Deep Learning (Cont. I)
Introduction to Deep Learning Part I
E534 2019 BDAA DL Section Intro Unit: E534 2019 Big Data Applications and Analytics Introduction to Deep Learning Part I (Unit Intro) Section Summary
This section covers the growing importance of the use of Deep Learning in Big Data Applications and Analytics. The Intro Unit is an introduction to the technology with examples incidental. It includes an introducton to the laboratory where we use Keras and Tensorflow. The Tech unit covers the deep learning technology in more detail. The Application Units cover deep learning applications at different levels of sophistication.
Intro Unit Summary
This unit is an introduction to deep learning with four major lessons
Lesson Summaries Optimization: Overview of Optimization Opt lesson overviews optimization with a focus on issues of importance for deep learning. Gives a quick review of Objective Function, Local Minima (Optima), Annealing, Everything is an optimization problem with examples, Examples of Objective Functions, Greedy Algorithms, Distances in funny spaces, Discrete or Continuous Parameters, Genetic Algorithms, Heuristics.
First Deep Learning Example
FirstDL: Your First Deep Learning Example FirstDL Lesson gives an experience of running a non trivial deep learning application. It goes through the identification of numbers from NIST database using a Multilayer Perceptron using Keras+Tensorflow running on Google Colab
Deep Learning Basics
DLBasic: Basic Terms Used in Deep Learning DLBasic lesson reviews important Deep Learning topics including Activation: (ReLU, Sigmoid, Tanh, Softmax), Loss Function, Optimizer, Stochastic Gradient Descent, Back Propagation, One-hot Vector, Vanishing Gradient, Hyperparameter
Deep Learning Types
DLTypes: Types of Deep Learning: Summaries DLtypes Lesson reviews important Deep Learning neural network architectures including Multilayer Perceptron, CNN Convolutional Neural Network, Dropout for regularization, Max Pooling, RNN Recurrent Neural Networks, LSTM: Long Short Term Memory, GRU Gated Recurrent Unit, (Variational) Autoencoders, Transformer and Sequence to Sequence methods, GAN Generative Adversarial Network, (D)RL (Deep) Reinforcement Learning.