Key Lessons
- Map Image from one space to another space
- Vector mapped to output space
- Regression - Continuous Value
- All are types of Regression Problems
- Most work in ML is creating features
- Mapping function feature space to classification
- DL learns features from Data
- Key DL Components
- Architecture - Loss Function - Optimizer (part of learning process)
Building Blocks
- Activation Functions (Relu, Elu, SRelu, PreLU
- Convolutional Layers (Dimensionality vs complexity)
- Aggregation Layers (Pooling / Convolutions)
- Convolution Example
- Input X Kernel = Output
- Kernels are matrices
- Backprop apply gradient descent
- Weighted matrix multiplication is convolution
- Standard Convolution 3x3 kernel
- Dialted Convolution - Spacing between elements of kernel (Bring Context and Relationships)
- Strided Convolution (Apply convolution every X number of Pixels)
- Unpooling - Upsampling Image
- Residual Connections (apply a little + plus)
- Drop Out, Batch Norm (Form of Regularization)
- Classification (K Classes)
- Scale -> Depth
- Conv + Relu + Pooling
- Extract Features that scale
- Segmentation (Input -> Output Same cardinality)
- Unet Based Approach
- Vnet (Residual Model)
- DeepMedic (Downsample / Crop) + Merge Later
- HighResNet (Right Features / Right Scale / Learn Relationships)
- Segmentation Task
- Tips and Tricks
- Data Augmentation to avoid overfitting
- Hyper Parameter Tuning (Grid Search, Random Search, Bayesian Optimization)
- Abstraction Layers for missing inputs
- Uncertainty of networks
Happy Mastering DL!!!
