Topic
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Date
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Notes
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Basic machine learning and Python scikit-learn
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01/23/2019
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Introduction
Basic Unix command sheet
Instructions for AFS login
Basic machine learning background with Python scikit-learn
Datasets
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Introduction to GPU computing
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01/28/2019
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GPU coding (also see Cuda by Example by Kandrot and Sanders)
Numpy tutorials
Official Numpy tutorial
External Numpy tutorial
CUDA in Python
Numba
CUDAJIT in Anaconda
PyCUDA (PyCUDA slides)
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CUDA programming
|
01/30/2019
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Parallel chi-square 2-df test
Chi-square 2-df test in parallel on a GPU
Simulated GWAS
Class labels for above data
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CUDA programming
|
02/04/2019
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Parallel Chi-square 2-df test
Assignment 1
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OpenCL and OpenMP programming
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02/06/2019
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CUDA to OpenCL slides
libOpenCL.so (NVIDIA library file for OpenCL code)
Chi2 opencl implementation
OpenCL files
CUDA to OpenMP slides
OpenMP reference
Chi2 openmp implementation
Assignment 2
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Neural networks
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02/11/2019
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Multi-layer perceptrons
Scikit-learn MLP code
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Image classification
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02/13/2019
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Image classification code
Python Image Library
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Convolutional kernels for images
|
02/18/2019
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Convolutional kernels
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Convolutional neural networks
|
02/20/2019
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Convolutional neural network
(Additional slides by Yunzhe Xue)
Convolution and single layer neural networks objective and optimization
Flower image classification with CNNs code
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Convolutional neural networks
|
02/25/2019
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Convolution and single layer neural networks objective and optimization
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Back propagation for single layer network with numpy
|
02/27/2019
|
Assignment 3
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Online lecture for backpropagation weight updates
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03/02/2019
|
|
NJIT closed due to weather
|
03/04/2019
|
|
Back propagation for single layer network with numpy, stochastic gradient descent
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03/06/2019
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Optimization in neural networks
Assignment 4
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Back propagation for convolutional network with one convolutional layer
followed by global average pooling
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03/11/2019
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Assignment 5
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Introduction to Keras
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03/13/2019
|
Spring Break
|
03/18/2019
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Spring Break
|
03/20/2019
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Spring Break lecture
|
03/22/2019
|
Keras and mid-term review
|
03/25/2019
|
Mid-term review sheet
Assignment 6
|
|
03/27/2019
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Mid-term exam
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Convolutional neural networks in Keras: convolutional blocks and layering
|
04/01/2019
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Image classification code v2
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Convolutional neural networks in Keras: normalization types and their effect
|
04/03/2019
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Batch normalization
Batch normalization paper
Group normalization paper
How does batch normalization help optimization
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Convolutional neural networks: gradient descent optimization. Large-scale image
classification
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04/08/2019
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Gradient descent optimization
An overview of gradient descent optimization algorithms
Curriculum learning
CIFAR 10
CIFAR 100
STL 10
Mini ImageNet
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More on training convolutional neural networks
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04/10/2019
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On training deep networks
The Loss Surfaces of Multilayer Networks
Ant colony optimization for deep networks
Simulated Annealing Algorithm for Deep Learning
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Convolutional neural networks in Keras: pertrained models and transfer learning
Convolutions and deconvolutions, temporal convolutional kernels
Common architectures: ResNet, DenseNet, VGG
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04/15/2019
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A guide to convolution arithmetic for deep
learning
Common architectures
Transfer learning by Yunzhe Xue
Pre-trained models in Keras
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Continuation of above plus cross-entropy loss function vs. least squares
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04/17/2019
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Assignment 7
Mini ImageNet in original form. Data are 256x256 images spread
across different directories
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Multi-path networks, data augmentation, time-series and sequence networks
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04/22/2019
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An Empirical Evaluation of Generic Convolutional and Recurrent
Networks for Sequence Modeling
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Image classification on Kaggle datasets, self-designed networks, self-supervised learning
|
04/24/2019
|
Assignment 8
Kaggle datasets:
(a) Fruits
(b) Flowers
(c) Chest X-rays
Exploring Randomly Wired Neural Networks for Image
Recognition
Automatically Designing CNN Architectures Using Genetic Algorithm
for Image Classification
UNSUPERVISED REPRESENTATION LEARNING BY PREDICTING IMAGE
ROTATIONS
Unsupervised Visual Representation Learning by Context
Prediction
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
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Image retrieval, U-networks
|
04/29/2019
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U-Net: Convolutional Networks for Biomedical Image Segmentation
GitHub UNet in Keras
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Image retrieval
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05/01/2019
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Deep Learning for Image Retrieval: What Works and What Doesn't
CNN Features off-the-shelf: an Astounding Baseline for Recognition
Deep Learning for Content-Based Image Retrieval: A Comprehensive Study
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Adversarial attacks, final exam review
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05/06/2019
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Final exam review sheet
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