Python Deep Learning Projects: 9 projects demystifying neural network and deep learning models for building intelligent systems

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
Release Finelybook 出版日期: 2018-10-31
pages 页数: (472 )

$44.99


Book Description to Finelybook sorting

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
14: DEVELOP AN AUTONOMOUS AGENT WITH DEEP R LEARNING
15: SUMMARY AND NEXT STEPS IN YOUR DEEP LEARNING CAREER
What You Will Learn
Set up a deep learning development environment on Amazon Web Services (AWS)
Apply GPU-powered instances as well as the deep learning AMI
Implement seq-to-seq networks for modeling natural language processing (NLP)
Develop an end-to-end speech recognition system
Build a system for pixel-wise semantic labeling of an image
Create a system that generates images and their regions
Authors
Matthew Lamons
Matthew Lamons’s background is in experimental psychology and deep learning. Founder and CEO of Skejul—the AI platform to help people manage their activities. Named by Gartner, Inc. as a “Cool Vendor” in the “Cool Vendors in Unified Communication, 2017” report. He founded The Intelligence Factory to build AI strategy, solutions, insights, and talent for enterprise clients and incubate AI tech startups based on the success of his Applied AI MasterMinds group. Matthew’s global community of more than 85 K are leaders in AI, forecasting, robotics, autonomous vehicles, marketing tech, NLP, computer vision, reinforcement, and deep learning. Matthew invites you to join him on his mission to simplify the future and to build AI for good.

Rahul Kumar
Rahul Kumar is an AI scientist, deep learning practitioner, and independent researcher. His expertise in building multilingual NLU systems and large-scale AI infrastructures has brought him to Copenhagen, where he leads a large team of AI engineers as Chief AI Scientist at Jatana. Often invited to speak at AI conferences, he frequently travels between India, Europe, and the US where, among other research initiatives, he collaborates with The Intelligence Factory as NLP data science fellow. Keen to explore the ramifications of emerging technologies for his next book, he’s currently involved in various research projects on Quantum Computing (QC), high-performance computing (HPC), and the brain-computer interaction (BCI).

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