Deep Learning with Python
Learn Best Practices of Deep Learning Models with PyTorch
Authors: Moolayil, Jojo & Ketkar, Nikhil
What is this book about?
Master the practical aspects of implementing deep learning solutions with PyTorch, using a hands-on approach to understanding both theory and practice. This updated edition will prepare you for applying deep learning to real world problems with a sound theoretical foundation and practical know-how with PyTorch, a platform developed by Facebook’s Artificial Intelligence Research Group.
You’ll start with a perspective on how and why deep learning with PyTorch has emerged as an path-breaking framework with a set of tools and techniques to solve real-world problems. Next, the book will ground you with the mathematical fundamentals of linear algebra, vector calculus, probability and optimization. Having established this foundation, you’ll move on to key components and functionality of PyTorch including layers, loss functions and optimization algorithms.
You’ll also gain an understanding of Graphical Processing Unit (GPU) based computation, which is essential for training deep learning models. All the key architectures in deep learning are covered, including feedforward networks, convolution neural networks, recurrent neural networks, long short-term memory networks, autoencoders and generative adversarial networks. Backed by a number of tricks of the trade for training and optimizing deep learning models, this edition of Deep Learning with Python explains the best practices in taking these models to production with PyTorch.
What You’ll Learn
- Review machine learning fundamentals such as overfitting, underfitting, and regularization.
- Understand deep learning fundamentals such as feed-forward networks, convolution neural networks, recurrent neural networks, automatic differentiation, and stochastic gradient descent.
- Apply in-depth linear algebra with PyTorch
- Explore PyTorch fundamentals and its building blocks
- Work with tuning and optimizing models
Who This Book Is For
Beginners with a working knowledge of Python who want to understand Deep Learning in a practical, hands-on manner.
Applied Supervised Learning with R
Use ML libraries of R to build models that solve problems and predict business trends
Authors: Moolayil, Jojo & Ramasubramanian, Karthik
What is this book about?
Supervised Learning with R covers the complete process of developing applications with supervised machine learning algorithms that cater to your business needs. Your learning curve starts with developing your analytical thinking towards creating a problem statement using business inputs or domain research. You will learn many evaluation metrics that compare various algorithms, and you can then use these metrics to select the best algorithm for your problem. After finalizing the algorithm you want to use, you will study the hyperparameter optimization technique to fine tune your set of optimal parameters. To avoid overfitting your model, you will also be shown how to add various regularization terms.
When you complete the book, you will find yourself an expert at modelling a supervised machine learning algorithm that precisely fulfills your business need.
What will you learn?
Develop analytical thinking to precisely identify a business problem. Wrangle data with dplyr, tidyr, and reshape2. Validate your supervised machine learning model using k-fold. Optimize hyperparameters with grid and random search and bayesian optimization. Deploy your model on AWS Lambda with plumber, an R package. Improve the model performance with feature selection and dimensionality reduction. Use a research paper with a real-world problem, frame a problem statement, and replicate its results with a supervised machine learning model
Who This Book Is For? Researchers, Academics, Software engineers and data engineers with basic programming skills in any language and who are keen on exploring deep learning for a career move or an enterprise project.
Learn Keras for Deep Neural Networks
A Fast-Track Approach to Modern Deep Learning with Python
Authors: Moolayil, Jojo
Tech Reviewer: Swamynathan, Manohar
What is the book about?Learn, understand, and implement deep neural networks in a math- and programming-friendly approach using Keras and Python. The book focuses on an end-to-end approach to developing supervised learning algorithms in regression and classification with practical business-centric use-cases implemented in Keras.
The overall book comprises three sections with two chapters in each section. The first section prepares you with all the necessary basics to get started in deep learning. Chapter 1 introduces you to the world of deep learning and its difference from machine learning, the choices of frameworks for deep learning, and the Keras ecosystem. You will cover a real-life business problem that can be solved by supervised learning algorithms with deep neural networks. You’ll tackle one use case for regression and another for classification leveraging popular Kaggle datasets.
Later, you will see an interesting and challenging part of deep learning: hyperparameter tuning; helping you further improve your models when building robust deep learning applications. Finally, you’ll further hone your skills in deep learning and cover areas of active development and research in deep learning.
At the end of Learn Keras for Deep Neural Networks, you will have a thorough understanding of deep learning principles and have practical hands-on experience in developing enterprise-grade deep learning solutions in Keras.
What You’ll Learn? Master fast-paced practical deep learning concepts with math- and programming-friendly abstractions. Design, develop, train, validate, and deploy deep neural networks using the Keras framework.Use best practices for debugging and validating deep learning modelsDeploy and integrate deep learning as a service into a larger software service or productExtend deep learning principles into other popular frameworks
Who This Book Is For? Software engineers and data engineers with basic programming skills in any language and who are keen on exploring deep learning for a career move or an enterprise project.
Smarter Decisions – The Intersection of IoT & Decision Science
Enter the world of Internet of Things with the power of data science with this highly practical, engaging book
Authors: Moolayil, Jojo
Tech Reviewer: Basak, Anindita
What is the book about?
With an increasing number of devices getting connected to the Internet, massive amounts of data are being generated that can be used for analysis. This book helps you to understand Internet of Things in depth and decision science, and solve business use cases. With IoT, the frequency and impact of the problem is huge. Addressing a problem with such a huge impact requires a very structured approach.
The entire journey of addressing the problem by defining it, designing the solution, and executing it using decision science is articulated in this book through engaging and easy-to-understand business use cases. You will get a detailed understanding of IoT, decision science, and the art of solving a business problem in IoT through decision science.
By the end of this book, you’ll have an understanding of the complex aspects of decision making in IoT and will be able to take that knowledge with you onto whatever project calls for it
What You Will Learn Explore decision science with respect to IoTExplore decision science with respect to IoT. Get to know the end to end analytics stack – Descriptive + Inquisitive + Predictive + Prescriptive. Solve problems in IoT connected assets and connected operations. Design and solve real-life IoT business use cases using