Neural Network Projects with Python by Loy, James -- Read -- Imperial Library of Trantor

Technical requirements Computer vision and object recognition Types of object recognition tasks Digital images as neural network input Building blocks of CNNs

Filtering and convolution Max pooling

Basic architecture of CNNs A review of modern CNNs

LeNet (1998) AlexNet (2012) VGG16 (2014) Inception (2014) ResNet (2015) Where we stand today

The cats and dogs dataset Managing image data for Keras Image augmentation Model building

Building a simple CNN Leveraging on pre-trained models using transfer learning

Results analysis Summary Questions

Removing Noise from Images Using Autoencoders

Technical requirements What are autoencoders? Latent representation Autoencoders for data compression The MNIST handwritten digits dataset Building a simple autoencoder

Building autoencoders in Keras Effect of hidden layer size on autoencoder performance

Denoising autoencoders

Deep convolutional denoising autoencoder

Denoising documents with autoencoders

Basic convolutional autoencoder Deep convolutional autoencoder

Summary Questions

Sentiment Analysis of Movie Reviews Using LSTM

Technical requirements Sequential problems in machine learning NLP and sentiment analysis

Why sentiment analysis is difficult

RNN

What's inside an RNN? Long- and short-term dependencies in RNNs The vanishing gradient problem

The LSTM network

LSTMs – the intuition What's inside an LSTM network?

Forget gate Input gate Output gate Making sense of this

The IMDb movie reviews dataset Representing words as vectors

One-hot encoding Word embeddings

Model architecture

Input Word embedding layer LSTM layer Dense layer Output

Model building in Keras

Importing data Zero padding Word embedding and LSTM layers Compiling and training models

Analyzing the results

Confusion matrix

Putting it all together Summary Questions

Implementing a Facial Recognition System with Neural Networks

Technical requirements Facial recognition systems Breaking down the face recognition problem

Face detection

Face detection in Python

Face recognition

Requirements of face recognition systems

Speed Scalability High accuracy with small data

One-shot learning

Naive one-shot prediction – Euclidean distance between two vectors

Siamese neural networks Contrastive loss The faces dataset Creating a Siamese neural network in Keras Model training in Keras Analyzing the results Consolidating our code Creating a real-time face recognition program

The onboarding process Face recognition process Future work

Summary Questions

What's Next?

Putting it all together

Machine Learning and Neural Networks 101 Predicting Diabetes with Multilayer Perceptrons Predicting Taxi Fares with Deep Feedforward Nets Cats Versus Dogs – Image Classification Using CNNs Removing Noise from Images Using Autoencoders Sentiment Analysis of Movie Reviews Using LSTM Implementing a Facial Recognition System with Neural Networks

Cutting edge advancements in neural networks

Generative adversarial networks Deep reinforcement learning

Limitations of neural networks The future of artificial intelligence and machine learning

Artificial general intelligence Automated machine learning

Keeping up with machine learning

Books Scientific journals Practicing on real-world datasets

Favorite machine learning tools Summary

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