Deep learning for dummies:
Gespeichert in:
| Beteiligte Personen: | , |
|---|---|
| Format: | Buch |
| Sprache: | Englisch |
| Veröffentlicht: |
Hoboken, New Jersey
John Wiley & Sons
[2019]
|
| Schriftenreihe: | For dummies
Learning made easy |
| Schlagwörter: | |
| Links: | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=031419310&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| Beschreibung: | Index. Auf dem Cover: "Learn how deep learning is an essential technology, experiment with deep learning in a Python environment, see examples of major deep learning application types" |
| Umfang: | xi, 350 Seiten |
| ISBN: | 9781119543046 |
Internformat
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| 505 | 8 | |a Introduction -- Discovering deep learning -- Considering deep learning basics -- Interacting with deep learning -- The part of tens | |
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Table of Contents INTRODUCTION. 1 About This Book. 1 Foolish Assumptions.2 Icons Used in This Book. 3 Beyond the Book. 4 Where to Go from Here. 5 PART 1: DISCOVERING DEEP LEARNING.7 chapter i: Introducing Deep Learning. 9 Defining What Deep Learning Means.10 Starting from Artificial Intelligence.10 Considering the role of Al.12 Focusing on machine learning. 15 Moving from machine learning to deep learning. 16 Using Deep Learning in the Real World.18 Understanding the concept of learning. 18 Performing deep learning tasks. 19 Employing deep learning in applications. 19 Considering the Deep Learning
Programming Environment.19 Overcoming Deep Learning Hype. 22 Discovering the start-up ecosystem.22 Knowing when not to use deep learning. 22 CHAPTER 2: Introducing the Machine Learning Principles.25 Defining Machine Learning. 26 Understanding how machine learning works.26 Understanding that it's pure math.27 Learning by different strategies. 28 Training, validating, and testing data.30 Looking for generalization.31 Getting to know the limits of bias. 32 Keeping model complexity in mind.33 Considering the Many Different Roads to Learning.33 Understanding there is no free lunch.34 Discovering the five main approaches.34 Delving into some different approaches. 36 Awaiting the next breakthrough. 40 Pondering the True Uses of Machine Learning. 40 Understanding machine learning
benefits. 41 Discovering machine teaming limits.43 Table of Contents V
CHAPTER 3: Getting and Using Python.45 Working with Python in this Book.46 Obtaining Your Copy of Anaconda.46 Getting Continuum Analytics Anaconda. 47 Installing Anaconda on Linux. 47 Installing Anaconda on MacOS. 48 Installing Anaconda on Windows.49 Downloading the Datasets and Example Code.54 Using jupyter Notebook.54 Defining the code repository. 56 Getting and using datasets. 61 Creating the Application.62 Understanding celis.62 Adding documentation cells. 63 Using other cell types.64 Understanding the Use of Indentation. 65 Adding Comments. 66 Understanding
comments. 67 Using comments to leave yourself reminders.68 Using comments to keep code from executing.69 Getting Help with the Python Language. 69 Working in the Cloud. 70 Using the Kaggle datasets and kernels. 70 Using the Google Colaboratory. 70 CHAPTER 4: Leveraging a Deep Learning Framework. 73 Presenting Frameworks. 74 Defining the differences.74 Explaining the popularity of frameworks. 75 Defining the deep learning framework. 77 Choosing a particular framework.78 Working with Low-End Frameworks. 79 Caffe2.79 Chaîner.80 PyTorch. 80 MXNet.81
Microsoft Cognitive Toolkit/CNTK.82 Understanding TensorFlow.82 Grasping why TensorFlow is so good.82 Making TensorFlow easier by using TFLearn. 84 Using Keras as the best simplifier. 85 Getting your copy of TensorFlow and Keras. 86 Fixing the C++ build tools error in Windows. 88 Accessing your new environment in Notebook.89 ѴІ Deep Learning For Dummies
PART 2: CONSIDERING DEEP LEARNING BASICS. 91 chapter s: Reviewing Matrix Math and Optimization.эз Revealing the Math You Really Need. 94 Working with data. 94 Creating and operating with a matrix.95 Understanding Scalar, Vector, and Matrix Operations. 96 Creating a matrix. 97 Performing matrix multiplication.99 Executing advanced matrix operations.100 Extending analysis to tensors. 102 Using vectorization effectively.104 Interpreting Learning as Optimization. 105 Exploring cost functions. 105 Descending the error curve.106 Learning the right direction.107 Updating. 109 CHAPTER 6: Laying Linear Regression Foundations.in Combining Variables.112 Working through simple linear
regression. 112 Advancing to multiple linear regression. 113 Including gradient descent.115 Seeing linear regression in action. 116 Mixing Variable Types.117 Modeling the responses. 117 Modeling the features. 118 Dealing with complex relations. 119 Switching to Probabilities. 121 Specifying a binary response. 121 Transforming numeric estimates into probabilities. 122 Guessing the Right Features.124 Defining the outcome of incompatible features.124 Solving overfitting using selection and regularization.125 Learning One Example ataTime. 127 Using gradient descent. 127 Understanding how SGD is different.127 CHAPTER 7: Introducing Neural Networks. 131 Discovering the Incredible
Perceptron.132 Understanding perceptron functionality. 132 Touching the nonseparability limit. 134 Hitting Complexity with Neural Networks.136 Considering the neuron. 136 Pushing data with feed-forward. 138 Table of Contents ѴІІ
Going even deeper into the rabbit hole.140 Using backpropagation to adjust learning.143 Struggling with Overfitting. 146 Understanding the problem.146 Openingthe black box. 146 CHAPTER 8: Building a Basic Neural Network. 149 Understanding Neural Networks.150 Defining the basic architecture.151 Documenting the essential modules. 153 Solving a simple problem. 155 Looking Underthe Hood of Neural Networks. 158 Choosing the right activation function.158 Relying on a smart optimizer. 160 Setting a working learning rate.161 CHAPTER 9: Moving to Deep Learning.16З Seeing Data Everywhere. 164 Considering the effects of structure. 164 Understanding Moore's implications. 165 Considering what Moore's Law
changes.166 Discovering the Benefits of Additional Data. 167 Defining the ramifications of data.168 Considering data timeliness and quality.168 Improving Processing Speed.169 Leveraging powerful hardware. 170 Making other investments. 170 Explaining Deep Learning Differences from Other Forms of Al.171 Adding more layers. 172 Changing the activations.174 Adding regularization by dropout.175 Finding Even Smarter Solutions. 176 Using online learning. 176 Transferring learning. 177 Learning end to end. 177 CHAPTER 10: Explaining Convolutional Neural Networks.179 Beginning the CNN Tour with Character Recognition. 180 Understanding image basics. 180 Explaining How Convolutions
Work. 183 Understanding convolutions. 183 Simplifying the use of pooling. 187 Describing the LeNet architecture.188 ѴІІІ Deep Learning For Dummies
Detecting Edges and Shapes from Images. 193 Visualizing convolutions. 194 Unveiling successful architectures. 196 Discussing transfer learning. 197 CHAPTER 11: Introducing Recurrent Neural Networks. 201 Introducing Recurrent Networks.202 Modeling sequences using memory. 202 Recognizing and translating speech. 20Д Placing the correct caption on pictures.206 Explaining Long Short-Term Memory. 207 Defining memory differences.208 Walking through the LSTM architecture.209 Discovering interesting variants. 211 Getting the necessary attention. 212 PART 3: INTERACTING WITH DEEP LEARNING. 215 CHAPTER 12: Performing Image Classification. 217 Using Image Classification Challenges. 218 Delving into ImageNet and MS COCO.219 Learning the magic of data augmentation. 221 Distinguishing
Traffic Signs. 223 Preparing image data.224 Running a classification task.228 CHAPTER 13: Learning Advanced CNNs.233 Distinguishing Classification Tasks. 234 Performing localization. 235 Classifying multiple objects.235 Annotating multiple objects in images.237 Segmenting images.237 Perceiving Objects in Their Surroundings.239 Discovering how RetinaNet works. 239 Using the Keras-RetinaNet code. 241 Overcoming Adversarial Attacks on Deep Learning Applications . .245 Tricking pixels. 246 Hacking with stickers and other artifacts.248 CHAPTCF 14.’ Working on Language Processing.251 Processing Language.252 Defining understanding as tokenization. 253 Putting all the documents into a
bag.254 Memorizing Sequences that Matter. 257 Understanding semantics by word embeddings.257 Using Al for Sentiment Analysis.261 TabfeofContents ІХ
CHAPTER 15: Generating Music and Visual Art.269 Learning to imitate Art and Life. 270 Transferring an artistic style. 271 Reducing the problem to statistics.272 Understanding that deep learning doesn't create. 274 Mimicking an Artist. 274 Defining a new piece based on a single artist. 274 Combining styles to create new art. 276 Visualizing how neural networks dream. 276 Using a network to compose music.277 CHAPTER 16: Building Generative Adversarial Networks.279 Making Networks Compete.280 Finding the key in the competition.280 Achieving more realistic results. 282 Considering a Growing Field.289 inventing realistic pictures of celebrities. 289 Enhancing details and image translation. 290 CHAPTER 17: Playing with Deep Reinforcement Learning.293 Playing a Game with Neural
Networks.294 Introducing reinforcement learning.294 Simulating game environments. 296 Presenting Q-learning. 299 Explaining Alpha-Go.302 Determining if you're going to win.303 Applying self-learning at scale. 305 PART 4; THE PART OF TENS.307 CHAPTER 18: Ten Applications that Require Deep Learning. зоѳ Restoring Color to Black-and-White Videos and Pictures.310 Approximating Person Poses in Real Time. 310 Performing Real-Time Behavior Analysis.311 Translating Languages. 312 Estimating Solar Savings Potential. 312 Beating People at Computer Games. 313 Generating Voices. 314 Predicting Demographics. 314 Creating Art from Real-World Pictures. 315 Forecasting Natural
Catastrophes.316 X Deep Learning For Dummies
cHAPTţRi9:Тєп Must-Have Deep LearmngTools. 317 Compiling Math Expressions Using Theano. 317 Augmenting TensorFiow Using Keras. 318 Dynamically Computing Graphs with Chaîner. 319 Creating a MATLAB-Like Environment with Torch. 319 Performing Tasks Dynamically with PyTorch. 320 Accelerating Deep Learning Research Using CUDA.321 Supporting Business Needs with Deeplearning4j. 323 Mining Data Using Neural Designer. 323 Training Algorithms Using Microsoft Cognitive Toolkit (CNTK). 324 Exploiting Full GPU Capability Using MXNet.325 CHAPTER 20: Ten Types of Occupations that Use Deep Learning.327 Managing People. 327 Improving Medicine. 328 Developing New Devices.329 Providing Customer Support. 329 Seeing Data in New Ways.330 Performing Analysis Faster. 331 Creating a
Better Work Environment. 331 Researching Obscure or Detailed Information. 333 Designing Buildings. 333 Enhancing Safety. 334 INDEX . 335 Table of Contents xi |
| any_adam_object | 1 |
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| discipline | Informatik |
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| id | DE-604.BV046037560 |
| illustrated | Not Illustrated |
| indexdate | 2025-02-13T07:00:25Z |
| institution | BVB |
| isbn | 9781119543046 |
| language | English |
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| publishDate | 2019 |
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| publisher | John Wiley & Sons |
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| series2 | For dummies Learning made easy |
| spellingShingle | Mueller, John Paul 1958- Massaron, Luca Deep learning for dummies Introduction -- Discovering deep learning -- Considering deep learning basics -- Interacting with deep learning -- The part of tens Machine learning Artificial intelligence Deep Learning (DE-588)1135597375 gnd Python Programmiersprache (DE-588)4434275-5 gnd |
| subject_GND | (DE-588)1135597375 (DE-588)4434275-5 |
| title | Deep learning for dummies |
| title_auth | Deep learning for dummies |
| title_exact_search | Deep learning for dummies |
| title_full | Deep learning for dummies by John Paul Mueller and Luca Massaron |
| title_fullStr | Deep learning for dummies by John Paul Mueller and Luca Massaron |
| title_full_unstemmed | Deep learning for dummies by John Paul Mueller and Luca Massaron |
| title_short | Deep learning for dummies |
| title_sort | deep learning for dummies |
| topic | Machine learning Artificial intelligence Deep Learning (DE-588)1135597375 gnd Python Programmiersprache (DE-588)4434275-5 gnd |
| topic_facet | Machine learning Artificial intelligence Deep Learning Python Programmiersprache |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=031419310&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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