# Applied Neural Networks with TensorFlow 2

### eBook Details:

**Paperback:**314 pages**Publisher:**WOW! eBook (December 14, 2020)**Language:**English**ISBN-10:**1484265122**ISBN-13:**978-1484265123

### eBook Description:

Applied Neural Networks with TensorFlow 2: API Oriented Deep Learning with Python: Differentiate supervised, unsupervised, and reinforcement machine learning

Implement deep learning applications using TensorFlow while learning the “why” through in-depth conceptual explanations.

You’ll start by learning what deep learning offers over other machine learning models. Then familiarize yourself with several technologies used to create deep learning models. While some of these technologies are complementary, such as Pandas, Scikit-Learn, and Numpy – others are competitors, such as PyTorch, Caffe, and Theano. This book clarifies the positions of deep learning and TensorFlow among their peers.

You’ll then work on supervised deep learning models to gain applied experience with the technology. A single-layer of multiple perceptrons will be used to build a shallow neural network before turning it into a deep neural network. After showing the structure of the ANNs, a real-life application will be created with TensorFlow 2.0 Keras API. Next, you’ll work on data augmentation and batch normalization methods. Then, the Fashion MNIST dataset will be used to train a CNN. CIFAR10 and Imagenet pre-trained models will be loaded to create already advanced CNNs.

What You’ll Learn

- Compare competing technologies and see why TensorFlow is more popular
- Generate text, image, or sound with GANs
- Predict the rating or preference a user will give to an item
- Sequence data with recurrent neural networks

Finally, move into theoretical applications and unsupervised learning with auto-encoders and reinforcement learning with tf-agent models. With this Applied Neural Networks with TensorFlow 2 book, you’ll delve into applied deep learning practical functions and build a wealth of knowledge about how to use TensorFlow effectively.

## 10 Responses

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[…] This TensorFlow 2.x in the Colaboratory Cloud book contains all the applied math and programming you need to master the content. Examples range from simple to relatively complex when necessary to ensure acquisition of appropriate deep learning concepts and constructs. Examples are carefully explained, concise, accurate, and complete to perfectly complement deep learning skill development. Care is taken to walk you through the foundational principles of deep learning through clear examples written in Python that you can try out and experiment with using Google Colab from the comfort of your own home or office. […]

[…] This TensorFlow 2.x in the Colaboratory Cloud book contains all the applied math and programming you need to master the content. Examples range from simple to relatively complex when necessary to ensure acquisition of appropriate deep learning concepts and constructs. Examples are carefully explained, concise, accurate, and complete to perfectly complement deep learning skill development. Care is taken to walk you through the foundational principles of deep learning through clear examples written in Python that you can try out and experiment with using Google Colab from the comfort of your own home or office. […]

[…] This TensorFlow 2.x in the Colaboratory Cloud book contains all the applied math and programming you need to master the content. Examples range from simple to relatively complex when necessary to ensure acquisition of appropriate deep learning concepts and constructs. Examples are carefully explained, concise, accurate, and complete to perfectly complement deep learning skill development. Care is taken to walk you through the foundational principles of deep learning through clear examples written in Python that you can try out and experiment with using Google Colab from the comfort of your own home or office. […]

[…] Starting with an introduction to the fundamentals of deep reinforcement learning and TensorFlow 2.x, the book covers OpenAI Gym, model-based RL, model-free RL, and how to develop basic agents. You’ll discover how to implement advanced deep reinforcement learning algorithms such as actor-critic, deep deterministic policy gradients, deep-Q networks, proximal policy optimization, and deep recurrent Q-networks for training your RL agents. As you advance, you’ll explore the applications of reinforcement learning by building cryptocurrency trading agents, stock/share trading agents, and intelligent agents for automating task completion. Finally, you’ll find out how to deploy deep reinforcement learning agents to the cloud and build cross-platform apps using TensorFlow 2.x. […]