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          Deep Learning AI Techniques for Executives, Developers and Managers培訓(xùn)

Day-1:
Basic Machine Learning
Module-1
Introduction:

Exercise – Installing Python and NN Libraries
Why machine learning?
Brief history of machine learning
The rise of deep learning
Basic concepts in machine learning
Visualizing a classification problem
Decision boundaries and decision regions
iPython notebooks
Module-2
Exercise – Decision Regions
The artificial neuron
The neural network, forward propagation and network layers
Activation functions
Exercise – Activation Functions
Backpropagation of error
Underfitting and overfitting
Interpolation and smoothing
Extrapolation and data abstraction
Generalization in machine learning
Module-3
Exercise – Underfitting and Overfitting
Training, testing, and validation sets
Data bias and the negative example problem
Bias/variance tradeoff
Exercise – Datasets and Bias
Module-4
Overview of NN parameters and hyperparameters
Logistic regression problems
Cost functions
Example – Regression
Classical machine learning vs. deep learning
Conclusion
Day-2 : Convolutional Neural Networks (CNN)
Module-5
Introduction to CNN
What are CNNs?
Computer vision
CNNs in everyday life
Images – pixels, quantization of color & space, RGB
Convolution equations and physical meaning, continuous vs. discrete
Exercise – 1D Convolution
Module-6
Theoretical basis for filtering
Signal as sum of sinusoids
Frequency spectrum
Bandpass filters
Exercise – Frequency Filtering
2D convolutional filters
Padding and stride length
Filter as bandpass
Filter as template matching
Exercise – Edge Detection
Gabor filters for localized frequency analysis
Exercise – Gabor Filters as Layer 1 Maps
Module-7
CNN architecture
Convolutional layers
Max pooling layers
Downsampling layers
Recursive data abstraction
Example of recursive abstraction
Module-8
Exercise – Basic CNN Usage
ImageNet dataset and the VGG-16 model
Visualization of feature maps
Visualization of feature meanings
Exercise – Feature Maps and Feature Meanings
Day-3 : Sequence Model
Module-9
What are sequence models?
Why sequence models?
Language modeling use case
Sequences in time vs. sequences in space
Module-10
RNNs
Recurrent architecture
Backpropagation through time
Vanishing gradients
GRU
LSTM
Deep RNN
Bidirectional RNN
Exercise – Unidirectional vs. Bidirectional RNN
Sampling sequences
Sequence output prediction
Exercise – Sequence Output Prediction
RNNs on simple time varying signals
Exercise – Basic Waveform Detection
Module-11
Natural Language Processing (NLP)
Word embeddings
Word vectors: word2vec
Word vectors: GloVe
Knowledge transfer and word embeddings
Sentiment analysis
Exercise – Sentiment Analysis
Module-12
Quantifying and removing bias
Exercise – Removing Bias
Audio data
Beam search
Attention model
Speech recognition
Trigger word Detection
Exercise – Speech Recognition