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Advanced Applied Deep Learning By Umberto Michelucci

Advanced Applied Deep Learning by Umberto Michelucci

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Advanced Applied Deep Learning Summary

Advanced Applied Deep Learning: Convolutional Neural Networks and Object Detection by Umberto Michelucci

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.

About Umberto Michelucci

Umberto Michelucci studied physics and mathematics. He is an expert in numerical simulation, statistics, data science, and machine learning. In addition to several years of research experience at the George Washington University (USA) and the University of Augsburg (DE), he has 15 years of practical experience in the fields of data warehouse, data science, and machine learning. His last book Applied Deep Learning - A Case-Based Approach to Understanding Deep Neural Networks was published by Apress in 2018. He is very active in research in the field of artificial intelligence and publishes his research results regularly in leading journals and gives regular talks at international conferences.
He teaches as a lecturer at the Zurich University of Applied Sciences and at the HWZ University of Applied Sciences in Business Administration. He is also responsible for AI, research, and new technologies at Helsana Vesicherung AG.
He recently founded TOELT LLC, a company aiming to develop new and modern teaching, coaching, and research methods for AI, to make AI technologies and research accessible to everyone.

Table of Contents

Chapter 1: Introduction Chapter Goal: Describe the book, the python infrastructure, give instructions on how to setup a system for deep learning projectsNo of pages : 30-50Sub -Topics1. Goal of the book2. Prerequisites3. Python Jupyter Notebooks introduction4. 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 calculations7. Additional resources
Chapter 2: Convolution Neural NetworksChapter Goal: Describe what convolution is and build a simple network with convolution.No of pages: 50-70Sub -Topics1. Overview of convolution2. Computer vision - example3. Edge detection with convolution4. Application to sample images5. Other convolution examples (horizontal edge detection, vertical edge detection, etc.)6. Strided convolution7. N-dimensional convolution8. Simple neural network with convolution
Chapter 3: ResNets, inception networks and other variantsChapter Goal: Describe what resnet, alexnet, inception networks are and their applicationNo of pages: 30-50Sub -Topics1. ResNets introduction, development, etc.2. Inception networks3. Other architectures
Chapter 4: More advanced networksChapter Goal: Describe the problem of more advanced algorithms, like siamese networks, triplet loss, neural style transferNo of pages: 50-70Sub -Topics1. Siamese networks2. Neural style transfer3. Different cost functions: style, content and cost
Chapter 5: Medical example with CNN (Cancer example) in collaboration with 4quant probablyChapter Goal: Develop a cancer diagnosis CNN with a real dataset in collaboration with 4quantNo of pages: 30-50Sub -Topics1. 4quant description2. Problem description3. Dataset preparation and discussion4. Network development5. Optimization6. Results
Chapter 6: Recurrent Neural Networks - an introductionChapter Goal: explain what Recurrent neural networks areNo of pages: 30-50Sub -Topics1. Recurrent neural networks 2. Time component in RNN3. Different types of RNN4. LSTM Networks
Chapter 7: LSTM Networks - a more advanced discussionChapter Goal: Discuss in more details LSTM Networks No of pages: 50-60Sub -Topics1. Overview of LSTM networks2. The mathematics behind them3. A practical application
Chapter 8: Recurrent Neural Networks and language Chapter Goal: Introduction on how to use RNN and language problemNo of pages: 30-50Sub -Topics1. Word embeddings and the problem of language modelling2. Word2vec3. A practical example
Chapter 9: Sequence to sequence architectureChapter Goal: Introduce sequence to sequence architecturesNo of pages: 30-50Sub -Topics1. Introduction to the architecture2. Practical implementation tips3. Real use case application
Chapter 10: A practical complete example: Speech recognitionChapter 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 recognitionNo of pages: 30-50Sub -Topics1. A complete example on speech recognition - an introduction2. Dataset discussion3. Dataset preparation4. The implementation

Additional information

Advanced Applied Deep Learning: Convolutional Neural Networks and Object Detection by Umberto Michelucci
Used - Like New
Book picture is for illustrative purposes only, actual binding, cover or edition may vary.
The book has been read, but looks new. The book cover has no visible wear, and the dust jacket is included if applicable. No missing or damaged pages, no tears, possible very minimal creasing, no underlining or highlighting of text, and no writing in the margins

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