Tensorflow Deep Learning Projects
Luca Massaron, Alberto Boschetti, Alexey Grigorev, Abhishek Thakur, Rajalingappaa Shanmugamani
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貴賓價: $450深度學習與計算機視覺:算法原理、框架應用與代碼實現 (Deep Learning & Computer Vision:Algorithms and Examples)
貴賓價: $450機器學習導論 (An Introduction to Machine Learning)
貴賓價: $507TensorFlow 深度學習應用實踐
售價: $356Keras 快速上手：基於 Python 的深度學習實戰
- Projects on implementing high performance deep learning models with Tensorflow
- Master the techniques to train different kinds of neural networks with real-world use-cases such as image processing, natural language processing, and more
- Your one-stop guide to master deep learning with Tensorflow in the best possible manner
Tensorflow is one of the most popular frameworks out there, used for machine learning and more recently, deep learning. It provides a fast, efficient framework for training different kinds of deep learning models, with very high accuracy. This book is your guide to master deep learning with Tensorflow, with the help of 12 real-world projects.
Starting with setting up the right Tensorflow environment for deep learning, you will see how you can train different types of deep learning models using Tensorflow - including Convolutional Neural Networks, Recurrent Neural Networks, LSTMs, and more. While doing so, you will build end-to-end deep learning solutions to tackle different real-world problems in image processing, enterprise AI, natural language processing, to name a few. You will train high performance models to generate captions for images automatically, predict stocks performance, create intelligent chatbots, perform large-scale text classification and more. Some advanced aspects like implementation on an enterprise-scale and reinforcement learning are also covered in this book.
By the end of this book, you will have mastered all the concepts of deep learning and their implementation with Tensorflow - and will be able to build and train your own deep learning models with Tensorflow to tackle any kind of problem.
What you will learn
- Set up the Tensorflow environment for deep learning
- Construct your own convnets for effective image processing
- Use LSTMs for caption generation for images
- Forecast stock prediction accurately with a LSTM architecture
- Learn what semantic matching is by detecting duplicate Quora questions
- Set up an AWS instance for distributed computing with Tensorflow to classify large amounts of Images
- Train and set up an intelligent chatbot to understand and interpret human input
- Build an AI capable of playing Pacman by itself and winning