Hands-on transfer learning with python pdf free download

Published on : July 10, Python version: TW pages. Percival 0. By taking you through the development of a real web application from beginning to end, the updated second edition of this hands-on guide demonstrates the practical advantages of test-driven develop If you know how to program, you have the skills to turn data into knowledge, using tools of probability and statistics.

This concise introduction shows you how to perform statistical analysis compu Published on : Oct. With this book, you'll learn how to solve statistical problems with Pyth Published on : June 29, Python version: TW pages. The Flask Mega-Tutorial is an overarching tutorial for Python beginner and intermediate developers that teaches web development with the Flask framework. The tutorial has been thoroughly revised an Want a book on the Django web framework that doesn't leave anything out?

Hands-On Transfer Learning with Python: 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.

Following is what you need for this book: If you are a machine learning developer, engineer, or professional who wants to delve into the deep learning approach for a complex environment, then this is the book for you. Proficiency in Python programming and basic understanding of decision-making in reinforcement learning is assumed.

With the following software and hardware list you can run all code files present in the book Chapter Click here to download it. Nazia Habib is a data scientist who has worked in a variety of industries to generate predictive analytics solutions for diverse groups of stakeholders. She is an expert in building solutions to optimization problems under conditions of uncertainty. Her projects range from predicting user behavior and engagement with social media apps to designing adaptive testing software.

All you need is programming and some machine learning experience to get started. Compare the strengths and weaknesses of the different machine learning approaches: supervised, unsupervised, and reinforcement learning Set up and manage machine learning projects end-to-end Build an anomaly detection system to catch credit card fraud Clusters users into distinct and homogeneous groups Perform semi-supervised learning Develop movie recommender systems using restricted Boltzmann machines Generate synthetic images using generative adversarial networks.