Dipanjan Sarkar / Raghav Bali / Tamoghna Ghosh
Librería Samer Atenea
Librería Aciertas (Toledo)
Kálamo Books
Librería Perelló (Valencia)
Librería Elías (Asturias)
Donde los libros
Librería Kolima (Madrid)
Librería Proteo (Málaga)
Deep learning simplified by taking supervised, unsupervised, and reinforcement learning to the next level using the Python ecosystemKey FeaturesBuild deep learning models with transfer learning principles in Pythonimplement transfer learning to solve real-world research problemsPerform complex operations such as image captioning neural style transferBook DescriptionTransfer learning is a machine learning (ML) technique where knowledge gained during training a set of problems can be used to solve other similar problems. The purpose of this book is two-fold; firstly, we focus on detailed coverage of deep learning (DL) and transfer learning, comparing and contrasting the two with easy-to-follow concepts and examples. The second area of focus is real-world examples and research problems using TensorFlow, Keras, and the Python ecosystem with hands-on examples. The book starts with the key essential concepts of ML and DL, followed by depiction and coverage of important DL architectures such as convolutional neural networks (CNNs), deep neural networks (DNNs), recurrent neural networks (RNNs), long short-term memory (LSTM), and capsule networks. Our focus then shifts to transfer learning concepts, such as model freezing, fine-tuning, pre-trained models including VGG, inception, ResNet, and how these systems perform better than DL models with practical examples. In the concluding chapters, we will focus on a multitude of real-world case studies and problems associated with areas such as computer vision, audio analysis and natural language processing (NLP).By the end of this book, you will be able to implement both DL and transfer learning principles in your own systems.What you will learnSet up your own DL environment with graphics processing unit (GPU) and Cloud supportDelve into transfer learning principles with ML and DL modelsExplore various DL architectures, including CNN, LSTM, and capsule networks Learn about data and network representation and loss functionsGet to grips with models and strategies in transfer learningWalk through potential challenges in building complex transfer learning models from scratchExplore real-world research problems related to computer vision and audio analysisUnderstand how transfer learning can be leveraged in NLP
