#Hands-On Transfer Learning with Python
This is the code repository for Hands-On Transfer Learning with Python, published by Packt.
Implement advanced deep learning and neural network models using TensorFlow and Keras
What is this book about?
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.
This book covers the following exciting features:
- Set up your own DL environment with graphics processing unit (GPU) and Cloud support
- Delve into transfer learning principles with ML and DL models
- Explore various DL architectures, including CNN, LSTM, and capsule networks
- Learn about data and network representation and loss functions
- Get to grips with models and strategies in transfer learning
If you feel this book is for you, get your copy today!
Instructions and Navigations
All of the code is organized into folders. For example, Chapter02.
The code will look like the following:
import glob
import numpy as np
import os
import shutil
from utils import log_progress
np.random.seed(42)
Following is what you need for this book: 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.
With the following software and hardware list you can run all code files present in the book (Chapter 1-12).
Software and Hardware List
| Chapter | Software required | OS required |
|---|---|---|
| 1-12 | Python 3.6, TensorFlow 1.10, Keras 2.2.0 | Windows, Mac OS X, and Linux (Any) |
We also provide a PDF file that has color images of the screenshots/diagrams used in this book. Click here to download it.
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Get to Know the Author(s)
Dipanjan (DJ) Sarkar is a Data Scientist at Intel, leveraging data science, machine learning, and deep learning to build large-scale intelligent systems. He holds a master of technology degree with specializations in Data Science and Software Engineering.
He has been an analytics practitioner for several years now, specializing in machine learning, NLP, statistical methods, and deep learning. He is passionate about education and also acts as a Data Science Mentor at various organizations like Springboard, helping people learn data science. He is also a key contributor and editor for Towards Data Science, a leading online journal on AI and Data Science. He has also authored several books on R, Python, machine learning, NLP, and deep learning.
Raghav Bali is a Data Scientist at Optum (United Health Group). His work involves research and development of enterprise-level solutions based on machine learning, deep learning, and NLP for Healthcare and Insurance related use cases. In his previous role at Intel, he was involved in enabling proactive data driven IT initiatives. He has also worked in ERP and finance domains with some of the leading organizations in the world. Raghav has also authored multiple books with leading publishers.
Raghav has a master's degree (gold medalist) in Information Technology from International Institute of Information Technology, Bangalore. He loves reading and is a shutterbug capturing moments when he isn't busy solving problems.
Tamoghna Ghosh is a machine learning engineer at Intel Corporation. He has overall 11 years of work experience including 4 years of core research experience at Microsoft Research (MSR) India. At MSR he worked as a research assistant in cryptanalysis of block ciphers.
His technical expertise's are in big data, machine learning, NLP, information retrieval, data visualization and software development. He received M.Tech (Computer Science) degree from the Indian Statistical Institute, Kolkata and M.Sc. (Mathematics) from University of Calcutta with specialization in functional analysis and mathematical modeling/dynamical systems. He is passionate about teaching and conducts internal training in data science for Intel at various levels.
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