Course Outline

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

Requirements

There are no specific requirements needed to attend this course.

  21 Hours
 

Number of participants


Starts

Ends


Dates are subject to availability and take place between 09:30 and 16:30.
Open Training Courses require 5+ participants.

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