Key Lessons
- DL to extract patterns in data with little human effort (Automated Approach)
- Evolution of Deep Learning with respect to years of new innovations
- DL Tools
- Apply methodology requires organize data, label, select aspect of data to reval answers for our questions
- CPU, GPU, TPU - Hardware for efficient execution
- Higher abstractions help people to solve it in lesser time
- DL - Higher and Higher level of abstraction of understanding patterns
- History of Science is the Simpler representation of Ideas
- DL remove human from the picture, Automate the feature learning
- Problems are not formulated as Data Driven Learning
- RNN to predict intent of players in scene
- Visual understanding is hard
- Regression is Continuous, Classification is Categorical
- Sequence of inputs, Sequence of outputs
- Forward Pass
- Many levels of layers create the necessary layers
- Many of the slides are summary and self-explanatory, Well explained
- Activation functions, Loss functions
- Backpropagation to adjust the weights, error flow backward to network
- After Forward pass compute error
- Backward Pass - Compute gradients
- Learning rate how fast the network learns
Happy Mastering DL!!!
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