Obviously Awesome

DL with Python & DL with PyTorch - book notes

Deep Learning with Python (2nd ed) - Chollet
Deep Learning with PyTorch (1st ed) - multiple
  • Python 2nd ed:
    • 1) Definition
    • 2) Math
      • Neural nets - first look
      • Data representation
      • Tensor ops
      • Gradients
      • Review
    • 3) Keras & Tensorflow
      • Keras
      • Tensorflow
      • History
      • Workspace setup
      • TF first steps
      • Neural net anatomy
    • 4) Classification basics
      • Binary classification (movie reviews)
      • Multiclass classification (newswires)
      • Regression (house prices)
    • 5) ML fundamentals
      • Generalization
      • Model evaluation
      • Improving model fit
      • Improving generalization
    • 6) Universal workflow
      • Tasks
      • Models
      • Deployment
    • 7) Keras deep dive
      • Workflows
      • How to build models
      • Training & evaluation loops - built-in
      • Training & evaluation loops - custom
    • 8) DL for CV (intro)
      • Convnets
      • Training from scratch - small dataset
      • Leveraging pretrained models
    • 9) DL for CV (advanced)
      • Three essential tasks
      • Image segmentation
      • Modern convnets
      • Interpreting what convnets learn
    • 10) DL for time series
      • Tasks
      • Example - temp forecasting
      • Recurrent networks (RNNs)
      • RNNs - advanced usage
    • 11) DL for text
      • NLP
      • Data prep
      • Word representation - two methods
      • Transformer
      • Sequence-to-sequence learning
    • 12) Generative DL
      • Text generation
        • History
        • Sequence data
        • Sampling
        • Keras implementation
        • Text-generation callback
        • Wrapup
      • Deep Dream
        • Keras implementation
        • Wrapup
      • Style Transfer
        • Content loss
        • Style loss
        • Keras implementation
        • Wrapup
      • Variational Autoencoders - Generating images
        • Sampling from latent image spaces
        • Concept vectors - image editing
        • VAEs
        • Keras implementation
        • Wrapup
      • GANs (generative adversarial nets)
        • Schematic
        • Bag of Tricks
        • CelebA dataset
        • Discriminator
        • Generator
        • Adversarial net
        • Wrapup
    • 13) Best practices
      • Parameter optimization
      • Model ensembles
      • Mixed-precision math
      • Multi-GPU training
      • TPU training
    • 14) Conclusions
      • Key concepts
      • Limitations
      • Generality
      • Implementing intelligence
      • Future
      • Staying up to date
  • PyTorch:
    • Core
      • 1) Intro
        • Deep Learning
        • PyTorch
        • Why PyTorch
        • PyTorch for DL
        • HW/SW requirements
      • 2) Pretrained Nets
        • Net that recognizes image subjects
        • Fake it till you make it
        • Scene descriptions
        • Torch Hub
      • 3) Tensors
        • Floating point
        • Intro
        • Indexing
        • Named
        • Element types
        • API
        • Storage
        • Size, Offset, Stride
        • Moving to GPU
        • NumPy
        • Generalized tensors
        • Serializing
      • 4) Data Representation
        • Images
        • 3D (volumetric) Images
        • Tablular data
        • Time series
        • Text
      • 5) Learning Mechanics
        • Modeling lessons
        • Parameter estimation
        • Loss
        • Gradients
        • PyTorch autograd
      • 6) Fitting and Nets
        • Neurons
        • PyTorch nn module
        • Neural net
      • 7) Learning from Images
        • Image dataset
        • Distinguishing birds from airplanes
      • 8) Convolutions
        • Purpose
        • Convos in action
        • Subclassing the nn module
        • Training
        • Model design
    • Learning from Images
      • 9) Example: Cancer diagnosis
        • Intro
        • Setup
        • What is a CT Scan
        • Project (E2E)
      • 10) Combining Datasets
        • Raw CT Files
        • Parsing LUNA data
        • Loading Individual CT Scans
        • Patient Coordinates
        • Dataset Implementation
      • 11) Tumor Detection
        • Foundational Model
        • Main (entry point)
        • Setup
        • Net design (1st pass)
        • Training & validation
        • Metrics
        • Training script
        • Evaluation
        • Visualization w/ Tensorboard
        • Problem solving
      • 12) Metrics & Augmentation
        • High level plan
        • False positives & false negatives
        • Graphing
        • “Ideal” datasets
        • Overfitting
        • Overfitting & data augmentation
      • 13) Segmentation
        • Adding a 2nd model
        • Types of segmentation
        • Semantic segmentation (per-pixel classification)
        • Updating the model
        • Updating the dataset
        • Updating the script
        • Results
      • 14) E2E Nodule Analysis
        • Finish Line
        • Validation set independence
        • Bridging CT segmentation & nodule candidate classification
        • Quantitative validation
        • Predicting malignancy
        • What we see
        • What’s next?
    • Deployment
      • 15) To Production
        • Serving PyTorch models
        • Exporting
        • PyTorch JIT
        • LibTorch
        • Mobile
        • Enterprise Model Serving
        • Conclusion