A ML Developer's Notebook


Book Description

A ML Developer's Notebook Specification; 150 Dotted Grid, individually numbered, cream 90g/m2 pages. Perfect matte 220g/m2 soft cover with sleek design. Customised for: ML Developers and Programmers 6" x 9" dimensions; fits backpack, school, home or work. Perfect gift for adults and kids for any gift giving occasion ( Christmas, Birthdays and other festive occasions. ) Designed with Love by the team at 2Scribble.




A Standard ML Developer's Notebook


Book Description

A Standard ML Developer's Notebook Specification; 150 Dotted Grid, individually numbered, cream 90g/m2 pages. Perfect matte 220g/m2 soft cover with sleek design. Customised for: Standard ML Developers and Programmers 6" x 9" dimensions; fits backpack, school, home or work. Perfect gift for adults and kids for any gift giving occasion ( Christmas, Birthdays and other festive occasions. ) Designed with Love by the team at 2Scribble.




Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow


Book Description

Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how. By using concrete examples, minimal theory, and two production-ready Python frameworks—Scikit-Learn and TensorFlow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you’ve learned, all you need is programming experience to get started. Explore the machine learning landscape, particularly neural nets Use Scikit-Learn to track an example machine-learning project end-to-end Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods Use the TensorFlow library to build and train neural nets Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning Learn techniques for training and scaling deep neural nets




ML Programming


Book Description

I am a Developer. This minimalist, classic notebook measure 6" x 9" and features 120 pages of white paper with lines pattern for professional note taking. This is a great personalized unique Notebook for Coders & Programmers & Web Developers that can be used for: Design notes Project management To-do lists Personal journal Creative writing Appointment reminders This journal is loved by geeks, managers, designers, coders & programmers. Stand out of the crowd and create a brand of yourself. ★ Notebook features: Cover: Matte finish Pages: 120 - One full year Layout: Lined Journal Dimensions: 6" x 9" (15.24 x 22.86 cm) Interior: White paper Mate cover for silky finish what will feel amazing in your hands! Perfect for gift giving! ♥ Please feel free to browse our wide range of notebooks and find the best suited for your needs. ✓ Made by Farid Ait Hamza @FaridODev / Notebooks.




Introduction to Machine Learning with Python


Book Description

Machine learning has become an integral part of many commercial applications and research projects, but this field is not exclusive to large companies with extensive research teams. If you use Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions. With all the data available today, machine learning applications are limited only by your imagination. You’ll learn the steps necessary to create a successful machine-learning application with Python and the scikit-learn library. Authors Andreas Müller and Sarah Guido focus on the practical aspects of using machine learning algorithms, rather than the math behind them. Familiarity with the NumPy and matplotlib libraries will help you get even more from this book. With this book, you’ll learn: Fundamental concepts and applications of machine learning Advantages and shortcomings of widely used machine learning algorithms How to represent data processed by machine learning, including which data aspects to focus on Advanced methods for model evaluation and parameter tuning The concept of pipelines for chaining models and encapsulating your workflow Methods for working with text data, including text-specific processing techniques Suggestions for improving your machine learning and data science skills




Deep Learning with Python


Book Description

Summary 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 François 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 François 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 François 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 What is deep learning? Before we begin: the mathematical building blocks of neural networks Getting started with neural networks Fundamentals of machine learning PART 2 - DEEP LEARNING IN PRACTICE Deep learning for computer vision Deep learning for text and sequences Advanced deep-learning best practices Generative deep learning Conclusions appendix A - Installing Keras and its dependencies on Ubuntu appendix B - Running Jupyter notebooks on an EC2 GPU instance




ML Programming


Book Description

♥ You're Worth It! ✓ ML was originally designed as a meta-programming language: a language to be used to create other languages. But over time, it has come to be a general-purpose language, widely used in education as well as math, science, and even finance. This is a great personalized unique ML Programming Notebook journal also is a perfect gift any time of year including birthday, Christmas, friendship gifts, and a journal for mothers; This notebook is easy to carry around and perfect for the desk. It's time to inspire someone you love today! ★ ML Paper journals never need to be charged and no batteries are required! You only need your thoughts and dreams and something to write with, this ML Notebook can be used for: Design notes For school Project management To-do lists Personal journal Creative writing Appointment reminders It's also a worthy receptacle for all of your brightest ideas ★ ★ ML Notebook features: Pages: 120 - One full year Layout: Lined Journal Dimensions: 6" x 9" (15.24 x 22.86 cm) Interior: White paper Cover what will feel amazing in your hands! Perfect for gift giving! ♥ Please feel free to browse our wide range of notebooks and find the best suited for your needs. ✓ Made by "Jessica Davis" - #Programmer, #Developer and #Coder Notebooks.




Machine Learning for Developers


Book Description

Your one-stop guide to becoming a Machine Learning expert. About This Book Learn to develop efficient and intelligent applications by leveraging the power of Machine Learning A highly practical guide explaining the concepts of problem solving in the easiest possible manner Implement Machine Learning in the most practical way Who This Book Is For This book will appeal to any developer who wants to know what Machine Learning is and is keen to use Machine Learning to make their day-to-day apps fast, high performing, and accurate. Any developer who wants to enter the field of Machine Learning can effectively use this book as an entry point. What You Will Learn Learn the math and mechanics of Machine Learning via a developer-friendly approach Get to grips with widely used Machine Learning algorithms/techniques and how to use them to solve real problems Get a feel for advanced concepts, using popular programming frameworks. Prepare yourself and other developers for working in the new ubiquitous field of Machine Learning Get an overview of the most well known and powerful tools, to solve computing problems using Machine Learning. Get an intuitive and down-to-earth introduction to current Machine Learning areas, and apply these concepts on interesting and cutting-edge problems. In Detail Most of us have heard about the term Machine Learning, but surprisingly the question frequently asked by developers across the globe is, “How do I get started in Machine Learning?”. One reason could be attributed to the vastness of the subject area because people often get overwhelmed by the abstractness of ML and terms such as regression, supervised learning, probability density function, and so on. This book is a systematic guide teaching you how to implement various Machine Learning techniques and their day-to-day application and development. You will start with the very basics of data and mathematical models in easy-to-follow language that you are familiar with; you will feel at home while implementing the examples. The book will introduce you to various libraries and frameworks used in the world of Machine Learning, and then, without wasting any time, you will get to the point and implement Regression, Clustering, classification, Neural networks, and more with fun examples. As you get to grips with the techniques, you'll learn to implement those concepts to solve real-world scenarios for ML applications such as image analysis, Natural Language processing, and anomaly detections of time series data. By the end of the book, you will have learned various ML techniques to develop more efficient and intelligent applications. Style and approach This book gives you a glimpse of Machine Learning Models and the application of models at scale using clustering, classification, regression and reinforcement learning with fun examples. Hands-on examples will be presented to understand the power of problem solving with Machine Learning and Advanced architectures, software installation, and configuration.




Machine Learning for iOS Developers


Book Description

Harness the power of Apple iOS machine learning (ML) capabilities and learn the concepts and techniques necessary to be a successful Apple iOS machine learning practitioner! Machine earning (ML) is the science of getting computers to act without being explicitly programmed. A branch of Artificial Intelligence (AI), machine learning techniques offer ways to identify trends, forecast behavior, and make recommendations. The Apple iOS Software Development Kit (SDK) allows developers to integrate ML services, such as speech recognition and language translation, into mobile devices, most of which can be used in multi-cloud settings. Focusing on Apple’s ML services, Machine Learning for iOS Developers is an up-to-date introduction to the field, instructing readers to implement machine learning in iOS applications. Assuming no prior experience with machine learning, this reader-friendly guide offers expert instruction and practical examples of ML integration in iOS. Organized into two sections, the book’s clearly-written chapters first cover fundamental ML concepts, the different types of ML systems, their practical uses, and the potential challenges of ML solutions. The second section teaches readers to use models—both pre-trained and user-built—with Apple’s CoreML framework. Source code examples are provided for readers to download and use in their own projects. This book helps readers: Understand the theoretical concepts and practical applications of machine learning used in predictive data analytics Build, deploy, and maintain ML systems for tasks such as model validation, optimization, scalability, and real-time streaming Develop skills in data acquisition and modeling, classification, and regression. Compare traditional vs. ML approaches, and machine learning on handsets vs. machine learning as a service (MLaaS) Implement decision tree based models, an instance-based machine learning system, and integrate Scikit-learn & Keras models with CoreML Machine Learning for iOS Developers is a must-have resource software engineers and mobile solutions architects wishing to learn ML concepts and implement machine learning on iOS Apps.




ML Programming


Book Description

♥ You're Worth It! ✓ ML was originally designed as a meta-programming language: a language to be used to create other languages. But over time, it has come to be a general-purpose language, widely used in education as well as math, science, and even finance. This is a great personalized unique ML Programming Notebook journal also is a perfect gift any time of year including birthday, Christmas, friendship gifts, and a journal for mothers; This notebook is easy to carry around and perfect for the desk. It's time to inspire someone you love today! ★ ML Paper journals never need to be charged and no batteries are required! You only need your thoughts and dreams and something to write with, this ML Notebook can be used for: Design notes For school Project management To-do lists Personal journal Creative writing Appointment reminders It's also a worthy receptacle for all of your brightest ideas ★ ★ ML Notebook features: Pages: 120 - One full year Layout: Lined Journal Dimensions: 6" x 9" (15.24 x 22.86 cm) Interior: White paper Cover what will feel amazing in your hands! Perfect for gift giving! ♥ Please feel free to browse our wide range of notebooks and find the best suited for your needs. ✓ Made by "Madison Williams" - #Programmer, #Developer and #Coder Notebooks.