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Plan du cours
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
Pré requis
There are no specific requirements needed to attend this course.
21 heures
