上传者: 43960172
|
上传时间: 2019-12-21 21:49:51
|
文件大小: unknown
|
文件类型: pdf
Python Deep Learning Projects: 9 projects demystifying neural network and deep learning models for building intelligent systems
By 作者: Matthew Lamons – Rahul Kumar – Abhishek Nagaraja
ISBN-10 书号: 1788997093
ISBN-13 书号: 9781788997096
出版日期: 2018-10-31
pages 页数: (670)
Deep learning has been gradually revolutionizing every field of artificial intelligence, making application development easier.
Python Deep Learning Projects imparts all the knowledge needed to implement complex deep learning projects in the field of computational linguistics and computer vision. Each of these projects is unique, helping you progressively master the subject. You’ll learn how to implement a text classifier system using a recurrent neural network (RNN) model and optimize it to understand the shortcomings you might experience while implementing a simple deep learning system.
Similarly, you’ll discover how to develop various projects, including word vector representation, open domain question answering, and building chatbots using seq-to-seq models and language modeling. In addition to this, you’ll cover advanced concepts, such as regularization, gradient clipping, gradient normalization, and bidirectional RNNs, through a series of engaging projects.
By the end of this book, you will have gained knowledge to develop your own deep learning systems in a straightforward way and in an efficient way
Contents
1: BUILDING DEEP LEARNING ENVIRONMENTS
2: TRAINING NN FOR PREDICTION USING REGRESSION
3: WORD REPRESENTATION USING WORD2VEC
4: BUILDING AN NLP PIPELINE FOR BUILDING CHATBOTS
5: SEQUENCE-TO-SEQUENCE MODELS FOR BUILDING CHATBOTS
6: GENERATIVE LANGUAGE MODEL FOR CONTENT CREATION
7: BUILDING SPEECH RECOGNITION WITH DEEPSPEECH2
8: HANDWRITTEN DIGITS CLASSIFICATION USING CONVNETS
9: OBJECT DETECTION USING OPENCV AND TENSORFLOW
10: BUILDING FACE RECOGNITION USING FACENET
11: AUTOMATED IMAGE CAPTIONING
12: POSE ESTIMATION ON 3D MODELS USING CONVNETS
13: IMAGE TRANSLATION USING GANS FOR STYLE TRANSFER