Articles liés à Advanced Applied Deep Learning: Convolutional Neural...
Advanced Applied Deep Learning: Convolutional Neural Networks and Object Detection - Couverture souple
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Synopsis
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: 50-70Sub -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: 30-50Sub -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 r
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