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Umberto Michelucci Advanced Applied Deep Learning (Paperback) (UK IMPORT)
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eBay item number:116082791738
Item specifics
- Condition
- Book Title
- Advanced Applied Deep Learning
- Title
- Advanced Applied Deep Learning
- Subtitle
- Convolutional Neural Networks and Object Detection
- EAN
- 9781484249758
- ISBN
- 9781484249758
- Edition
- 1st ed.
- Release Date
- 09/29/2019
- Release Year
- 2019
- Country/Region of Manufacture
- US
- Item Height
- 235mm
- Genre
- Computing & Internet
- Subject Area
- Computers
- Publication Name
- Advanced Applied Deep Learning : Convolutional Neural Networks and Object Detection
- Publisher
- Apress L. P.
- Item Length
- 9.3 in
- Subject
- Programming / Open Source, Intelligence (Ai) & Semantics, Programming Languages / Python
- Publication Year
- 2019
- Type
- Textbook
- Format
- Trade Paperback
- Language
- English
- Item Weight
- 16.5 Oz
- Item Width
- 6.1 in
- Number of Pages
- Xviii, 285 Pages
About this product
Product Information
Develop and optimize deep learning models with advanced architectures. This book teaches you the intricate details and subtleties of the algorithms that are at the core of convolutional neural networks. In Advanced Applied Deep Learning , you will study advanced topics on CNN and object detection using Keras and TensorFlow. Along the way, you will look at the fundamental operations in CNN, such as convolution and pooling, and then look at more advanced architectures such as inception networks, resnets, and many more. While the book discusses theoretical topics, you will discover how to work efficiently with Keras with many tricks and tips, including how to customize logging in Keras with custom callback classes, what is eager execution, and how to use it in your models. Finally, you will study how object detection works, and build a complete implementation of the YOLO (you only look once) algorithm in Keras and TensorFlow. By the end of the book you will have implemented various models in Keras and learned many advanced tricks that will bring your skills to the next level. What You Will Learn See how convolutional neural networks and object detection work Save weights and models on disk Pause training and restart it at a later stage Use hardware acceleration (GPUs) in your code Work with the Dataset TensorFlow abstraction and use pre-trained models and transfer learning Remove and add layers to pre-trained networks to adapt them to your specific project Apply pre-trained models such as Alexnet and VGG16 to new datasets Who This Book Is For Scientists and researchers with intermediate-to-advanced Python and machine learning know-how. Additionally, intermediate knowledge of Keras and TensorFlow is expected.
Product Identifiers
Publisher
Apress L. P.
ISBN-10
1484249755
ISBN-13
9781484249758
eBay Product ID (ePID)
23038381964
Product Key Features
Number of Pages
Xviii, 285 Pages
Language
English
Publication Name
Advanced Applied Deep Learning : Convolutional Neural Networks and Object Detection
Publication Year
2019
Subject
Programming / Open Source, Intelligence (Ai) & Semantics, Programming Languages / Python
Type
Textbook
Subject Area
Computers
Format
Trade Paperback
Dimensions
Item Weight
16.5 Oz
Item Length
9.3 in
Item Width
6.1 in
Additional Product Features
Number of Volumes
1 Vol.
Illustrated
Yes
Lc Classification Number
Q334-342
Table of Content
Chapter 1: Introduction Chapter Goal: Describe the book, the python infrastructure, give instructions on how to setup a system for deep learning projects No of pages : 30-50 Sub -Topics 1. Goal of the book 2. Prerequisites 3. Python Jupyter Notebooks introduction 4. How to setup a computer to follow the book (docker image?) 5. Tips for Python development and libraries needed (numpy, matplotlib, etc.) 6. The problem of vectorization of code and calculations 7. Additional resources Chapter 2: Convolution Neural Networks Chapter Goal: Describe what convolution is and build a simple network with convolution. No of pages: 50-70 Sub -Topics 1. Overview of convolution 2. Computer vision - example 3. Edge detection with convolution 4. Application to sample images 5. Other convolution examples (horizontal edge detection, vertical edge detection, etc.) 6. Strided convolution 7. N-dimensional convolution 8. Simple neural network with convolution Chapter 3: ResNets, inception networks and other variants Chapter Goal: Describe what resnet, alexnet, inception networks are and their application No of pages: 30-50 Sub -Topics 1. ResNets introduction, development, etc. 2. Inception networks 3. Other architectures Chapter 4: More advanced networks Chapter Goal: Describe the problem of more advanced algorithms, like siamese networks, triplet loss, neural style transfer No of pages: 50-70 Sub -Topics 1. Siamese networks 2. Neural style transfer 3. Different cost functions: style, content and cost Chapter 5: Medical example with CNN (Cancer example) in collaboration with 4quant probably Chapter Goal: Develop a cancer diagnosis CNN with a real dataset in collaboration with 4quant No of pages: 30-50 Sub -Topics 1. 4quant description 2. Problem description 3. Dataset preparation and discussion 4. Network development 5. Optimization 6. Results Chapter 6: Recurrent Neural Networks - an introduction Chapter Goal: explain what Recurrent neural networks are No of pages: 30-50 Sub -Topics 1. Recurrent neural networks 2. Time component in RNN 3. Different types of RNN 4. LSTM Networks Chapter 7: LSTM Networks - a more advanced discussion Chapter Goal: Discuss in more details LSTM Networks No of pages: 50-60 Sub -Topics 1. Overview of LSTM networks 2. The mathematics behind them 3. A practical application Chapter 8: Recurrent Neural Networks and language Chapter Goal: Introduction on how to use RNN and language problem No of pages: 30-50 Sub -Topics 1. Word embeddings and the problem of language modelling 2. Word2vec 3. A practical example Chapter 9: Sequence to sequence architecture Chapter Goal: Introduce sequence to sequence architectures No of pages: 30-50 Sub -Topics 1. Introduction to the architecture 2. Practical implementation tips 3. Real use case application Chapter 10: A practical complete example: Speech recognition Chapter Goal: in this chapter I will put together all that was explained before and do a real-life example ML project (with all aspects included) about speech recognition No of pages: 30-50 Sub -Topics 1. A complete example on speech recognition - an introduction 2. Dataset discussion 3. Dataset preparation 4. The implementation
Copyright Date
2019
Item description from the seller
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- GB 864154811
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eBay item number:116082791738
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