Neural Network Projects with Python: The ultimate guide to using Python to explore the true power of neural networks through six projects

Description

Build your Machine Learning portfolio by creating 6 cutting-edge Artificial Intelligence projects using neural networks in Python Key FeaturesDiscover neural network architectures (like CNN and LSTM) that are driving recent advancements in AI Build expert neural networks in Python using popular libraries such as Keras Includes projects such as object detection, face identification, sentiment analysis, and moreBook Description Neural networks are at the core of recent AI advances, providing some of the best resolutions to many real-world problems, including image recognition, medical diagnosis, text analysis, and more. This book goes through some basic neural network and deep learning concepts, as well as some popular libraries in Python for implementing them. It contains practical demonstrations of neural networks in domains such as fare prediction, image classification, sentiment analysis, and more. In each case, the book provides a problem statement, the specific neural network architecture required to tackle that problem, the reasoning behind the algorithm used, and the associated Python code to implement the solution from scratch. In the process, you will gain hands-on experience with using popular Python libraries such as Keras to build and train your own neural networks from scratch. By the end of this book, you will have mastered the different neural network architectures and created cutting-edge AI projects in Python that will immediately strengthen your machine learning portfolio. What you will learnLearn various neural network architectures and its advancements in AI Master deep learning in Python by building and training neural network Master neural networks for regression and classification Discover convolutional neural networks for image recognition Learn sentiment analysis on textual data using Long Short-Term Memory Build and train a highly accurate facial recognition security systemWho this book is for This book is a perfect match for data scientists, machine learning engineers, and deep learning enthusiasts who wish to create practical neural network projects in Python. Readers should already have some basic knowledge of machine learning and neural networks. Table of ContentsMachine Learning and Neural Networks 101Predicting Diabetes with Multilayer PerceptronsPredicting Taxi Fares with Deep Feedforward NetworksCats Versus Dogs - Image Classification Using CNNsRemoving Noise from Images Using AutoencodersSentiment Analysis of Movie Reviews Using LSTMImplementing a Facial Recognition System with Neural NetworksWhat's Next? Cover Title Page Copyright and Credits Dedication About Packt Contributors Table of Contents Preface Chapter 1: Machine Learning and Neural Networks 101 What is machine learning? Machine learning algorithms The machine learning workflow Setting up your computer for machine learning Neural networks Why neural networks? The basic architecture of neural networks Training a neural network from scratch in Python Feedforward The loss function Backpropagation Putting it all together Deep learning and neural networks pandas – a powerful data analysis toolkit in Python pandas DataFrames Data visualization in pandas Data preprocessing in pandas Encoding categorical variables Imputing missing values Using pandas in neural network projects TensorFlow and Keras – open source deep learning libraries The fundamental building blocks in Keras Layers – the atom of neural networks in Keras Models – a collection of layers Loss function – error metric for neural network training Optimizers – training algorithm for neural networks Creating neural networks in Keras Other Python libraries Summary Chapter 2: Predicting Diabetes with Multilayer Perceptrons Technical requirements Diabetes – understanding the problem AI in healthcare Automated diagnosis The diabetes mellitus dataset Exploratory data analysis Data preprocessing Handling missing values Data standardization Splitting the data into training, testing, and validation sets MLPs Model architecture Input layer Hidden layers Activation functions ReLU Sigmoid activation function Model building in Python using Keras Model building Model compilation Model training Results analysis Testing accuracy Confusion matrix ROC curve Further improvements Summary Questions Chapter 3: Predicting Taxi Fares with Deep Feedforward Networks Technical requirements Predicting taxi fares in New York City The NYC taxi fares dataset Exploratory data analysis Visualizing geolocation data Ridership by day and hour Data preprocessing Handling missing values and data anomalies Feature engineering Temporal features Geolocation features Feature scaling Deep feedforward networks Model architecture Loss functions for regression problems Model building in Python using Keras Results analysis Putting it all together Summary Questions Chapter 4: Cats Versus Dogs - Image Classification Using CNNs 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 Chapter 5: 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 Chapter 6: 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 Chapter 7: 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 Chapter 8: 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 Other Books You May Enjoy Index