In many common practices, data is limited. Training a deep neural network such as ConvNet, which has millions of parameters on a small set of data, can lead to overfitting. To avoid such issues, a common practice is to leverage an existing deep neural network that was trained on a much larger dataset, such as ImageNet (1.2 million labeled images), and fine-tune it on the smaller dataset at hand, which is not drastically different; that is, to continue to train the existing network using this new and smaller dataset. As we have discussed, one of the advantages of deep learning networks is that the first few layers often represent more general patterns mined from the data. Fine-tuning essentially leverages the common knowledge learned from a large pool of data and applies it to a specific area/application. For example, the first few layers in ConvNet may capture universal...
Deep Learning Essentials
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Deep Learning Essentials
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Overview of this book
Deep Learning a trending topic in the field of Artificial Intelligence today and can be considered to be an advanced form of machine learning.
This book will help you take your first steps in training efficient deep learning models and applying them in various practical scenarios. You will model, train, and deploy different kinds of neural networks such as CNN, RNN, and will see some of their applications in real-world domains including computer vision, natural language processing, speech recognition, and so on. You will build practical projects such as chatbots, implement reinforcement learning to build smart games, and develop expert systems for image captioning and processing using Python library such as TensorFlow. This book also covers solutions for different problems you might come across while training models, such as noisy datasets, and small datasets.
By the end of this book, you will have a firm understanding of the basics of deep learning and neural network modeling, along with their practical applications.
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Current Title:
Deep Learning Essentials
Table of Contents (12 chapters)
Preface
Free Chapter
Why Deep Learning?
Getting Yourself Ready for Deep Learning
Getting Started with Neural Networks
Deep Learning in Computer Vision
NLP - Vector Representation
Advanced Natural Language Processing
Multimodality
Deep Reinforcement Learning
Deep Learning Hacks
Deep Learning Trends
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