Neural networks from scratch in Python:
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
[Pahrump, NV]
Harrison Kinsley
[2020]
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | 666 Seiten Illustrationen, Diagramme |
Internformat
MARC
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Datensatz im Suchindex
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Table of Contents Acknowledgements 2 Copyright 8 License for Code 9 Readme 10 Introducing Neural Networks 11 A Brief History.12 What is a Neural Network?.13 Coding Our First Neurons 25 A Single Neuron. 26 A Layer of Neurons. 30 Tensors, Arrays and Vectors.34 Dot Product and Vector Addition.38 A Single Neuron with NumPy. 40 A Batch of Data. 44 Matrix Product. 47 Transposition for the Matrix Product.50 A Layer of Neurons Batch of Data w/ NumPy.54 Adding Layers 59 Training Data. 62 Dense Layer Class. 66 Full code up to this
point:. 70 4 I Neural Networks from Scratch
Activation Functions 72 The Step Activation Function. 73 The Linear Activation Function. 74 The Sigmoid Activation Function. 75 The Rectified Linear Activation Function. 76 Why Use Activation Functions?. 77 Linear Activation in the Hidden Layers. 79 ReLU Activation in a Pair of Neurons. 81 ReLU Activation in the Hidden Layers. 85 ReLU Activation Function Code. 95 The Softmax Activation Function. 98 Full code up to this point:.108 Calculating Network Error with Loss 111 Categorical Cross-Entropy Loss.112 The Categorical Cross-Entropy Loss Class.123 Combining everything up to this point:.125 Accuracy
Calculation.129 Introducing Optimization 131 Full code up to this point:.136 Derivatives 139 The Impact of a Parameter on the Output.140 The Slope.142 The Numerical Derivative.146 The Analytical Derivative.154 Summary. 164 Gradients, Partial Derivatives, and the Chain Rule 166 The Partial Derivative.167 The Partial Derivative of a Sum.168 The Partial Derivative of Multiplication.170 The Partial Derivative of Max.172 The Gradient. 173 The Chain Rule. 174
Summary.178 Backpropagation 180 Categorical Cross-Entropy loss derivative. 215 Neural Networks from Scratch | 5
Categorical Cross-Entropy loss derivative code implementation.218 Softmax activation derivative. 220 Softmax activation derivative code implementation.226 Common Categorical Cross-Entropy loss and Softmax activation derivative 230 Common Categorical Cross-Entropy loss and Softmax activation derivative code implementation. 234 Full code up to this point:. 243 Optimizers 249 Stochastic Gradient Descent (SGD).250 Learning Rate. 257 Learning Rate Decay. 274 Stochastic Gradient Descent with Momentum.283 AdaGrad. 293 RMSProp. 298 Adam. 304 Full code up to this point:.309 Testing with Out-of-Sample Data 321 Validation Data 328
Training Dataset 332 LÍ and L2 Regularization 335 Forward Pass. 336 Backward pass. 340 Dropout 361 Forward Pass. 362 Backward Pass. 369 The Code. 370 Binary Logistic Regression 388 Sigmoid Activation Function.389 Sigmoid Function Derivative.391 Sigmoid Function Code.395 Binary Cross-Entropy Loss.396 Binary Cross-Entropy Loss Derivative.398 Binary Cross-Entropy Code. 401 Implementing Binary Logistic Regression and Binary Cross-Entropy Loss .404 6 Į Neural Networks from Scratch
Full code up to this point:. 407 Regression 423 Linear Activation. 425 Mean Squared Error Loss. 426 Mean Squared Error Loss Derivative. 427 Mean Squared Error (MSE) Loss Code. 428 Mean Absolute Error Loss. 429 Mean Absolute Error Loss Derivative. 430 Mean Absolute Error Loss Code. 431 Accuracy in Regression. 432 Regression Model Training. 433 Full code up to this point:. 458 Model Object 475 Full code up to this point:. 512 A Real Dataset 532 Data preparation. 534 Data loading. 536 Data
preprocessing. 543 Data Shuffling. 546 Batches. 549 Training. 563 Full code up to now:. 570 Model Evaluation 594 Saving and Loading Models and Their Parameters 601 Retrieving Parameters. 601 Setting Parameters. 605 Saving Parameters. 609 Loading Parameters. 610 Saving the Model. 612 Loading the Model. 615 Prediction /Inference 617 Full code:. 633 Closing 661 Neural Networks from Scratch | 7 |
| adam_txt |
Table of Contents Acknowledgements 2 Copyright 8 License for Code 9 Readme 10 Introducing Neural Networks 11 A Brief History.12 What is a Neural Network?.13 Coding Our First Neurons 25 A Single Neuron. 26 A Layer of Neurons. 30 Tensors, Arrays and Vectors.34 Dot Product and Vector Addition.38 A Single Neuron with NumPy. 40 A Batch of Data. 44 Matrix Product. 47 Transposition for the Matrix Product.50 A Layer of Neurons Batch of Data w/ NumPy.54 Adding Layers 59 Training Data. 62 Dense Layer Class. 66 Full code up to this
point:. 70 4 I Neural Networks from Scratch
Activation Functions 72 The Step Activation Function. 73 The Linear Activation Function. 74 The Sigmoid Activation Function. 75 The Rectified Linear Activation Function. 76 Why Use Activation Functions?. 77 Linear Activation in the Hidden Layers. 79 ReLU Activation in a Pair of Neurons. 81 ReLU Activation in the Hidden Layers. 85 ReLU Activation Function Code. 95 The Softmax Activation Function. 98 Full code up to this point:.108 Calculating Network Error with Loss 111 Categorical Cross-Entropy Loss.112 The Categorical Cross-Entropy Loss Class.123 Combining everything up to this point:.125 Accuracy
Calculation.129 Introducing Optimization 131 Full code up to this point:.136 Derivatives 139 The Impact of a Parameter on the Output.140 The Slope.142 The Numerical Derivative.146 The Analytical Derivative.154 Summary. 164 Gradients, Partial Derivatives, and the Chain Rule 166 The Partial Derivative.167 The Partial Derivative of a Sum.168 The Partial Derivative of Multiplication.170 The Partial Derivative of Max.172 The Gradient. 173 The Chain Rule. 174
Summary.178 Backpropagation 180 Categorical Cross-Entropy loss derivative. 215 Neural Networks from Scratch | 5
Categorical Cross-Entropy loss derivative code implementation.218 Softmax activation derivative. 220 Softmax activation derivative code implementation.226 Common Categorical Cross-Entropy loss and Softmax activation derivative 230 Common Categorical Cross-Entropy loss and Softmax activation derivative code implementation. 234 Full code up to this point:. 243 Optimizers 249 Stochastic Gradient Descent (SGD).250 Learning Rate. 257 Learning Rate Decay. 274 Stochastic Gradient Descent with Momentum.283 AdaGrad. 293 RMSProp. 298 Adam. 304 Full code up to this point:.309 Testing with Out-of-Sample Data 321 Validation Data 328
Training Dataset 332 LÍ and L2 Regularization 335 Forward Pass. 336 Backward pass. 340 Dropout 361 Forward Pass. 362 Backward Pass. 369 The Code. 370 Binary Logistic Regression 388 Sigmoid Activation Function.389 Sigmoid Function Derivative.391 Sigmoid Function Code.395 Binary Cross-Entropy Loss.396 Binary Cross-Entropy Loss Derivative.398 Binary Cross-Entropy Code. 401 Implementing Binary Logistic Regression and Binary Cross-Entropy Loss .404 6 Į Neural Networks from Scratch
Full code up to this point:. 407 Regression 423 Linear Activation. 425 Mean Squared Error Loss. 426 Mean Squared Error Loss Derivative. 427 Mean Squared Error (MSE) Loss Code. 428 Mean Absolute Error Loss. 429 Mean Absolute Error Loss Derivative. 430 Mean Absolute Error Loss Code. 431 Accuracy in Regression. 432 Regression Model Training. 433 Full code up to this point:. 458 Model Object 475 Full code up to this point:. 512 A Real Dataset 532 Data preparation. 534 Data loading. 536 Data
preprocessing. 543 Data Shuffling. 546 Batches. 549 Training. 563 Full code up to now:. 570 Model Evaluation 594 Saving and Loading Models and Their Parameters 601 Retrieving Parameters. 601 Setting Parameters. 605 Saving Parameters. 609 Loading Parameters. 610 Saving the Model. 612 Loading the Model. 615 Prediction /Inference 617 Full code:. 633 Closing 661 Neural Networks from Scratch | 7 |
| any_adam_object | 1 |
| any_adam_object_boolean | 1 |
| author | Kinsley, Harrison Kukiela, Daniel |
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| indexdate | 2024-10-07T12:00:45Z |
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| language | English |
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| physical | 666 Seiten Illustrationen, Diagramme |
| publishDate | 2020 |
| publishDateSearch | 2020 |
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| publisher | Harrison Kinsley |
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| spelling | Kinsley, Harrison Verfasser (DE-588)1246983990 aut Neural networks from scratch in Python Harrison Kinsley & Daniel Kukieła Building neural networks in raw Python [Pahrump, NV] Harrison Kinsley [2020] © 2020 666 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier Python Programmiersprache (DE-588)4434275-5 gnd rswk-swf Neuronales Netz (DE-588)4226127-2 gnd rswk-swf Neuronales Netz (DE-588)4226127-2 s Python Programmiersprache (DE-588)4434275-5 s DE-604 Kukiela, Daniel Verfasser (DE-588)1246984547 aut Digitalisierung UB Passau - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032977075&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Kinsley, Harrison Kukiela, Daniel Neural networks from scratch in Python Python Programmiersprache (DE-588)4434275-5 gnd Neuronales Netz (DE-588)4226127-2 gnd |
| subject_GND | (DE-588)4434275-5 (DE-588)4226127-2 |
| title | Neural networks from scratch in Python |
| title_alt | Building neural networks in raw Python |
| title_auth | Neural networks from scratch in Python |
| title_exact_search | Neural networks from scratch in Python |
| title_exact_search_txtP | Neural networks from scratch in Python |
| title_full | Neural networks from scratch in Python Harrison Kinsley & Daniel Kukieła |
| title_fullStr | Neural networks from scratch in Python Harrison Kinsley & Daniel Kukieła |
| title_full_unstemmed | Neural networks from scratch in Python Harrison Kinsley & Daniel Kukieła |
| title_short | Neural networks from scratch in Python |
| title_sort | neural networks from scratch in python |
| topic | Python Programmiersprache (DE-588)4434275-5 gnd Neuronales Netz (DE-588)4226127-2 gnd |
| topic_facet | Python Programmiersprache Neuronales Netz |
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