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Author: J. W. Wilson
Publisher:
ISBN: 9781953806345
Category :
Languages : en
Pages :
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Book Description
Discover the New Scientific Approach That is Transforming How the World Learns and Changes Behaviors. Cracking the Learning Code is a disruptive book that will completely transform your beliefs about how you learn and change behaviors, so you can reach your goals and live the life of your dreams. This easy-to-read compendium takes a deep dive into how our outdated learning strategies prevent us from living up to our own expectations and what we can do about it.Based on over thirty years of research, it is 725 pages of the world's most comprehensive reference source that unravels the mystery behind how you can more quickly learn, remember, and change.The Learning Code's cutting-edge scientific approach supports you to rapidly log information into your brain, so it is more easily retained and recalled, which allows you to live a more joyful, fulfilled, and meaningful life. Plus, this knowledge can help you have a more dramatic impact on your work, and in the world. We took a unique approach. You don't have to read the whole book to get what you need. Once a general understanding of a topic is presented, you will see special icons. They alert you when a chapter will start to go deeper into the science. Here you have a choice. You can either skip to the next topic or you can read deeper to gain a more profound view of that subject, the science behind it, and the effect it is having upon you.The Learning Code is the holy grail in the field of education and behavioral change. This knowledge needs to be available worldwide.-Dr. Jeanette Vos Cofounder, National Institute for Teaching Excellence, Coauthor, The Learning RevolutionCracking the Learning Code is the reference source for this new revolutionary approach to learning. This new way of learning enables you to easily solidify information into your long-term memory so you can retain it longer and recall it faster.
Author: J. W. Wilson
Publisher:
ISBN: 9781953806345
Category :
Languages : en
Pages :
Get Book Here
Book Description
Discover the New Scientific Approach That is Transforming How the World Learns and Changes Behaviors. Cracking the Learning Code is a disruptive book that will completely transform your beliefs about how you learn and change behaviors, so you can reach your goals and live the life of your dreams. This easy-to-read compendium takes a deep dive into how our outdated learning strategies prevent us from living up to our own expectations and what we can do about it.Based on over thirty years of research, it is 725 pages of the world's most comprehensive reference source that unravels the mystery behind how you can more quickly learn, remember, and change.The Learning Code's cutting-edge scientific approach supports you to rapidly log information into your brain, so it is more easily retained and recalled, which allows you to live a more joyful, fulfilled, and meaningful life. Plus, this knowledge can help you have a more dramatic impact on your work, and in the world. We took a unique approach. You don't have to read the whole book to get what you need. Once a general understanding of a topic is presented, you will see special icons. They alert you when a chapter will start to go deeper into the science. Here you have a choice. You can either skip to the next topic or you can read deeper to gain a more profound view of that subject, the science behind it, and the effect it is having upon you.The Learning Code is the holy grail in the field of education and behavioral change. This knowledge needs to be available worldwide.-Dr. Jeanette Vos Cofounder, National Institute for Teaching Excellence, Coauthor, The Learning RevolutionCracking the Learning Code is the reference source for this new revolutionary approach to learning. This new way of learning enables you to easily solidify information into your long-term memory so you can retain it longer and recall it faster.
Author: J. W. Wilson
Publisher:
ISBN: 9781953806314
Category :
Languages : en
Pages :
Get Book Here
Book Description
Discover the New Scientific Approach That is Transforming How the World Learns and Changes Behaviors. Cracking the Learning Code is a disruptive book that will completely transform your beliefs about how you learn and change behaviors, so you can reach your goals and live the life of your dreams. This easy-to-read compendium takes a deep dive into how our outdated learning strategies prevent us from living up to our own expectations and what we can do about it.Based on over thirty years of research, it is 725 pages of the world's most comprehensive reference source that unravels the mystery behind how you can more quickly learn, remember, and change.The Learning Code's cutting-edge scientific approach supports you to rapidly log information into your brain, so it is more easily retained and recalled, which allows you to live a more joyful, fulfilled, and meaningful life. Plus, this knowledge can help you have a more dramatic impact on your work, and in the world. We took a unique approach. You don't have to read the whole book to get what you need. Once a general understanding of a topic is presented, you will see special icons. They alert you when a chapter will start to go deeper into the science. Here you have a choice. You can either skip to the next topic or you can read deeper to gain a more profound view of that subject, the science behind it, and the effect it is having upon you.The Learning Code is the holy grail in the field of education and behavioral change. This knowledge needs to be available worldwide.-Dr. Jeanette Vos Cofounder, National Institute for Teaching Excellence, Coauthor, The Learning RevolutionCracking the Learning Code is the reference source for this new revolutionary approach to learning. This new way of learning enables you to easily solidify information into your long-term memory so you can retain it longer and recall it faster.
Author: J. W. Wilson
Publisher:
ISBN: 9781953806376
Category :
Languages : en
Pages :
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Book Description
Author: Paolo Perrotta
Publisher: Pragmatic Bookshelf
ISBN: 1680507710
Category : Computers
Languages : en
Pages : 455
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Book Description
You've decided to tackle machine learning - because you're job hunting, embarking on a new project, or just think self-driving cars are cool. But where to start? It's easy to be intimidated, even as a software developer. The good news is that it doesn't have to be that hard. Master machine learning by writing code one line at a time, from simple learning programs all the way to a true deep learning system. Tackle the hard topics by breaking them down so they're easier to understand, and build your confidence by getting your hands dirty. Peel away the obscurities of machine learning, starting from scratch and going all the way to deep learning. Machine learning can be intimidating, with its reliance on math and algorithms that most programmers don't encounter in their regular work. Take a hands-on approach, writing the Python code yourself, without any libraries to obscure what's really going on. Iterate on your design, and add layers of complexity as you go. Build an image recognition application from scratch with supervised learning. Predict the future with linear regression. Dive into gradient descent, a fundamental algorithm that drives most of machine learning. Create perceptrons to classify data. Build neural networks to tackle more complex and sophisticated data sets. Train and refine those networks with backpropagation and batching. Layer the neural networks, eliminate overfitting, and add convolution to transform your neural network into a true deep learning system. Start from the beginning and code your way to machine learning mastery. What You Need: The examples in this book are written in Python, but don't worry if you don't know this language: you'll pick up all the Python you need very quickly. Apart from that, you'll only need your computer, and your code-adept brain.
Author: Jeremy Howard
Publisher: O'Reilly Media
ISBN: 1492045497
Category : Computers
Languages : en
Pages : 624
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Book Description
Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications. Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. Train models in computer vision, natural language processing, tabular data, and collaborative filtering Learn the latest deep learning techniques that matter most in practice Improve accuracy, speed, and reliability by understanding how deep learning models work Discover how to turn your models into web applications Implement deep learning algorithms from scratch Consider the ethical implications of your work Gain insight from the foreword by PyTorch cofounder, Soumith Chintala
Author: Scott H. Young
Publisher: HarperCollins
ISBN: 0062852744
Category : Business & Economics
Languages : en
Pages : 278
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Book Description
Now a Wall Street Journal bestseller. Learn a new talent, stay relevant, reinvent yourself, and adapt to whatever the workplace throws your way. Ultralearning offers nine principles to master hard skills quickly. This is the essential guide to future-proof your career and maximize your competitive advantage through self-education. In these tumultuous times of economic and technological change, staying ahead depends on continual self-education—a lifelong mastery of fresh ideas, subjects, and skills. If you want to accomplish more and stand apart from everyone else, you need to become an ultralearner. The challenge of learning new skills is that you think you already know how best to learn, as you did as a student, so you rerun old routines and old ways of solving problems. To counter that, Ultralearning offers powerful strategies to break you out of those mental ruts and introduces new training methods to help you push through to higher levels of retention. Scott H. Young incorporates the latest research about the most effective learning methods and the stories of other ultralearners like himself—among them Benjamin Franklin, chess grandmaster Judit Polgár, and Nobel laureate physicist Richard Feynman, as well as a host of others, such as little-known modern polymath Nigel Richards, who won the French World Scrabble Championship—without knowing French. Young documents the methods he and others have used to acquire knowledge and shows that, far from being an obscure skill limited to aggressive autodidacts, ultralearning is a powerful tool anyone can use to improve their career, studies, and life. Ultralearning explores this fascinating subculture, shares a proven framework for a successful ultralearning project, and offers insights into how you can organize and exe - cute a plan to learn anything deeply and quickly, without teachers or budget-busting tuition costs. Whether the goal is to be fluent in a language (or ten languages), earn the equivalent of a college degree in a fraction of the time, or master multiple tools to build a product or business from the ground up, the principles in Ultralearning will guide you to success.
Author: Luca Pietro Giovanni Antiga
Publisher: Simon and Schuster
ISBN: 1638354073
Category : Computers
Languages : en
Pages : 518
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Book Description
“We finally have the definitive treatise on PyTorch! It covers the basics and abstractions in great detail. I hope this book becomes your extended reference document.” —Soumith Chintala, co-creator of PyTorch Key Features Written by PyTorch’s creator and key contributors Develop deep learning models in a familiar Pythonic way Use PyTorch to build an image classifier for cancer detection Diagnose problems with your neural network and improve training with data augmentation Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About The Book Every other day we hear about new ways to put deep learning to good use: improved medical imaging, accurate credit card fraud detection, long range weather forecasting, and more. PyTorch puts these superpowers in your hands. Instantly familiar to anyone who knows Python data tools like NumPy and Scikit-learn, PyTorch simplifies deep learning without sacrificing advanced features. It’s great for building quick models, and it scales smoothly from laptop to enterprise. Deep Learning with PyTorch teaches you to create deep learning and neural network systems with PyTorch. This practical book gets you to work right away building a tumor image classifier from scratch. After covering the basics, you’ll learn best practices for the entire deep learning pipeline, tackling advanced projects as your PyTorch skills become more sophisticated. All code samples are easy to explore in downloadable Jupyter notebooks. What You Will Learn Understanding deep learning data structures such as tensors and neural networks Best practices for the PyTorch Tensor API, loading data in Python, and visualizing results Implementing modules and loss functions Utilizing pretrained models from PyTorch Hub Methods for training networks with limited inputs Sifting through unreliable results to diagnose and fix problems in your neural network Improve your results with augmented data, better model architecture, and fine tuning This Book Is Written For For Python programmers with an interest in machine learning. No experience with PyTorch or other deep learning frameworks is required. About The Authors Eli Stevens has worked in Silicon Valley for the past 15 years as a software engineer, and the past 7 years as Chief Technical Officer of a startup making medical device software. Luca Antiga is co-founder and CEO of an AI engineering company located in Bergamo, Italy, and a regular contributor to PyTorch. Thomas Viehmann is a Machine Learning and PyTorch speciality trainer and consultant based in Munich, Germany and a PyTorch core developer. Table of Contents PART 1 - CORE PYTORCH 1 Introducing deep learning and the PyTorch Library 2 Pretrained networks 3 It starts with a tensor 4 Real-world data representation using tensors 5 The mechanics of learning 6 Using a neural network to fit the data 7 Telling birds from airplanes: Learning from images 8 Using convolutions to generalize PART 2 - LEARNING FROM IMAGES IN THE REAL WORLD: EARLY DETECTION OF LUNG CANCER 9 Using PyTorch to fight cancer 10 Combining data sources into a unified dataset 11 Training a classification model to detect suspected tumors 12 Improving training with metrics and augmentation 13 Using segmentation to find suspected nodules 14 End-to-end nodule analysis, and where to go next PART 3 - DEPLOYMENT 15 Deploying to production
Author: Luis Serrano
Publisher: Simon and Schuster
ISBN: 1617295914
Category : Computers
Languages : en
Pages : 510
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Book Description
Discover valuable machine learning techniques you can understand and apply using just high-school math. In Grokking Machine Learning you will learn: Supervised algorithms for classifying and splitting data Methods for cleaning and simplifying data Machine learning packages and tools Neural networks and ensemble methods for complex datasets Grokking Machine Learning teaches you how to apply ML to your projects using only standard Python code and high school-level math. No specialist knowledge is required to tackle the hands-on exercises using Python and readily available machine learning tools. Packed with easy-to-follow Python-based exercises and mini-projects, this book sets you on the path to becoming a machine learning expert. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Discover powerful machine learning techniques you can understand and apply using only high school math! Put simply, machine learning is a set of techniques for data analysis based on algorithms that deliver better results as you give them more data. ML powers many cutting-edge technologies, such as recommendation systems, facial recognition software, smart speakers, and even self-driving cars. This unique book introduces the core concepts of machine learning, using relatable examples, engaging exercises, and crisp illustrations. About the book Grokking Machine Learning presents machine learning algorithms and techniques in a way that anyone can understand. This book skips the confused academic jargon and offers clear explanations that require only basic algebra. As you go, you’ll build interesting projects with Python, including models for spam detection and image recognition. You’ll also pick up practical skills for cleaning and preparing data. What's inside Supervised algorithms for classifying and splitting data Methods for cleaning and simplifying data Machine learning packages and tools Neural networks and ensemble methods for complex datasets About the reader For readers who know basic Python. No machine learning knowledge necessary. About the author Luis G. Serrano is a research scientist in quantum artificial intelligence. Previously, he was a Machine Learning Engineer at Google and Lead Artificial Intelligence Educator at Apple. Table of Contents 1 What is machine learning? It is common sense, except done by a computer 2 Types of machine learning 3 Drawing a line close to our points: Linear regression 4 Optimizing the training process: Underfitting, overfitting, testing, and regularization 5 Using lines to split our points: The perceptron algorithm 6 A continuous approach to splitting points: Logistic classifiers 7 How do you measure classification models? Accuracy and its friends 8 Using probability to its maximum: The naive Bayes model 9 Splitting data by asking questions: Decision trees 10 Combining building blocks to gain more power: Neural networks 11 Finding boundaries with style: Support vector machines and the kernel method 12 Combining models to maximize results: Ensemble learning 13 Putting it all in practice: A real-life example of data engineering and machine learning
Author: Daniel Zingaro
Publisher: No Starch Press
ISBN: 1718501331
Category : Computers
Languages : en
Pages : 398
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Book Description
Learn to Code by Solving Problems is a practical introduction to programming using Python. It uses coding-competition challenges to teach you the mechanics of coding and how to think like a savvy programmer. Computers are capable of solving almost any problem when given the right instructions. That’s where programming comes in. This beginner’s book will have you writing Python programs right away. You’ll solve interesting problems drawn from real coding competitions and build your programming skills as you go. Every chapter presents problems from coding challenge websites, where online judges test your solutions and provide targeted feedback. As you practice using core Python features, functions, and techniques, you’ll develop a clear understanding of data structures, algorithms, and other programming basics. Bonus exercises invite you to explore new concepts on your own, and multiple-choice questions encourage you to think about how each piece of code works. You’ll learn how to: Run Python code, work with strings, and use variables Write programs that make decisions Make code more efficient with while and for loops Use Python sets, lists, and dictionaries to organize, sort, and search data Design programs using functions and top-down design Create complete-search algorithms and use Big O notation to design more efficient code By the end of the book, you’ll not only be proficient in Python, but you’ll also understand how to think through problems and tackle them with code. Programming languages come and go, but this book gives you the lasting foundation you need to start thinking like a programmer.
Author: Phil Winder Ph.D.
Publisher: "O'Reilly Media, Inc."
ISBN: 1492072346
Category : Computers
Languages : en
Pages : 517
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Book Description
Reinforcement learning (RL) will deliver one of the biggest breakthroughs in AI over the next decade, enabling algorithms to learn from their environment to achieve arbitrary goals. This exciting development avoids constraints found in traditional machine learning (ML) algorithms. This practical book shows data science and AI professionals how to learn by reinforcement and enable a machine to learn by itself. Author Phil Winder of Winder Research covers everything from basic building blocks to state-of-the-art practices. You'll explore the current state of RL, focus on industrial applications, learn numerous algorithms, and benefit from dedicated chapters on deploying RL solutions to production. This is no cookbook; doesn't shy away from math and expects familiarity with ML. Learn what RL is and how the algorithms help solve problems Become grounded in RL fundamentals including Markov decision processes, dynamic programming, and temporal difference learning Dive deep into a range of value and policy gradient methods Apply advanced RL solutions such as meta learning, hierarchical learning, multi-agent, and imitation learning Understand cutting-edge deep RL algorithms including Rainbow, PPO, TD3, SAC, and more Get practical examples through the accompanying website
