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From Model to Production

来源 1 浏览 198 扫码分享 2021-03-31 08:21:32

From Model to Production

Let’s start with how you should frame your problem.

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04 Under the Hood: Training a Digit Classifier
- Pixels: The Foundations of Computer Vision
- Jargon Recap
- Questionnaire
- Sidebar: Tenacity and Deep Learning
- End sidebar
- First Try: Pixel Similarity
- Computing Metrics Using Broadcasting
- Stochastic Gradient Descent (SGD)
- The MNIST Loss Function
- Putting It All Together
- Adding a Nonlinearity
05 Image Classification
- From Dogs and Cats to Pet Breeds
- Presizing
- Cross-Entropy Loss
- Model Interpretation
- Improving Our Model
- Conclusion
- Questionnaire
18 CNN Interpretation with CAM
- CAM and Hooks
- Gradient CAM
- Conclusion
- Questionnaire
12 A Language Model from Scratch
- The Data
- Our First Language Model from Scratch
- Improving the RNN
- Multilayer RNNs
- LSTM
- Regularizing an LSTM
- Conclusion
- Questionnaire
10 NLP Deep Dive: RNNs
- Text Preprocessing
- Training a Text Classifier
- Disinformation and Language Models
- Conclusion
- Questionnaire
13 Convolutional Neural Networks
- The Magic of Convolutions
- Our First Convolutional Neural Network
- Color Images
- Improving Training Stability
- Conclusions
- Questionnaire
14 ResNets
- Going Back to Imagenette
- Building a Modern CNN: ResNet
- Conclusion
- Questionnaire
19 A fastai Learner from Scratch
- Data
- Module and Parameter
- Loss
- Learner
- Conclusion
- Questionnaire
16 The Training Process
- Establishing a Baseline
- Foundations of Deep Learning: Wrap up
- A Generic Optimizer
- Momentum
- RMSProp
- Adam
- Decoupled Weight Decay
- Callbacks
- Conclusion
- Questionnaire
07 Training a State-of-the-Art Model
- Imagenette
- Normalization
- Progressive Resizing
- Test Time Augmentation
- Mixup
- Label Smoothing
- Conclusion
- Questionnaire
01 Introduction
- Your Deep Learning Journey
- clean
09 Tabular Modeling Deep Dive
- Categorical Embeddings
- Questionnaire
- Beyond Deep Learning
- The Dataset
- Decision Trees
- Random Forests
- Model Interpretation
- Extrapolation and Neural Networks
- Ensembling
- Conclusion: Our Advice for Tabular Modeling
03 Data Ethics
- Key Examples for Data Ethics
- Integrating Machine Learning with Product Design
- Topics in Data Ethics
- Identifying and Addressing Ethical Issues
- Role of Policy
- Conclusion
- Questionnaire
- Deep Learning in Practice: That’s a Wrap!
app blog
app jupyter
11 Data Munging with fastai’s Mid-Level API
- Going Deeper into fastai’s Layered API
- TfmdLists and Datasets: Transformed Collections
- Applying the Mid-Level Data API: SiamesePair
- Conclusion
- Questionnaire
- Understanding fastai’s Applications: Wrap Up
20 conclusion
08 Collaborative Filtering Deep Dive
- A First Look at the Data
- Learning the Latent Factors
- Creating the DataLoaders
- Collaborative Filtering from Scratch
- Interpreting Embeddings and Biases
- Bootstrapping a Collaborative Filtering Model
- Deep Learning for Collaborative Filtering
- Conclusion
- Questionnaire
06 Other Computer Vision Problems
- Multi-Label Classification
- Regression
- Conclusion
- Questionnaire
17 A Neural Net from the Foundations
- Building a Neural Net Layer from Scratch
- The Forward and Backward Passes
- Conclusion
- Questionnaire
02 Production
- From Model to Production
  - The Practice of Deep Learning
  - Gathering Data
- clean
15 Application Architectures Deep Dive
- Computer Vision
- Natural Language Processing
- Tabular
- Wrapping Up Architectures
- Questionnaire
00 README

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