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) Optimizers
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Classroom Contents
Deep Learning Crash Course for Beginners
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- 1 ) Introduction
- 2 ) What is Deep Learning
- 3 ) Introduction to Neural Networks
- 4 ) How do Neural Networks LEARN?
- 5 ) Core terminologies used in Deep Learning
- 6 ) Activation Functions
- 7 ) Loss Functions
- 8 ) Optimizers
- 9 ) Parameters vs Hyperparameters
- 10 ) Epochs, Batches & Iterations
- 11 ) Conclusion to Terminologies
- 12 ) Introduction to Learning
- 13 ) Supervised Learning
- 14 ) Unsupervised Learning
- 15 ) Reinforcement Learning
- 16 ) Regularization
- 17 ) Introduction to Neural Network Architectures
- 18 ) Fully-Connected Feedforward Neural Nets
- 19 ) Recurrent Neural Nets
- 20 ) Convolutional Neural Nets
- 21 ) Introduction to the 5 Steps to EVERY Deep Learning Model
- 22 ) 1. Gathering Data
- 23 ) 2. Preprocessing the Data
- 24 ) 3. Training your Model
- 25 ) 4. Evaluating your Model
- 26 ) 5. Optimizing your Model's Accuracy
- 27 ) Conclusion to the Course
