TensorFlow Machine Learning Projects
Build 13 real-world projects with advanced numerical computations using the Python ecosystem
Nonfiction, Computers, Advanced Computing, Engineering, Neural Networks, Artificial Intelligence, General Computing
| Author: | Ankit Jain, Armando Fandango, Amita Kapoor | ISBN: | 9781789132403 |
| Publisher: | Packt Publishing | Publication: | November 30, 2018 |
| Imprint: | Packt Publishing | Language: | English |
| Author: | Ankit Jain, Armando Fandango, Amita Kapoor |
| ISBN: | 9781789132403 |
| Publisher: | Packt Publishing |
| Publication: | November 30, 2018 |
| Imprint: | Packt Publishing |
| Language: | English |
Implement TensorFlow's offerings such as TensorBoard, TensorFlow.js, TensorFlow Probability, and TensorFlow Lite to build smart automation projects
Key Features
- Use machine learning and deep learning principles to build real-world projects
- Get to grips with TensorFlow's impressive range of module offerings
- Implement projects on GANs, reinforcement learning, and capsule network
Book Description
TensorFlow has transformed the way machine learning is perceived. TensorFlow Machine Learning Projects teaches you how to exploit the benefits—simplicity, efficiency, and flexibility—of using TensorFlow in various real-world projects. With the help of this book, you’ll not only learn how to build advanced projects using different datasets but also be able to tackle common challenges using a range of libraries from the TensorFlow ecosystem.
To start with, you’ll get to grips with using TensorFlow for machine learning projects; you’ll explore a wide range of projects using TensorForest and TensorBoard for detecting exoplanets, TensorFlow.js for sentiment analysis, and TensorFlow Lite for digit classification.
As you make your way through the book, you’ll build projects in various real-world domains, incorporating natural language processing (NLP), the Gaussian process, autoencoders, recommender systems, and Bayesian neural networks, along with trending areas such as Generative Adversarial Networks (GANs), capsule networks, and reinforcement learning. You’ll learn how to use the TensorFlow on Spark API and GPU-accelerated computing with TensorFlow to detect objects, followed by how to train and develop a recurrent neural network (RNN) model to generate book scripts.
By the end of this book, you’ll have gained the required expertise to build full-fledged machine learning projects at work.
What you will learn
- Understand the TensorFlow ecosystem using various datasets and techniques
- Create recommendation systems for quality product recommendations
- Build projects using CNNs, NLP, and Bayesian neural networks
- Play Pac-Man using deep reinforcement learning
- Deploy scalable TensorFlow-based machine learning systems
- Generate your own book script using RNNs
Who this book is for
TensorFlow Machine Learning Projects is for you if you are a data analyst, data scientist, machine learning professional, or deep learning enthusiast with basic knowledge of TensorFlow. This book is also for you if you want to build end-to-end projects in the machine learning domain using supervised, unsupervised, and reinforcement learning techniques
Implement TensorFlow's offerings such as TensorBoard, TensorFlow.js, TensorFlow Probability, and TensorFlow Lite to build smart automation projects
Key Features
- Use machine learning and deep learning principles to build real-world projects
- Get to grips with TensorFlow's impressive range of module offerings
- Implement projects on GANs, reinforcement learning, and capsule network
Book Description
TensorFlow has transformed the way machine learning is perceived. TensorFlow Machine Learning Projects teaches you how to exploit the benefits—simplicity, efficiency, and flexibility—of using TensorFlow in various real-world projects. With the help of this book, you’ll not only learn how to build advanced projects using different datasets but also be able to tackle common challenges using a range of libraries from the TensorFlow ecosystem.
To start with, you’ll get to grips with using TensorFlow for machine learning projects; you’ll explore a wide range of projects using TensorForest and TensorBoard for detecting exoplanets, TensorFlow.js for sentiment analysis, and TensorFlow Lite for digit classification.
As you make your way through the book, you’ll build projects in various real-world domains, incorporating natural language processing (NLP), the Gaussian process, autoencoders, recommender systems, and Bayesian neural networks, along with trending areas such as Generative Adversarial Networks (GANs), capsule networks, and reinforcement learning. You’ll learn how to use the TensorFlow on Spark API and GPU-accelerated computing with TensorFlow to detect objects, followed by how to train and develop a recurrent neural network (RNN) model to generate book scripts.
By the end of this book, you’ll have gained the required expertise to build full-fledged machine learning projects at work.
What you will learn
- Understand the TensorFlow ecosystem using various datasets and techniques
- Create recommendation systems for quality product recommendations
- Build projects using CNNs, NLP, and Bayesian neural networks
- Play Pac-Man using deep reinforcement learning
- Deploy scalable TensorFlow-based machine learning systems
- Generate your own book script using RNNs
Who this book is for
TensorFlow Machine Learning Projects is for you if you are a data analyst, data scientist, machine learning professional, or deep learning enthusiast with basic knowledge of TensorFlow. This book is also for you if you want to build end-to-end projects in the machine learning domain using supervised, unsupervised, and reinforcement learning techniques
