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Deep learning simplified by taking supervised, unsupervised, and reinforcement learning to the next level using the Python ecosystem
Transfer 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 learn
Who this book is for
Hands-On Transfer Learning with Python is for data scientists, machine learning engineers, analysts and developers with an interest in data and applying state-of-the-art transfer learning methodologies to solve tough real-world problems. Basic proficiency in machine learning and Python is required.
About the Author
Dipanjan Sarkar is a Data Scientist at Intel, the world's largest silicon company, on a mission to make the world more connected and productive. He primarily works on data science, advanced analytics, business intelligence, application development and building large-scale intelligent systems. Besides this, he is also a formal course instructor around the areas of data science and artificial intelligence. Dipanjan holds a master of technology degree in Information Technology with specializations in Data Science and Software Engineering from the International Institute of Information Technology, Bangalore. He is also an avid supporter of self-learning, especially Massive Open Online Courses and also holds a Data Science Specialization from Johns Hopkins University on Coursera besides multiple technical certifications around the areas of machine learning and data science.
Raghav Bali is a Data Scientist at Optum, a United Health Group Company. He is part of the Data Science group where his work is enabling United Health Group develop data driven solutions to transform healthcare sector. He primarily works on data science, analytics and development of scalable machine learning based solutions. In his previous role at Intel as a Data Scientist, his work involved research and development of enterprise solutions in the infrastructure domain leveraging cutting edge techniques from machine learning, deep learning and transfer learning. He has also worked in domains such as ERP and finance with some of the leading organizations of the world. Raghav has a master's degree (gold medalist) in Information Technology from International Institute of Information Technology, Bangalore.
Tamoghna Ghosh is a data scientist at Intel Corporation. He has overall 10+ years of experience in analytics, algorithms, data visualization & software development. He received his master's degree in Computer Science from the Indian Statistical Institute,Kolkata , with a focus on pattern recognition and information retrieval. He also holds a master degree in Mathematics from University of Calcutta with specialization in Functional Analysis & Mathematical Modelling/Dynamical systems.
Tamoghna started his career at Microsoft Research India as a Research assistant and worked on designing some novel approach to crypt-analysis of block ciphers. Tamoghna's interests include algorithms, data science, artificial intelligence and deep learning. In his free time he likes reading books and travelling.
Table of Contents
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