Author:
Publisher:
ISBN:
Category : Fruit
Languages : en
Pages : 1162

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Author: University of California Agricultural Extension Service
Publisher:
ISBN:
Category : Agriculture
Languages : en
Pages : 298

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Author: Rakefet Hadar
Publisher: Rowman & Littlefield
ISBN: 081177015X
Category : Self-Help
Languages : en
Pages : 177

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Book Description
Express yourself in a visual journal! With the ideas in this book, you will learn to create mixed media pages that express your soul and create a path to healing, internal freedom, and the sparking of passion. “Visual Journey Journaling” is an innovative artistic method taught by Rakefet Hadar and made up of seven elements: Intention, Magical Coincidence, Background, Images, Lines, Color, and Text. Visual Journey Journaling invites you to a fascinating world where you connect with your hidden inner artist to create "soul pages" using simple techniques and subtle guidelines to take a look inside yourself. Rakefet has taught these methods for many years, guiding even inexperienced artists to find and express the stories within themselves. In the first chapter of the book you will learn how to master the seven elements in your journal. There are many fun exercises and a step-by-step tutorial of how to start a simple journal. Next you will learn how to make a soul page with the seven elements. You will explore a variety of materials and how to work with them to find and create your pages. You will learn to build your journal and how to bind it into a finished book. Throughout the book and in the final section, you'll see and find the meanings in Rakefet's stunning private art journal pages and read her stories behind them.

Author: Ian Goodfellow
Publisher: MIT Press
ISBN: 0262337371
Category : Computers
Languages : en
Pages : 801

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Book Description
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.

Author:
Publisher:
ISBN:
Category : Agricultural
Languages : en
Pages : 492

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Author: Michael Bernico
Publisher: Packt Publishing Ltd
ISBN: 1788838912
Category : Computers
Languages : en
Pages : 261

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Book Description
Dive deeper into neural networks and get your models trained, optimized with this quick reference guide Key Features A quick reference to all important deep learning concepts and their implementations Essential tips, tricks, and hacks to train a variety of deep learning models such as CNNs, RNNs, LSTMs, and more Supplemented with essential mathematics and theory, every chapter provides best practices and safe choices for training and fine-tuning your models in Keras and Tensorflow. Book Description Deep learning has become an essential necessity to enter the world of artificial intelligence. With this book deep learning techniques will become more accessible, practical, and relevant to practicing data scientists. It moves deep learning from academia to the real world through practical examples. You will learn how Tensor Board is used to monitor the training of deep neural networks and solve binary classification problems using deep learning. Readers will then learn to optimize hyperparameters in their deep learning models. The book then takes the readers through the practical implementation of training CNN's, RNN's, and LSTM's with word embeddings and seq2seq models from scratch. Later the book explores advanced topics such as Deep Q Network to solve an autonomous agent problem and how to use two adversarial networks to generate artificial images that appear real. For implementation purposes, we look at popular Python-based deep learning frameworks such as Keras and Tensorflow, Each chapter provides best practices and safe choices to help readers make the right decision while training deep neural networks. By the end of this book, you will be able to solve real-world problems quickly with deep neural networks. What you will learn Solve regression and classification challenges with TensorFlow and Keras Learn to use Tensor Board for monitoring neural networks and its training Optimize hyperparameters and safe choices/best practices Build CNN's, RNN's, and LSTM's and using word embedding from scratch Build and train seq2seq models for machine translation and chat applications. Understanding Deep Q networks and how to use one to solve an autonomous agent problem. Explore Deep Q Network and address autonomous agent challenges. Who this book is for If you are a Data Scientist or a Machine Learning expert, then this book is a very useful read in training your advanced machine learning and deep learning models. You can also refer this book if you are stuck in-between the neural network modeling and need immediate assistance in getting accomplishing the task smoothly. Some prior knowledge of Python and tight hold on the basics of machine learning is required.

Author: Patricia Gaydos
Publisher: Patricia Gaydos
ISBN:
Category : Photography
Languages : en
Pages : 53

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Book Description
The techniques and tips presented in Adobe Photoshop Layers can be applied using Adobe Photohsop versions CS2 thru CS6. This book covers everything you need to know about layers. Each technique includes step-by-step instructions and a practice exercise which can be downloaded at htpps://patriciagaydos.com. I am a past Adobe Certified Expert and instructor and use this book in my Layers workshops. My students praise the easy to follow, step-by-step instructions. I recommend this book to beginners and seasoned users alike.

Author:
Publisher:
ISBN:
Category : Chemistry
Languages : en
Pages : 842

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Author: Ronald T. Kneusel
Publisher: No Starch Press
ISBN: 1718500742
Category : Computers
Languages : en
Pages : 463

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Book Description
Practical Deep Learning teaches total beginners how to build the datasets and models needed to train neural networks for your own DL projects. If you’ve been curious about machine learning but didn’t know where to start, this is the book you’ve been waiting for. Focusing on the subfield of machine learning known as deep learning, it explains core concepts and gives you the foundation you need to start building your own models. Rather than simply outlining recipes for using existing toolkits, Practical Deep Learning teaches you the why of deep learning and will inspire you to explore further. All you need is basic familiarity with computer programming and high school math—the book will cover the rest. After an introduction to Python, you’ll move through key topics like how to build a good training dataset, work with the scikit-learn and Keras libraries, and evaluate your models’ performance. You’ll also learn: How to use classic machine learning models like k-Nearest Neighbors, Random Forests, and Support Vector Machines How neural networks work and how they’re trained How to use convolutional neural networks How to develop a successful deep learning model from scratch You’ll conduct experiments along the way, building to a final case study that incorporates everything you’ve learned. The perfect introduction to this dynamic, ever-expanding field, Practical Deep Learning will give you the skills and confidence to dive into your own machine learning projects.

Author:
Publisher:
ISBN:
Category : Agricultural extension work
Languages : en
Pages : 458

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Book Description