$1,260Fluent Python (Paperback)
$199AlphaGo 如何戰勝人類圍棋大師 — 智能硬件 TensorFlow 實踐
$1,188Applied Deep Learning: A Case-Based Approach to Understanding Deep Neural Networks
- A quick guide to gain hands-on experience with deep learning in different domains such as digit/image classification, and texts
- Build your own smart, predictive models with TensorFlow using easy-to-follow approach mentioned in the book
- Understand deep learning and predictive analytics along with its challenges and best practices
Predictive decisions are becoming a huge trend worldwide catering wide sectors of industries by predicting which decisions are more likely to give maximum results. The data mining, statistics, machine learning allows users to discover predictive intelligence by uncovering patterns and showing the relationship among the structured and unstructured data. This book will help you build solutions which will make automated decisions. In the end tune and build your own predictive analytics model with the help of TensorFlow.
This book will be divided in three main sections.
In the first section-Applied Mathematics, Statistics, and Foundations of Predictive Analytics; will cover Linear algebra needed to getting started with data science in a practical manner by using the most commonly used Python packages. It will also cover the needed background in probability and information theory that is must for Data Scientists.
The second section shows how to develop large-scale predictive analytics pipelines using supervised (classification/regression) and unsupervised (clustering) learning algorithms. It'll then demonstrate how to develop predictive models for NLP. Finally, reinforcement learning and recommendation system will be used for developing predictive models.
The third section covers practical mastery of deep learning architectures for advanced predictive analytics: including Deep Neural Networks (MLP & DBN) and Recurrent Neural Networks for high-dimensional and sequence data. Finally, it'll show how to develop Convolutional Neural Networks- based predictive models for emotion recognition, image classification, and sentiment analysis.
So in total, this book will help you control the power of deep learning in diverse fields, providing best practices and tips from the real world use cases and helps you in decision making based on predictive analytics.
What you will learn
- Get solid and theoretical understanding of linear algebra, statistics, and probability theory for predictive analytics
- Learn practical predictive analytics using machine learning algorithms (classification, regression, and clustering) in order to avoid pitfalls and fallacies
- Discern how to develop predictive models for NLP
- Get practical know-how of deep learning architectures for advanced predictive analytics using Deep Neural Networks (MLP and DBN) for predictive analytics
- Emotion recognition, image classification, and sentiment analysis using convolutional neural networks
- Use Recurrent Neural Networks and reinforcement learning for predictive analytics
- Develop recommendation systems for predictive analytics