Deep learning with applications using python, Manaswi, Navin Kumar

Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Written by Keras creator and Google AI researcher Francois Chollet, this book builds your understanding through intuitive explanations and practical examples.

Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

About the Technology

Machine learning has made remarkable progress in recent years. We went from near-unusable speech and image recognition, to near-human accuracy. We went from machines that couldn't beat a serious Go player, to defeating a world champion. Behind this progress is deep learning--a combination of engineering advances, best practices, and theory that enables a wealth of previously impossible smart applications.

About the Book

Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Written by Keras creator and Google AI researcher Francois Chollet, this book builds your understanding through intuitive explanations and practical examples. You'll explore challenging concepts and practice with applications in computer vision, natural-language processing, and generative models. By the time you finish, you'll have the knowledge and hands-on skills to apply deep learning in your own projects.

What's Inside

• Deep learning from first principles
• Setting up your own deep-learning environment
• Image-classification models
• Deep learning for text and sequences
• Neural style transfer, text generation, and image generation

About the Reader

Readers need intermediate Python skills. No previous experience with Keras, TensorFlow, or machine learning is required.

About the Author

Francois Chollet works on deep learning at Google in Mountain View, CA. He is the creator of the Keras deep-learning library, as well as a contributor to the TensorFlow machine-learning framework. He also does deep-learning research, with a focus on computer vision and the application of machine learning to formal reasoning. His papers have been published at major conferences in the field, including the Conference on Computer Vision and Pattern Recognition (CVPR), the Conference and Workshop on Neural Information Processing Systems (NIPS), the International Conference on Learning Representations (ICLR), and others.

Table of Contents

PART 1 - FUNDAMENTALS OF DEEP LEARNING
1. What is deep learning?
2. Before we begin: the mathematical building blocks of neural networks
3. Getting started with neural networks
4. Fundamentals of machine learning
PART 2 - DEEP LEARNING IN PRACTICE
5. Deep learning for computer vision
6. Deep learning for text and sequences
7. Advanced deep-learning best practices
8. Generative deep learning
9. Conclusions
appendix A - Installing Keras and its dependencies on Ubuntuappendix B - Running Jupyter notebooks on an EC2 GPU instance

• Understand the intuition and mathematics that power deep learning models
  
• Utilize various algorithms using the R programming language and its packages
  
• Use best practices for experimental design and variable selection
  
• Practice the methodology to approach and effectively solve problems as a data scientist
  
• Evaluate the effectiveness of algorithmic solutions and enhance their predictive power
  

Part 1 focuses on understanding machine learning concepts and tools. This includes machine learning basics with a broad overview of algorithms, techniques, concepts and applications, followed by a tour of the entire Python machine learning ecosystem. Brief guides for useful machine learning tools, libraries and frameworks are also covered.

Part 3 explores multiple real-world case studies spanning diverse domains and industries like retail, transportation, movies, music, marketing, computer vision and finance. For each case study, you will learn the application of various machine learning techniques and methods. The hands-on examples will help you become familiar with state-of-the-art machine learning tools and techniques and understand what algorithms are best suited for any problem.

Practical Machine Learning with Python will empower you to start solving your own problems with machine learning today

• Execute end-to-end machine learning projects and systems
  
• Implement hands-on examples with industry standard, open source, robust machine learning tools and frameworks
  
• Review case studies depicting applications of machine learning and deep learning on diverse domains and industries
• Apply a wide range of machine learning models including regression, classification, and clustering.
• Understand and apply the latest models and methodologies from deep learning including CNNs, RNNs, LSTMs and transfer learning.
  

• Build your own Python-based machine learning systems tailored to solve any problem
• Discover how Python offers a multiple context solution for create machine learning systems
• Practical scenarios using the key Python machine learning libraries to successfully implement in your projects

Book Description

Using machine learning to gain deeper insights from data is a key skill required by modern application developers and analysts alike. Python is a wonderful language to develop machine learning applications. As a dynamic language, it allows for fast exploration and experimentation. With its excellent collection of open source machine learning libraries you can focus on the task at hand while being able to quickly try out many ideas.

This book shows you exactly how to find patterns in your raw data. You will start by brushing up on your Python machine learning knowledge and introducing libraries. You'll quickly get to grips with serious, real-world projects on datasets, using modeling, creating recommendation systems. Later on, the book covers advanced topics such as topic modeling, basket analysis, and cloud computing. These will extend your abilities and enable you to create large complex systems.

With this book, you gain the tools and understanding required to build your own systems, tailored to solve your real-world data analysis problems.

What you will learn

• Build a classification system that can be applied to text, images, or sounds
• Use NumPy, SciPy, scikit-learn - scientific Python open source libraries for scientific computing and machine learning
• Explore the mahotas library for image processing and computer vision
• Build a topic model for the whole of Wikipedia
• Employ Amazon Web Services to run analysis on the cloud
• Debug machine learning problems
• Get to grips with recommendations using basket analysis
• Recommend products to users based on past purchases

• Updated for JIRA 7, this book covers all the new features introduced in JIRA 7 with a dedicated chapter on JIRA Service Desk--one of the biggest new add-ons to JIRA
• This book lays a strong foundation to work with agile projects in JIRA from both the administrator and end user's perspective
• Learn to solve challenging data science problems by building powerful machine learning models using Python

Book Description

Machine learning is increasingly spreading in the modern data-driven world. It is used extensively across many fields such as search engines, robotics, self-driving cars, and more. Machine learning is transforming the way we understand and interact with the world around us.

In the first module, Python Machine Learning Cookbook, you will learn how to perform various machine learning tasks using a wide variety of machine learning algorithms to solve real-world problems and use Python to implement these algorithms.

The second module, Advanced Machine Learning with Python, is designed to take you on a guided tour of the most relevant and powerful machine learning techniques and you'll acquire a broad set of powerful skills in the area of feature selection and feature engineering.

The third module in this learning path, Large Scale Machine Learning with Python, dives into scalable machine learning and the three forms of scalability. It covers the most effective machine learning techniques on a map reduce framework in Hadoop and Spark in Python.

This Learning Path will teach you Python machine learning for the real world. The machine learning techniques covered in this Learning Path are at the forefront of commercial practice.

This Learning Path combines some of the best that Packt has to offer in one complete, curated package. It includes content from the following Packt products:

1. Python Machine Learning Cookbook by Prateek Joshi
2. Advanced Machine Learning with Python by John Hearty
3. Large Scale Machine Learning with Python by Bastiaan Sjardin, Alberto Boschetti, Luca Massaron

What you will learn

• Use predictive modeling and apply it to real-world problems
• Understand how to perform market segmentation using unsupervised learning
• Apply your new-found skills to solve real problems, through clearly-explained code for every technique and test
• Compete with top data scientists by gaining a practical and theoretical understanding of cutting-edge deep learning algorithms
• Increase predictive accuracy with deep learning and scalable data-handling techniques
• Work with modern state-of-the-art large-scale machine learning techniques
• Learn to use Python code to implement a range of machine learning algorithms and techniques

Who this book is for

This Learning Path is for Python programmers who are looking to use machine learning algorithms to create real-world applications. It is ideal for Python professionals who want to work with large and complex datasets and Python developers and analysts or data scientists who are looking to add to their existing skills by accessing some of the most powerful recent trends in data science. Experience with Python, Jupyter Notebooks, and command-line execution together with a good level of mathematical knowledge to understand the concepts is expected. Machine learning basic knowledge is also expected.

You will work through a series of common Python data science problems in an increasing order of complexity. The practical projects presented in this book are simple, clear, and can be used as templates to jump-start many other types of projects. You will learn to create a web application around numerical or categorical predictions, understand the analysis of text, create powerful and interactive presentations, serve restricted access to data, and leverage web plugins to accept credit card payments and donations. You will get your projects into the hands of the world in no time.

Each chapter follows three steps: modeling the right way, designing and developing a local web application, and deploying onto a popular and reliable serverless cloud provider. You can easily jump to or skip particular topics in the book. You also will have access to Jupyter notebooks and code repositories for complete versions of the code covered in the book.

What You'll Learn

• Extend your machine learning models using simple techniques to create compelling and interactive web dashboards
  
• Leverage the Flask web framework for rapid prototyping of your Python models and ideas
• Create dynamic content powered by regression coefficients, logistic regressions, gradient boosting machines, Bayesian classifications, and more
  
• Harness the power of TensorFlow by exporting saved models into web applications
  
• Create rich web dashboards to handle complex real-time user input with JavaScript and Ajax to yield interactive and tailored content
• Create dashboards with paywalls to offer subscription-based access
• Access API data such as Google Maps, OpenWeather, etc.
• Apply different approaches to make sense of text data and return customized intelligence
  
• Build an intuitive and useful recommendation site to add value to users and entice them to keep coming back
  
• Utilize the freemium offerings of Google Analytics and analyze the results
  
• Take your ideas all the way to your customer's plate using the top serverless cloud providers

Who This Book Is For

Those with some programming experience with Python, code editing, and access to an interpreter in working order. The book is geared toward entrepreneurs who want to get their ideas onto the web without breaking the bank, small companies without an IT staff, students wanting exposure and training, and for all data science professionals ready to take things to the next level.