Deep Learning Illustrated

In Deep Learning Illustrated, three world-class instructors and practitioners present a uniquely visual, intuitive, and accessible high-level introduction to the techniques and applications of deep learning.

Deep Learning Illustrated

Deep learning is one of today's hottest fields. This approach to machine learning is achieving breakthrough results in some of today's highest profile applications, in organizations ranging from Google to Tesla, Facebook to Apple. Thousands of technical professionals and students want to start leveraging its power, but previous books on deep learning have often been non-intuitive, inaccessible, and dry. In Deep Learning Illustrated, three world-class instructors and practitioners present a uniquely visual, intuitive, and accessible high-level introduction to the techniques and applications of deep learning. Packed with vibrant, full-colour illustrations, it abstracts away much of the complexity of building deep learning models, making the field more fun to learn, and accessible to a far wider audience. Part I's high-level overview explains what Deep Learning is, why it has become so ubiquitous, and how it relates to concepts and terminology such as Artificial Intelligence, Machine Learning, Artificial Neural Networks, and Reinforcement Learning. These opening chapters are replete with vivid illustrations, easy-to-grasp analogies, and character-focused narratives. Building on this foundation, the authors then offer a practical reference and tutorial for applying a wide spectrum of proven deep learning techniques. Essential theory is covered with as little mathematics as possible, and illuminated with hands-on Python code. Theory is supported with practical "run-throughs" available in accompanying Jupyter notebooks, delivering a pragmatic understanding of all major deep learning approaches and their applications: machine vision, natural language processing, image generation, and videogaming.

Deep Learning with Python Illustrated Guide for Beginners and Intermediates

Providing a foundation in deep learning, this book is aimed at beginners and intermediate deep learning experts, and covers important concepts and problems in the field.

Deep Learning with Python Illustrated Guide for Beginners and Intermediates

Providing a foundation in deep learning, this book is aimed at beginners and intermediate deep learning experts, and covers important concepts and problems in the field. Each chapter begins with theory and follows with a practical implementation in Python. It covers a variety of problems, such as digital recognition, image reclassification, stock market prediction, and NLP problems such as text classification and sentimental analysis. Datasets are provided for each problem so that readers can solve using different algorithms, such as with convolutional neural networks, or with recurrent neural networks.

Python Machine Learning Illustrated Guide For Beginners Intermediates

From here on I would suggest you to study machine learning on your own.
Following are some of the very good resources: 1. For Python: https://www.python
.org/ 2. For Machine Learning: http://scikit-learn.org/stable/ 3. For Deep ...

Python Machine Learning Illustrated Guide For Beginners   Intermediates

Python Machine Learning Illustrated Guide For Beginners & Intermediates Machines Can Learn ?! Automation and systematization is taking over the world. Slowly but surely we continuously see the rapid expansion of artificial intelligence, self-check out cash registers, automated phone lines, people-less car-washes , etc. The world is changing, find out how python programming ties into machine learning so you don't miss out on this next big trend! This is your beginner's step by step guide with illustrated pictures! Let's face it, machine learning is here to stay for the foreseeable future and will impact the lives billions worldwide! Drastically changing the world we live in the most fundamental ways, from our perceptions, life-style, thinking and in other aspects as well. What You Will Learn Linear & Polynomial Regression Support Vector Machines Decision Trees Random Forest KNN Algorithm Naive Bayes Algorithm Unsupervised Learning Clustering Cross Validation Grid Search And, much, much more! If you want to learn more about python machine learning it is highly recommended you start from the ground up by using this book. Normally books on this subject matter are expensive! Why not start off by making a small and affordable investment with your illustrated beginners guide that walks you through python machine learning step by step Why choose this book? Addresses Fundamental Concepts Goes Straight To The Point, uNo fluff or Nonsense Practical Examples High Quality Diagrams "Noob friendly" (Good For Beginners & Intermediates) Contains Various Aspects of Machine Learning Endorses Learn "By Doing Approach" Concise And To The Point I been working tirelessly to provide you quality books at an affordable price. I believe this book will give you the confidence to tackle python machine learning at a fundamental level. What are you waiting for? Make the greatest investment in YOUR knowledge base right now. Buy your copy now!

Python for Probability Statistics and Machine Learning

This book, fully updated for Python version 3.6+, covers the key ideas that link probability, statistics, and machine learning illustrated using Python modules in these areas.

Python for Probability  Statistics  and Machine Learning

This book, fully updated for Python version 3.6+, covers the key ideas that link probability, statistics, and machine learning illustrated using Python modules in these areas. All the figures and numerical results are reproducible using the Python codes provided. The author develops key intuitions in machine learning by working meaningful examples using multiple analytical methods and Python codes, thereby connecting theoretical concepts to concrete implementations. Detailed proofs for certain important results are also provided. Modern Python modules like Pandas, Sympy, Scikit-learn, Tensorflow, and Keras are applied to simulate and visualize important machine learning concepts like the bias/variance trade-off, cross-validation, and regularization. Many abstract mathematical ideas, such as convergence in probability theory, are developed and illustrated with numerical examples. This updated edition now includes the Fisher Exact Test and the Mann-Whitney-Wilcoxon Test. A new section on survival analysis has been included as well as substantial development of Generalized Linear Models. The new deep learning section for image processing includes an in-depth discussion of gradient descent methods that underpin all deep learning algorithms. As with the prior edition, there are new and updated *Programming Tips* that the illustrate effective Python modules and methods for scientific programming and machine learning. There are 445 run-able code blocks with corresponding outputs that have been tested for accuracy. Over 158 graphical visualizations (almost all generated using Python) illustrate the concepts that are developed both in code and in mathematics. We also discuss and use key Python modules such as Numpy, Scikit-learn, Sympy, Scipy, Lifelines, CvxPy, Theano, Matplotlib, Pandas, Tensorflow, Statsmodels, and Keras. This book is suitable for anyone with an undergraduate-level exposure to probability, statistics, or machine learning and with rudimentary knowledge of Python programming.

Python Machine Learning

This book introduces a broad range of topics in deep learning.Book DescriptionPython Machine Learning, is a comprehensive guide to machine learning and deep learning with Python.

Python Machine Learning

Have you always wanted to learn deep learning but are afraid it'll be too difficult for you? This book is for you.Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning.Book DescriptionPython Machine Learning, is a comprehensive guide to machine learning and deep learning with Python. It acts as both a step-by-step tutorial, and a reference you'll keep coming back to as you build your machine learning systems.Packed with clear explanations, visualizations, and working examples, the book covers most of the essential machine learning techniques in depth. While some books teach you only to follow instructions, with this machine learning book, this tutorial book teaches the principles behind machine learning, allowing you to build models and applications for yourself. Updated for TensorFlow, skit-learn, Keras, and theano, this edition introduces readers to its new Keras API features, as well as the latest additions to scikit-learn. It's also expanded to cover cutting-edge reinforcement learning techniques based on deep learning, as well as an introduction to GANs. Finally, this book also explores analysis by giving some examples, helping you learn how to use machine learning algorithms to classify or predict documents output.This book is your companion to machine learning with Python, whether you're a Python developer new to machine learning or want to deepen your knowledge of the latest developments.What you will learn-Master the frameworks, models, and techniques that enable machines to 'learn' from data-Use scikit-learn for machine learning and TensorFlow for deep learning-Apply machine learning to classification, predict predict customer churning, and more-Build and train neural networks, GANs, CNN, and other models-Discover best practices for evaluating and tuning models-Predict target outcomes using optimization algorithm such as Gradient Descent algorithm analysis-Overcome challenges in deep learning algorithms by using dropout, regulation-Who This Book Is ForIf you know some Python and you want to use machine learning and deep learning, pick up this book. Whether you want to start from scratch or extend your machine learning knowledge, this is an essential resource. Written for developers and data scientists who want to create practical machine learning and deep learning code, this book is ideal for anyone who wants to teach computers how to learn from data.Table of Contents1.Giving Computers the Ability to Learn from Data2.Training Simple ML Algorithms for Classification3.ML Classifiers Using scikit-learn4.Building Good Training Datasets - Data Preprocessing5.Compressing Data via Dimensionality Reduction6.Best Practices for Model Evaluation and Hyperparameter Tuning7.Combining Different Models for Ensemble Learning8.Predicting Continuous Target Variables with supversized learning 9.Implementing Multilayer Artificial Neural Networks10.Modeling Sequential Data Using Recurrent Neural Networks11.GANs for Synthesizing New Data...and so much more....In every chapter, you can edit the examples online

The Use of Secular Learning Illustrated by the Examples of S Luke the Evangelist and Daniel the Prophet Two Sermons

Egyptians , the learning of the Chaldeans , the classic lore of Greece and Rome ,
all is part of the Church's inheritance a new Canaan , won by the all ... We must
dig deep , and lay a solid foundation , if we desire to rear a substantial structure .

The Use of Secular Learning  Illustrated by the Examples of S  Luke the Evangelist and Daniel the Prophet  Two Sermons


Cloud Computing for Machine Learning and Cognitive Applications

The book will also be very useful as a reference for professionals who want to work in cloud computing and data science.

Cloud Computing for Machine Learning and Cognitive Applications

The first textbook to teach students how to build data analytic solutions on large data sets using cloud-based technologies.

Deep Learning

The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning.

Deep Learning

An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives. “Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.” —Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.

Deep Learning with R

Summary Deep Learning with R introduces the world of deep learning using the powerful Keras library and its R language interface. The book builds your understanding of deep learning through intuitive explanations and practical examples.

Deep Learning with R

Introduces deep learning systems using the powerful Keras library and its R language interface. The book builds your understanding of deep learning through intuitive explanations and practical examples.

Deep Learning Techniques for Music Generation

Reinforcement learning (RL) may appear at first glance to be outside of our
interest in deep learning architectures, as it ... Let us start with a reminder of the
basic concepts of reinforcement learning, illustrated in Figure 5.40 • an agent
within an ...

Deep Learning Techniques for Music Generation

This book is a survey and analysis of how deep learning can be used to generate musical content. The authors offer a comprehensive presentation of the foundations of deep learning techniques for music generation. They also develop a conceptual framework used to classify and analyze various types of architecture, encoding models, generation strategies, and ways to control the generation. The five dimensions of this framework are: objective (the kind of musical content to be generated, e.g., melody, accompaniment); representation (the musical elements to be considered and how to encode them, e.g., chord, silence, piano roll, one-hot encoding); architecture (the structure organizing neurons, their connexions, and the flow of their activations, e.g., feedforward, recurrent, variational autoencoder); challenge (the desired properties and issues, e.g., variability, incrementality, adaptability); and strategy (the way to model and control the process of generation, e.g., single-step feedforward, iterative feedforward, decoder feedforward, sampling). To illustrate the possible design decisions and to allow comparison and correlation analysis they analyze and classify more than 40 systems, and they discuss important open challenges such as interactivity, originality, and structure. The authors have extensive knowledge and experience in all related research, technical, performance, and business aspects. The book is suitable for students, practitioners, and researchers in the artificial intelligence, machine learning, and music creation domains. The reader does not require any prior knowledge about artificial neural networks, deep learning, or computer music. The text is fully supported with a comprehensive table of acronyms, bibliography, glossary, and index, and supplementary material is available from the authors' website.