This textbook presents a concise, accessible and engaging first introduction to deep learning, offering a wide range of connectionist models which represent the current state-of-the-art. The text explores the most popular algorithms and architectures in a simple and intuitive style, explaining the mathematical derivations in a step-by-step manner. The content coverage includes convolutional networks, LSTMs, Word2vec, RBMs, DBNs, neural Turing machines, memory networks, and autoencoders. Numerous examples in working Python code are provided throughout the book, and the code is also supplied separately at an accompanying website.
This clearly written and lively primer on deep learning is essential reading for graduate and advanced undergraduate students of computer science, cognitive science and mathematics, as well as fields such as linguistics, logic, philosophy, and psychology.
Sejnowski played an important role in the founding of deep learning, as one of a small group of researchers in the 1980s who challenged the prevailing logic and symbol based version of AI. The new version of AI Sejnowski and others developed, which became deep learning, is fueled instead by data. Deep networks learn from data in the same way that babies experience the world, starting with fresh eyes and gradually acquiring the skills needed to navigate novel environments.
Learning algorithms extract information from raw data information can be used to create knowledge underlies understanding leads to wisdom. Someday a driverless car will know the road better than you do and drive with more skill a deep learning network will diagnose your illness a personal cognitive assistant will augment your puny human brain. It took nature many millions of years to evolve human intelligence AI is on a trajectory measured in decades. Sejnowski prepares us for a deep learning future.
Work with advanced topics in deep learning, such as optimization algorithms, hyperparameter tuning, dropout, and error analysis as well as strategies to address typical problems encountered when training deep neural networks. Youll begin by studying the activation functions mostly with a single neuron (ReLu, sigmoid, and Swish), seeing how to perform linear and logistic regression using TensorFlow, and choosing the right cost function.
What You Will Learn
Implement advanced techniques in the right way in Python and TensorFlow
Debug and optimize advanced methods (such as dropout and regularization)
Carry out error analysis (to realize if one has a bias problem, a variance problem, a data offset problem, and so on)
Set up a machine learning project focused on deep learning on a complex dataset
Deep Learning: Methods and Applications provides an overview of general deep learning methodology and its applications to a variety of signal and information processing tasks. The application areas are chosen with the following three criteria in mind:
expertise or knowledge of the authors
the application areas that have already been transformed by the successful use of deep learning technology, such as speech recognition and computer vision and
the application areas that have the potential to be impacted significantly by deep learning and that have been benefitting from recent research efforts, including natural language and text processing, information retrieval, and multimodal information processing empowered by multitask deep learning.
Deep Learning: Methods and Applications is a timely and important book for researchers and students with an interest in deep learning methodology and its applications in signal and information processing. This book provides an overview of a sweeping range of update deep learning methodologies and their application to a variety of signal and information processing tasks, including not only automatic speech recognition (ASR), but also computer vision, language modeling, text processing, multimodal learning, and information retrieval.