Singh Chadha

€8.99 ~~€117.99~~

1 (1 Ratings)

Video May 2019 2hrs 16mins 1st Edition

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Key benefits

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

Description

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. Note that Miniconda and TensorFlow 2.0 installations are required for taking this course. All the code and supporting files for this course are available on GitHub at https://github.com/PacktPublishing/Implementing-Deep-Learning-Algorithms-with-TensorFlow-2.0

Who is this book for?

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. Note that Miniconda and TensorFlow 2.0 installations are required for taking this course.

What you will learn

Understand what Deep Learning and TensorFlow 2.0 are and what problems they have solved and can solve
Study the various Deep Learning model architectures and work with them
Apply neural network models, deep learning, NLP, and LSTM to several diverse data classification scenarios, including breast cancer classification; predicting stock market data for Google; classifying Reuters news topics; and classifying flower species
Apply your newly-acquired skills to a wide array of practical and real-world scenarios

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