Deep Learning AI Techniques for Executives, Developers and Managers Training Course

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