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Advanced Applied Deep Learning: Convolutional Neural Networks and Object Detection
Chapter 1: Introduction Chapter 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 Describe what convolution is and build a simple network with convolution.No of 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 Describe what resnet, alexnet, inception networks are and their applicationNo of 30-50Sub -Topics1. ResNets introduction, development, etc.2. Inception networks3. Other architectures
Chapter 4: More advanced networksChapter Describe the problem of more advanced algorithms, like siamese networks, triplet loss, neural style transferNo of 50-70Sub -Topics1. Siamese networks2. Neural style transfer3. Different cost style, content and cost
Chapter 5: Medical example with CNN (Cancer example) in collaboration with 4quant probablyChapter Develop a cancer diagnosis CNN with a real dataset in collaboration with 4quantNo of 30-50Sub -Topics1. 4quant description2. Problem description3. Dataset preparation and discussion4. Network development5. Optimization6. Results
Chapter 6: Recurrent Neural Networks - an introductionChapter explain what Recurrent neural networks areNo of 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 Discuss in more details LSTM Networks No of 50-60Sub -Topics1. Overview of LSTM networks2. The mathematics behind them3. A practical application
Chapter 8: Recurrent Neural Networks and language Chapter Introduction on how to use RNN and language problemNo of 30-50Sub -Topics1. Word embeddings and the problem of language modelling2. Word2vec3. A practical example
Chapter 9: Sequence to sequence architectureChapter Introduce sequence to sequence architecturesNo of 30-50Sub -Topics1. Introduction to the architecture2. Practical implementation tips3. Real use case application
Chapter 10: A practical complete Speech recognitionChapter 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 r
Chapter 2: Convolution Neural NetworksChapter Describe what convolution is and build a simple network with convolution.No of 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 Describe what resnet, alexnet, inception networks are and their applicationNo of 30-50Sub -Topics1. ResNets introduction, development, etc.2. Inception networks3. Other architectures
Chapter 4: More advanced networksChapter Describe the problem of more advanced algorithms, like siamese networks, triplet loss, neural style transferNo of 50-70Sub -Topics1. Siamese networks2. Neural style transfer3. Different cost style, content and cost
Chapter 5: Medical example with CNN (Cancer example) in collaboration with 4quant probablyChapter Develop a cancer diagnosis CNN with a real dataset in collaboration with 4quantNo of 30-50Sub -Topics1. 4quant description2. Problem description3. Dataset preparation and discussion4. Network development5. Optimization6. Results
Chapter 6: Recurrent Neural Networks - an introductionChapter explain what Recurrent neural networks areNo of 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 Discuss in more details LSTM Networks No of 50-60Sub -Topics1. Overview of LSTM networks2. The mathematics behind them3. A practical application
Chapter 8: Recurrent Neural Networks and language Chapter Introduction on how to use RNN and language problemNo of 30-50Sub -Topics1. Word embeddings and the problem of language modelling2. Word2vec3. A practical example
Chapter 9: Sequence to sequence architectureChapter Introduce sequence to sequence architecturesNo of 30-50Sub -Topics1. Introduction to the architecture2. Practical implementation tips3. Real use case application
Chapter 10: A practical complete Speech recognitionChapter 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 r
304 pages, Paperback
Published October 2, 2019
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