Week 4 advanced labeling, augmentation and data preprocessingAjay Taneja
This is the Machine Learning Engineering in Production Course notes. This is the Week 4 of Machine Learning Data Life Cycle in Production (Course 2) course. This is the course 2 of MLOps specialization on coursera
Course 2 Machine Learning Data LifeCycle in Production - Week 1Ajay Taneja
This is the Machine Learning Engineering in Production Course notes. This is the Week 1 of Machine Learning Data Life Cycle in Production (Course 2) course. This is the course 2 of MLOps specialization on coursera
Machine Learning Data Life Cycle in Production (Week 2 feature engineering...Ajay Taneja
This is the Machine Learning Engineering in Production Course notes. This is the Week 2 of Machine Learning Data Life Cycle in Production (Course 2) course. This is the course 2 of MLOps specialization on coursera
This is the Machine Learning Engineering in Production Course notes. This is the Week 3 of Machine Learning Data Life Cycle in Production (Course 2) course. This is the course 2 of MLOps specialization on coursera
Week 4 advanced labeling, augmentation and data preprocessingAjay Taneja
This is the Machine Learning Engineering in Production Course notes. This is the Week 4 of Machine Learning Data Life Cycle in Production (Course 2) course. This is the course 2 of MLOps specialization on coursera
Course 2 Machine Learning Data LifeCycle in Production - Week 1Ajay Taneja
This is the Machine Learning Engineering in Production Course notes. This is the Week 1 of Machine Learning Data Life Cycle in Production (Course 2) course. This is the course 2 of MLOps specialization on coursera
Machine Learning Data Life Cycle in Production (Week 2 feature engineering...Ajay Taneja
This is the Machine Learning Engineering in Production Course notes. This is the Week 2 of Machine Learning Data Life Cycle in Production (Course 2) course. This is the course 2 of MLOps specialization on coursera
This is the Machine Learning Engineering in Production Course notes. This is the Week 3 of Machine Learning Data Life Cycle in Production (Course 2) course. This is the course 2 of MLOps specialization on coursera
This presentation nlp classifiers, the different types of models tfidf, word2vec & DL models such as feed forward NN , CNN & siamese networks. Details on important metrics such as precision, recall AUC are also given
Developing Recommendation System to provide a PersonalizedLearning experienc...Sanghamitra Deb
This presentation covers (1) Rich content developed at Chegg (2) An excellent knowledge graph that organizes content in a hierarchical fashion (3) Interaction of students across multiple products to enhance user signal in individual products.
Feature Engineering - Getting most out of data for predictive modelsGabriel Moreira
How should data be preprocessed for use in machine learning algorithms? How to identify the most predictive attributes of a dataset? What features can generate to improve the accuracy of a model?
Feature Engineering is the process of extracting and selecting, from raw data, features that can be used effectively in predictive models. As the quality of the features greatly influences the quality of the results, knowing the main techniques and pitfalls will help you to succeed in the use of machine learning in your projects.
In this talk, we will present methods and techniques that allow us to extract the maximum potential of the features of a dataset, increasing flexibility, simplicity and accuracy of the models. The analysis of the distribution of features and their correlations, the transformation of numeric attributes (such as scaling, normalization, log-based transformation, binning), categorical attributes (such as one-hot encoding, feature hashing, Temporal (date / time), and free-text attributes (text vectorization, topic modeling).
Python, Python, Scikit-learn, and Spark SQL examples will be presented and how to use domain knowledge and intuition to select and generate features relevant to predictive models.
Using Deep Learning to Find Similar DressesHJ van Veen
Report by Luís Mey ( https://www.linkedin.com/in/lu%C3%ADs-gustavo-bernardo-mey-97b38927/ ) on Udacity Machine Learning Course - Final Project: Use Deep Learning to Find Similar Dresses.
These are the slides from workshop: Introduction to Machine Learning with R which I gave at the University of Heidelberg, Germany on June 28th 2018.
The accompanying code to generate all plots in these slides (plus additional code) can be found on my blog: https://shirinsplayground.netlify.com/2018/06/intro_to_ml_workshop_heidelberg/
The workshop covered the basics of machine learning. With an example dataset I went through a standard machine learning workflow in R with the packages caret and h2o:
- reading in data
- exploratory data analysis
- missingness
- feature engineering
- training and test split
- model training with Random Forests, Gradient Boosting, Neural Nets, etc.
- hyperparameter tuning
Winning Kaggle 101: Introduction to StackingTed Xiao
An Introduction to Stacking by Erin LeDell, from H2O.ai
Presented as part of the "Winning Kaggle 101" event, hosted by Machine Learning at Berkeley and Data Science Society at Berkeley. Special thanks to the Berkeley Institute of Data Science for the venue!
H2O.ai: http://www.h2o.ai/
ML@B: ml.berkeley.edu
DSSB: http://dssberkeley.org
BIDS: http://bids.berkeley.edu/
Kaggle Otto Challenge: How we achieved 85th out of 3,514 and what we learntEugene Yan Ziyou
Our team achieved 85th position out of 3,514 at the very popular Kaggle Otto Product Classification Challenge. Here's an overview of how we did it, as well as some techniques we learnt from fellow Kagglers during and after the competition.
Basics of machine learning. Fundamentals of machine learning. These slides are collected from different learning materials and organized into one slide set.
Using Optimal Learning to Tune Deep Learning PipelinesScott Clark
SigOpt talk from NVIDIA GTC 2017 and AWS Popup Loft AI Day
We'll introduce Bayesian optimization as an efficient way to optimize machine learning model parameters, especially when evaluating different parameters is time consuming or expensive. Deep learning pipelines are notoriously expensive to train and often have many tunable parameters, including hyperparameters, the architecture, and feature transformations, that can have a large impact on the efficacy of the model. We'll provide several example applications using multiple open source deep learning frameworks and open datasets. We'll compare the results of Bayesian optimization to standard techniques like grid search, random search, and expert tuning. Additionally, we'll present a robust benchmark suite for comparing these methods in general.
http://imatge-upc.github.io/telecombcn-2016-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of big annotated data and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which had been addressed until now with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or text captioning.
This presentation nlp classifiers, the different types of models tfidf, word2vec & DL models such as feed forward NN , CNN & siamese networks. Details on important metrics such as precision, recall AUC are also given
Developing Recommendation System to provide a PersonalizedLearning experienc...Sanghamitra Deb
This presentation covers (1) Rich content developed at Chegg (2) An excellent knowledge graph that organizes content in a hierarchical fashion (3) Interaction of students across multiple products to enhance user signal in individual products.
Feature Engineering - Getting most out of data for predictive modelsGabriel Moreira
How should data be preprocessed for use in machine learning algorithms? How to identify the most predictive attributes of a dataset? What features can generate to improve the accuracy of a model?
Feature Engineering is the process of extracting and selecting, from raw data, features that can be used effectively in predictive models. As the quality of the features greatly influences the quality of the results, knowing the main techniques and pitfalls will help you to succeed in the use of machine learning in your projects.
In this talk, we will present methods and techniques that allow us to extract the maximum potential of the features of a dataset, increasing flexibility, simplicity and accuracy of the models. The analysis of the distribution of features and their correlations, the transformation of numeric attributes (such as scaling, normalization, log-based transformation, binning), categorical attributes (such as one-hot encoding, feature hashing, Temporal (date / time), and free-text attributes (text vectorization, topic modeling).
Python, Python, Scikit-learn, and Spark SQL examples will be presented and how to use domain knowledge and intuition to select and generate features relevant to predictive models.
Using Deep Learning to Find Similar DressesHJ van Veen
Report by Luís Mey ( https://www.linkedin.com/in/lu%C3%ADs-gustavo-bernardo-mey-97b38927/ ) on Udacity Machine Learning Course - Final Project: Use Deep Learning to Find Similar Dresses.
These are the slides from workshop: Introduction to Machine Learning with R which I gave at the University of Heidelberg, Germany on June 28th 2018.
The accompanying code to generate all plots in these slides (plus additional code) can be found on my blog: https://shirinsplayground.netlify.com/2018/06/intro_to_ml_workshop_heidelberg/
The workshop covered the basics of machine learning. With an example dataset I went through a standard machine learning workflow in R with the packages caret and h2o:
- reading in data
- exploratory data analysis
- missingness
- feature engineering
- training and test split
- model training with Random Forests, Gradient Boosting, Neural Nets, etc.
- hyperparameter tuning
Winning Kaggle 101: Introduction to StackingTed Xiao
An Introduction to Stacking by Erin LeDell, from H2O.ai
Presented as part of the "Winning Kaggle 101" event, hosted by Machine Learning at Berkeley and Data Science Society at Berkeley. Special thanks to the Berkeley Institute of Data Science for the venue!
H2O.ai: http://www.h2o.ai/
ML@B: ml.berkeley.edu
DSSB: http://dssberkeley.org
BIDS: http://bids.berkeley.edu/
Kaggle Otto Challenge: How we achieved 85th out of 3,514 and what we learntEugene Yan Ziyou
Our team achieved 85th position out of 3,514 at the very popular Kaggle Otto Product Classification Challenge. Here's an overview of how we did it, as well as some techniques we learnt from fellow Kagglers during and after the competition.
Basics of machine learning. Fundamentals of machine learning. These slides are collected from different learning materials and organized into one slide set.
Using Optimal Learning to Tune Deep Learning PipelinesScott Clark
SigOpt talk from NVIDIA GTC 2017 and AWS Popup Loft AI Day
We'll introduce Bayesian optimization as an efficient way to optimize machine learning model parameters, especially when evaluating different parameters is time consuming or expensive. Deep learning pipelines are notoriously expensive to train and often have many tunable parameters, including hyperparameters, the architecture, and feature transformations, that can have a large impact on the efficacy of the model. We'll provide several example applications using multiple open source deep learning frameworks and open datasets. We'll compare the results of Bayesian optimization to standard techniques like grid search, random search, and expert tuning. Additionally, we'll present a robust benchmark suite for comparing these methods in general.
http://imatge-upc.github.io/telecombcn-2016-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of big annotated data and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which had been addressed until now with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or text captioning.
Feature Engineering - Getting most out of data for predictive models - TDC 2017Gabriel Moreira
How should data be preprocessed for use in machine learning algorithms? How to identify the most predictive attributes of a dataset? What features can generate to improve the accuracy of a model?
Feature Engineering is the process of extracting and selecting, from raw data, features that can be used effectively in predictive models. As the quality of the features greatly influences the quality of the results, knowing the main techniques and pitfalls will help you to succeed in the use of machine learning in your projects.
In this talk, we will present methods and techniques that allow us to extract the maximum potential of the features of a dataset, increasing flexibility, simplicity and accuracy of the models. The analysis of the distribution of features and their correlations, the transformation of numeric attributes (such as scaling, normalization, log-based transformation, binning), categorical attributes (such as one-hot encoding, feature hashing, Temporal (date / time), and free-text attributes (text vectorization, topic modeling).
Python, Python, Scikit-learn, and Spark SQL examples will be presented and how to use domain knowledge and intuition to select and generate features relevant to predictive models.
Ernest: Efficient Performance Prediction for Advanced Analytics on Apache Spa...Spark Summit
Recent workload trends indicate rapid growth in the deployment of machine learning, genomics and scientific workloads using Apache Spark. However, efficiently running these applications on
cloud computing infrastructure like Amazon EC2 is challenging and we find that choosing the right hardware configuration can significantly
improve performance and cost. The key to address the above challenge is having the ability to predict performance of applications under
various resource configurations so that we can automatically choose the optimal configuration. We present Ernest, a performance prediction
framework for large scale analytics. Ernest builds performance models based on the behavior of the job on small samples of data and then
predicts its performance on larger datasets and cluster sizes. Our evaluation on Amazon EC2 using several workloads shows that our prediction error is low while having a training overhead of less than 5% for long-running jobs.
A Tale of Three Deep Learning Frameworks: TensorFlow, Keras, & PyTorch with B...Databricks
We all know what they say – the bigger the data, the better. But when the data gets really big, how do you mine it and what deep learning framework to use? This talk will survey, with a developer’s perspective, three of the most popular deep learning frameworks—TensorFlow, Keras, and PyTorch—as well as when to use their distributed implementations.
We’ll compare code samples from each framework and discuss their integration with distributed computing engines such as Apache Spark (which can handle massive amounts of data) as well as help you answer questions such as:
As a developer how do I pick the right deep learning framework?
Do I want to develop my own model or should I employ an existing one?
How do I strike a trade-off between productivity and control through low-level APIs?
What language should I choose?
In this session, we will explore how to build a deep learning application with Tensorflow, Keras, or PyTorch in under 30 minutes. After this session, you will walk away with the confidence to evaluate which framework is best for you.
Tensors Are All You Need: Faster Inference with HummingbirdDatabricks
The ever-increasing interest around deep learning and neural networks has led to a vast increase in processing frameworks like TensorFlow and PyTorch. These libraries are built around the idea of a computational graph that models the dataflow of individual units. Because tensors are their basic computational unit, these frameworks can run efficiently on hardware accelerators (e.g. GPUs).Traditional machine learning (ML) such as linear regressions and decision trees in scikit-learn cannot currently be run on GPUs, missing out on the potential accelerations that deep learning and neural networks enjoy.
In this talk, we’ll show how you can use Hummingbird to achieve 1000x speedup in inferencing on GPUs by converting your traditional ML models to tensor-based models (PyTorch andTVM). https://github.com/microsoft/hummingbird
This talk is for intermediate audiences that use traditional machine learning and want to speedup the time it takes to perform inference with these models. After watching the talk, the audience should be able to use ~5 lines of code to convert their traditional models to tensor-based models to be able to try them out on GPUs.
Outline:
Introduction of what ML inference is (and why it’s different than training)
Motivation: Tensor-based DNN frameworks allow inference on GPU, but “traditional” ML frameworks do not
Why “traditional” ML methods are important
Introduction of what Hummingbirddoes and main benefits
Deep dive on how traditional ML models are built
Brief intro onhow Hummingbird converter works
Example of how Hummingbird can convert a tree model into a tensor-based model
Other models
Demo
Status
Q&A
Start machine learning in 5 simple stepsRenjith M P
Simple steps to get started with machine learning.
The use case uses python programming. Target audience is expected to have a very basic python knowledge.
Standardizing on a single N-dimensional array API for PythonRalf Gommers
MXNet workshop Dec 2020 presentation on the array API standardization effort ongoing in the Consortium for Python Data API Standards - see data-apis.org
Learning to rank (LTR) for information retrieval (IR) involves the application of machine learning models to rank artifacts, such as webpages, in response to user's need, which may be expressed as a query. LTR models typically employ training data, such as human relevance labels and click data, to discriminatively train towards an IR objective. The focus of this lecture will be on the fundamentals of neural networks and their applications to learning to rank.
Scaling Up AI Research to Production with PyTorch and MLFlowDatabricks
PyTorch, the popular open-source ML framework, has continued to evolve rapidly since the introduction of PyTorch 1.0, which brought an accelerated workflow from research to production.
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
Water scarcity is the lack of fresh water resources to meet the standard water demand. There are two type of water scarcity. One is physical. The other is economic water scarcity.
About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Quality defects in TMT Bars, Possible causes and Potential Solutions.PrashantGoswami42
Maintaining high-quality standards in the production of TMT bars is crucial for ensuring structural integrity in construction. Addressing common defects through careful monitoring, standardized processes, and advanced technology can significantly improve the quality of TMT bars. Continuous training and adherence to quality control measures will also play a pivotal role in minimizing these defects.
Courier management system project report.pdfKamal Acharya
It is now-a-days very important for the people to send or receive articles like imported furniture, electronic items, gifts, business goods and the like. People depend vastly on different transport systems which mostly use the manual way of receiving and delivering the articles. There is no way to track the articles till they are received and there is no way to let the customer know what happened in transit, once he booked some articles. In such a situation, we need a system which completely computerizes the cargo activities including time to time tracking of the articles sent. This need is fulfilled by Courier Management System software which is online software for the cargo management people that enables them to receive the goods from a source and send them to a required destination and track their status from time to time.
Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
Automobile Management System Project Report.pdfKamal Acharya
The proposed project is developed to manage the automobile in the automobile dealer company. The main module in this project is login, automobile management, customer management, sales, complaints and reports. The first module is the login. The automobile showroom owner should login to the project for usage. The username and password are verified and if it is correct, next form opens. If the username and password are not correct, it shows the error message.
When a customer search for a automobile, if the automobile is available, they will be taken to a page that shows the details of the automobile including automobile name, automobile ID, quantity, price etc. “Automobile Management System” is useful for maintaining automobiles, customers effectively and hence helps for establishing good relation between customer and automobile organization. It contains various customized modules for effectively maintaining automobiles and stock information accurately and safely.
When the automobile is sold to the customer, stock will be reduced automatically. When a new purchase is made, stock will be increased automatically. While selecting automobiles for sale, the proposed software will automatically check for total number of available stock of that particular item, if the total stock of that particular item is less than 5, software will notify the user to purchase the particular item.
Also when the user tries to sale items which are not in stock, the system will prompt the user that the stock is not enough. Customers of this system can search for a automobile; can purchase a automobile easily by selecting fast. On the other hand the stock of automobiles can be maintained perfectly by the automobile shop manager overcoming the drawbacks of existing system.
Vaccine management system project report documentation..pdfKamal Acharya
The Division of Vaccine and Immunization is facing increasing difficulty monitoring vaccines and other commodities distribution once they have been distributed from the national stores. With the introduction of new vaccines, more challenges have been anticipated with this additions posing serious threat to the already over strained vaccine supply chain system in Kenya.
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
TECHNICAL TRAINING MANUAL GENERAL FAMILIARIZATION COURSEDuvanRamosGarzon1
AIRCRAFT GENERAL
The Single Aisle is the most advanced family aircraft in service today, with fly-by-wire flight controls.
The A318, A319, A320 and A321 are twin-engine subsonic medium range aircraft.
The family offers a choice of engines
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems!
Here's a great example: At a large natural gas-fired power plant, where they use waste heat to generate steam and energy, they were puzzled that their boiler wasn't producing as much steam as expected.
R&R and Tetra Engineering Group Inc. were asked to solve the issue with reduced steam production.
An inspection had shown that a significant amount of hot flue gas was bypassing the boiler tubes, where the heat was supposed to be transferred.
R&R Consult conducted a CFD analysis, which revealed that 6.3% of the flue gas was bypassing the boiler tubes without transferring heat. The analysis also showed that the flue gas was instead being directed along the sides of the boiler and between the modules that were supposed to capture the heat. This was the cause of the reduced performance.
Based on our results, Tetra Engineering installed covering plates to reduce the bypass flow. This improved the boiler's performance and increased electricity production.
It is always satisfying when we can help solve complex challenges like this. Do your systems also need a check-up or optimization? Give us a call!
Work done in cooperation with James Malloy and David Moelling from Tetra Engineering.
More examples of our work https://www.r-r-consult.dk/en/cases-en/
Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
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C3 w2
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4. High-dimensional data
Before. .. when it
was all about data
mining
● Domain experts selected features
● Designed feature transforms
● Small number of more relevant features were enough
Now … data science
is about integrating
everything
● Data generation and storage is less of a problem
● Squeeze out the best from data
● More high-dimensional data having more features
5. A note about neural networks
● Yes, neural networks will perform a kind of automatic feature selection
● However, that’s not as efficient as a well-designed dataset and model
○ Much of the model can be largely “shut off” to ignore unwanted
features
○ Even unused parts of the consume space and compute resources
○ Unwanted features can still introduce unwanted noise
○ Each feature requires infrastructure to collect, store, and manage
7. Word embedding - An example
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6 7 8 5 11
Auto Embedding Weight Matrix
[“I want to search for blood pressure result history “,
“Show blood pressure result for patient”,...]
Input layer
(Learned vectors)
Embedding Layer
i
want
to
search
for
blood
pressure
result
history
show
patient
...
LAST
1
2
3
4
5
6
7
8
9
10
11
20
8. Initialization and loading the dataset
import tensorflow as tf
from tensorflow import keras
import numpy as np
from keras.datasets import reuters
from keras.preprocessing import sequence
num_words = 1000
(reuters_train_x, reuters_train_y), (reuters_test_x, reuters_test_y) =
tf.keras.datasets.reuters.load_data(num_words=num_words)
n_labels = np.unique(reuters_train_y).shape[0]
17. ● More dimensions → more features
● Risk of overfitting our models
● Distances grow more and more alike
● No clear distinction between clustered objects
● Concentration phenomenon for Euclidean distance
Why is high-dimensional data a problem?
18. Curse of dimensionality
“As we add more dimensions we also increase the processing power we
need to train the model and make predictions, as well as the amount of
training data required”
Badreesh Shetty
19. Why are more features bad?
● Redundant / irrelevant features
● More noise added than signal
● Hard to interpret and visualize
● Hard to store and process data
20. The performance of algorithms ~ the number of dimensions
Optimal Dimensionality (# of features)
Classifier
Performance
22. Curse of dimensionality in the distance function
● New dimensions add non-negative terms to the sum
● Distance increases with the number of dimensions
● For a given number of examples, the feature space becomes
increasingly sparse
Euclidean distance
24. x x x
x
x
x
x x x
x x x
x x x
The more the features, the larger the hypothesis space
The lower the hypothesis space
● the easier it is to find the correct hypothesis
● the less examples you need
The Hughes effect
26. How dimensionality impacts in other ways
● Runtime and system memory
requirements
● Solutions take longer to reach global
optima
● More dimensions raise the likelihood
of correlated features
27. More features require more training data
● More features aren’t better if they don’t add predictive information
● Number of training instances needed increases exponentially with each
added feature
● Reduces real-world usefulness of models
29. Model #2 (adds a new feature)
sex:InputLayer
cp:InputLayer
fbs:InputLayer
restecg : InputLayer
exang:InputLayer
category_encoding_6:CategoryEncoding
category_encoding_1:CategoryEncoding
category_encoding_2:CategoryEncoding
category_encoding_3:CategoryEncoding
category_encoding_4:CategoryEncoding
thal:InputLayer string_lookup:StringLookup
ca:InputLayer
age:InputLayer
trestbps:InputLayer
chol:InputLayer
thalach:InputLayer
category_enconding_5:CategoryEncoding
normalization:Normalization
normalization_1:Normalization
normalization_2:Normalization
normalization_3:Normalization
oldpeak:InputLayer
normalization_4:Normalization
A new string categorical
feature is added!
slope:InputLayer normalization_5:Normalization concatenate: Concatenate dense: Dense dropout: dropout dense_1: Dense
30. from tensorflow.python.keras.utils.layer_utils import count_params
# Number of training parameters in Model #1
>>> count_params(model_1.trainable_variables)
833
# Number of training parameters in Model #2 (with an added feature)
>>> count_params(model_1.trainable_variables)
1057
Comparing the two models’ trainable variables
31. What do ML models need?
● No hard and fast rule on how many features are required
● Number of features to be used vary depending on
● Prefer uncorrelated data containing information to
produce correct results
33. Increasing predictive performance
● Features must have information to produce correct results
● Derive features from inherent features
● Extract and recombine to create new features
34. Combining features
● Number of features grows very quickly
● Reduce dimensionality
pixels,
contours,
textures, etc.
samples,
spectrograms,
etc.
ticks, trends,
reversals, etc.
dna, marker
sequences,
genes, etc.
words,
grammatical
classes and
relations, etc.
Initial features
Feature explosion
35. Why reduce dimensionality?
Major techniques for
dimensionality
reduction Engineering Selection
Storage Computational Consistency Visualization
36. Need for manually crafting features
Certainly provides food for thought
Engineer features
● Tabular - aggregate, combine,
decompose
● Text-extract context
indicators
● Image-prescribe filters for
relevant structures
Come up with ideas to construct
“better” features
Devising features to reduce
dimensionality
Select the right features to maximize
predictiveness
Evaluate models using chosen
features
It’s an iterative
process
Feature Engineering
40. from tensorflow.keras import layers
from tensorflow.keras.metrics import RootMeanSquared as RMSE
dnn_inputs = layers.DenseFeatures(feature_columns.values())(inputs)
h1 = layers.Dense(32, activation='relu', name='h1')(dnn_inputs)
h2 = layers.Dense(8, activation='relu', name='h2')(h1)
output = layers.Dense(1, activation='linear', name='fare')(h2)
model = models.Model(inputs, output)
model.compile(optimizer='adam', loss='mse',
metrics=[RMSE(name='rmse'), 'mse'])
Build a baseline model using raw features
42. Increasing model performance with Feature Engineering
● Carefully craft features for the data types
○ Temporal (pickup date & time)
○ Geographical (latitude and longitude)
43. def parse_datetime(s):
if type(s) is not str:
s = s.numpy().decode('utf-8')
return datetime.datetime.strptime(s, "%Y-%m-%d %H:%M:%S %Z")
def get_dayofweek(s):
ts = parse_datetime(s)
return DAYS[ts.weekday()]
@tf.function
def dayofweek(ts_in):
return tf.map_fn(
lambda s: tf.py_function(get_dayofweek, inp=[s],
Tout=tf.string),
ts_in)
Handling temporal features
53. Linear dimensionality reduction
● Linearly project n-dimensional data onto a k-dimensional subspace
(k < n, often k << n)
● There are infinitely many k-dimensional subspaces we can project the
data onto
● Which one should we choose?
54. f1
f2
f3
... ... ... fn-1
fn
Dimensionality
Reduction
Output
(dark) 0 1 (bright)
Projecting onto a line
55. Best k-dimensional subspace for projection
Classification: maximize separation among classes
Example: Linear discriminant analysis (LDA)
Regression: maximize correlation between projected data and response variable
Example: Partial least squares (PLS)
Unsupervised: retain as much data variance as possible
Example: Principal component analysis (PCA)
57. Principal component analysis (PCA)
● PCA is a minimization of the
orthogonal distance
● Widely used method for unsupervised
& linear dimensionality reduction
● Accounts for variance of data in as
few dimensions as possible using
linear projections
58. Principal components (PCs)
● PCs maximize the variance of
projections
● PCs are orthogonal
● Gives the best axis to project
● Goal of PCA: Minimize total squared
reconstruction error
1st
principal vector
2nd
principal vector
64. tot = sum(pca.e_vals_)
var_exp = [(i / tot) * 100 for i in sorted(pca.e_vals_, reverse=True)]
cum_var_exp = np.cumsum(var_exp)
Plot the explained variance
66. from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
from sklearn import datasets
# Load the data
digits = datasets.load_digits()
# Standardize the feature matrix
X = StandardScaler().fit_transform(digits.data)
PCA in scikit-learn
67. # Create a PCA that will retain 99% of the variance
pca = PCA(n_components=0.99, whiten=True)
# Conduct PCA
X_pca = pca.fit_transform(X)
PCA in scikit-learn
68. When to use PCA?
Strengths
● A versatile technique
● Fast and simple
● Offers several variations and extensions (e.g., kernel/sparse PCA)
Weaknesses
● Result is not interpretable
● Requires setting threshold for cumulative explained variance
71. Singular value decomposition (SVD)
● SVD decomposes non-square matrices
● Useful for sparse matrices as produced by TF-IDF
● Removes redundant features from the dataset
72. Independent Components Analysis (ICA)
● PCA seeks directions in feature space that minimize reconstruction
error
● ICA seeks directions that are most statistically independent
● ICA addresses higher order dependence
73. How does ICA work?
● Assume there exists independent signals:
𝑆 = [𝑠1
(𝑡) , 𝑠2
(𝑡) , … , 𝑠𝑁
(𝑡) ]
● Linear combinations of signals: 𝑌(𝑡) = 𝐴 𝑆(𝑡)
○ Both A and S are unknown
○ A - mixing matrix
● Goal of ICA: recover original signals, 𝑆(𝑡) from 𝑌(𝑡)
74. PCA ICA
Removes correlations ✓ ✓
Removes higher order
dependence
✓
All components treated
fairly?
✓
Orthogonality ✓
Comparing PCA and ICA
75. Non-negative Matrix Factorization (NMF)
● NMF models are interpretable and easier to understand
● NMF requires the sample features to be non-negative
78. Factors driving this trend
● Demands move ML capability from cloud to on-device
● Cost-effectiveness
● Compliance with privacy regulations
79. Online ML inference
● To generate real-time predictions you can:
○ Host the model on a server
○ Embed the model in the device
● Is it faster on a server, or on-device?
● Mobile processing limitations?
80. Classification request
Prediction results
Inference on the cloud/server
Pros
● Lots of compute capacity
● Scalable hardware
● Model complexity handled by the server
● Easy to add new features and update the model
● Low latency and batch prediction
Mobile inference
Cons
● Timely inference is needed
81. Pro
● Improved speed
● Performance
● Network connectivity
● No to-and-fro communication needed
On-device Inference
Mobile inference
Cons
● Less capacity
● Tight resource constraints
85. Why quantize neural networks?
● Neural networks have many parameters and take up space
● Shrinking model file size
● Reduce computational resources
● Make models run faster and use less power with low-precision
89. What parts of the model are affected?
● Static values (parameters)
● Dynamic values (activations)
● Computation (transformations)
90. Trade-offs
● Optimizations impact model accuracy
○ Difficult to predict ahead of time
● In rare cases, models may actually gain some accuracy
● Undefined effects on ML interpretability
97. Model accuracy
● Small accuracy loss incurred (mostly for smaller networks)
● Use the benchmarking tools to evaluate model accuracy
● If the loss of accuracy drop is not within acceptable limits, consider
using quantization-aware training
99. Quantization-aware training (QAT)
● Inserts fake quantization (FQ) nodes in the forward pass
● Rewrites the graph to emulate quantized inference
● Reduces the loss of accuracy due to quantization
● Resulting model contains all data to be quantized according to spec
103. import tensorflow_model_optimization as tfmot
model = tf.keras.Sequential([
...
])
QAT on entire model
# Quantize the entire model.
quantized_model = tfmot.quantization.keras.quantize_model(model)
# Continue with training as usual.
quantized_model.compile(...)
quantized_model.fit(...)
104. import tensorflow_model_optimization as tfmot
quantize_annotate_layer = tfmot.quantization.keras.quantize_annotate_layer
model = tf.keras.Sequential([
...
# Only annotated layers will be quantized.
quantize_annotate_layer(Conv2D()),
quantize_annotate_layer(ReLU()),
Dense(),
...
])
# Quantize the model.
quantized_model = tfmot.quantization.keras.quantize_apply(model)
Quantize part(s) of a model
106. # `quantize_apply` requires mentioning `DefaultDenseQuantizeConfig` with
`quantize_scope`
with quantize_scope(
{'DefaultDenseQuantizeConfig': DefaultDenseQuantizeConfig,
'CustomLayer': CustomLayer}):
# Use `quantize_apply` to actually make the model quantization aware.
quant_aware_model = tfmot.quantization.keras.quantize_apply(model)
Quantize custom Keras layer
115. Finding Sparse Neural Networks
“A randomly-initialized, dense neural network contains a subnetwork that is
initialized such that — when trained in isolation — it can match the test
accuracy of the original network after training for at most the same number
of iterations”
Jonathan Frankle and Michael Carbin
116. Pruning research is evolving
● The new method didn’t perform well at large scale
● The new method failed to identify the randomly initialized winners
● It’s an active area of research
117. Tensors with no sparsity (left), sparsity in blocks of 1x1 (center), and the
sparsity in blocks 1x2 (right)
Eliminate connections based on their magnitude
118. Example of sparsity ramp-up function with a schedule to start pruning from step 0
until step 100, and a final target sparsity of 90%.
Apply sparsity with a pruning routine
119. Animation of pruning applied to a tensor
Black cells indicate where the non-zero weights exist
Sparsity increases with training
120. What’s special about pruning?
● Better storage and/or transmission
● Gain speedups in CPU and some ML accelerators
● Can be used in tandem with quantization to get additional benefits
● Unlock performance improvements
121. Pruning with TF Model Optimization Toolkit
● INT8
TensorFlow
(tf.Keras)
Sparsify
+
Train model
Convert &
Quantize
(TF Lite)
TF Lite
Model
122. import tensorflow_model_optimization as tfmot
model = build_your_model()
pruning_schedule = tfmot.sparsity.keras.PolynomialDecay(
initial_sparsity=0.50, final_sparsity=0.80,
begin_step=2000, end_step=4000)
model_for_pruning = tfmot.sparsity.keras.prune_low_magnitude(
model,
pruning_schedule=pruning_schedule)
...
model_for_pruning.fit(...)
Pruning with Keras