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This document summarizes various optimization techniques for deep learning models, including gradient descent, stochastic gradient descent, and variants like momentum, Nesterov's accelerated gradient, AdaGrad, RMSProp, and Adam. It provides an overview of how each technique works and comparisons of their performance on image classification tasks using MNIST and CIFAR-10 datasets. The document concludes by encouraging attendees to try out the different optimization methods in Keras and provides resources for further deep learning topics.

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Decision Trees

Decision Trees

Deep neural networks

Deep neural networks

Feedforward neural network

Feedforward neural network

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Decision Trees

Decision trees are a type of supervised learning algorithm used for classification and regression. ID3 and C4.5 are algorithms that generate decision trees by choosing the attribute with the highest information gain at each step. Random forest is an ensemble method that creates multiple decision trees and aggregates their results, improving accuracy. It introduces randomness when building trees to decrease variance.

Deep neural networks

Deep learning and neural networks are inspired by biological neurons. Artificial neural networks (ANN) can have multiple layers and learn through backpropagation. Deep neural networks with multiple hidden layers did not work well until recent developments in unsupervised pre-training of layers. Experiments on MNIST digit recognition and NORB object recognition datasets showed deep belief networks and deep Boltzmann machines outperform other models. Deep learning is now widely used for applications like computer vision, natural language processing, and information retrieval.

Feedforward neural network

This slide is prepared for the lectures-in-turn challenge within the study group of social informatics, kyoto university.

Perceptron (neural network)

i. Perceptron
Representation & Issues
Classification
learning
ii. linear Separability

Support vector machines (svm)

A Support Vector Machine (SVM) is a discriminative classifier formally defined by a separating hyperplane. In other words, given labeled training data (supervised learning), the algorithm outputs an optimal hyperplane which categorizes new examples. In two dimentional space this hyperplane is a line dividing a plane in two parts where in each class lay in either side.

Convolutional Neural Networks (CNN)

A comprehensive tutorial on Convolutional Neural Networks (CNN) which talks about the motivation behind CNNs and Deep Learning in general, followed by a description of the various components involved in a typical CNN layer. It explains the theory involved with the different variants used in practice and also, gives a big picture of the whole network by putting everything together.
Next, there's a discussion of the various state-of-the-art frameworks being used to implement CNNs to tackle real-world classification and regression problems.
Finally, the implementation of the CNNs is demonstrated by implementing the paper 'Age ang Gender Classification Using Convolutional Neural Networks' by Hassner (2015).

Decision trees in Machine Learning

Supervised learning techniques , Decision tree algorithms for Machine learning . Classification and Regression trees.

Cnn

Convolutional neural networks (CNNs) learn multi-level features and perform classification jointly and better than traditional approaches for image classification and segmentation problems. CNNs have four main components: convolution, nonlinearity, pooling, and fully connected layers. Convolution extracts features from the input image using filters. Nonlinearity introduces nonlinearity. Pooling reduces dimensionality while retaining important information. The fully connected layer uses high-level features for classification. CNNs are trained end-to-end using backpropagation to minimize output errors by updating weights.

Activation function

This document provides an overview of activation functions in deep learning. It discusses the purpose of activation functions, common types of activation functions like sigmoid, tanh, and ReLU, and issues like vanishing gradients that can occur with some activation functions. It explains that activation functions introduce non-linearity, allowing neural networks to learn complex patterns from data. The document also covers concepts like monotonicity, continuity, and differentiation properties that activation functions should have, as well as popular methods for updating weights during training like SGD, Adam, etc.

Random Forest Algorithm - Random Forest Explained | Random Forest In Machine ...

This Random Forest Algorithm Presentation will explain how Random Forest algorithm works in Machine Learning. By the end of this video, you will be able to understand what is Machine Learning, what is classification problem, applications of Random Forest, why we need Random Forest, how it works with simple examples and how to implement Random Forest algorithm in Python.
Below are the topics covered in this Machine Learning Presentation:
1. What is Machine Learning?
2. Applications of Random Forest
3. What is Classification?
4. Why Random Forest?
5. Random Forest and Decision Tree
6. Comparing Random Forest and Regression
7. Use case - Iris Flower Analysis
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About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning.
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Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
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What skills will you learn from this Machine Learning course?
By the end of this Machine Learning course, you will be able to:
1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling.
2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.
5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems
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Ensemble methods in machine learning

Ensemble methods combine multiple machine learning models to obtain better predictive performance than from any individual model. There are two main types of ensemble methods: sequential (e.g AdaBoost) where models are generated one after the other, and parallel (e.g Random Forest) where models are generated independently. Popular ensemble methods include bagging, boosting, and stacking. Bagging averages predictions from models trained on random samples of the data, while boosting focuses on correcting previous models' errors. Stacking trains a meta-model on predictions from other models to produce a final prediction.

Support Vector Machines

This document summarizes support vector machines (SVMs), a machine learning technique for classification and regression. SVMs find the optimal separating hyperplane that maximizes the margin between positive and negative examples in the training data. This is achieved by solving a convex optimization problem that minimizes a quadratic function under linear constraints. SVMs can perform non-linear classification by implicitly mapping inputs into a higher-dimensional feature space using kernel functions. They have applications in areas like text categorization due to their ability to handle high-dimensional sparse data.

03 Machine Learning Linear Algebra

The document provides an introduction to linear algebra concepts for machine learning. It defines vectors as ordered tuples of numbers that express magnitude and direction. Vector spaces are sets that contain all linear combinations of vectors. Linear independence and basis of vector spaces are discussed. Norms measure the magnitude of a vector, with examples given of the 1-norm and 2-norm. Inner products measure the correlation between vectors. Matrices can represent linear operators between vector spaces. Key linear algebra concepts such as trace, determinant, and matrix decompositions are outlined for machine learning applications.

Deep learning - A Visual Introduction

It’s long ago, approx. 30 years, since AI was not only a topic for Science-Fiction writers, but also a major research field surrounded with huge hopes and investments. But the over-inflated expectations ended in a subsequent crash and followed by a period of absent funding and interest – the so-called AI winter. However, the last 3 years changed everything – again. Deep learning, a machine learning technique inspired by the human brain, successfully crushed one benchmark after another and tech companies, like Google, Facebook and Microsoft, started to invest billions in AI research. “The pace of progress in artificial general intelligence is incredible fast” (Elon Musk – CEO Tesla & SpaceX) leading to an AI that “would be either the best or the worst thing ever to happen to humanity” (Stephen Hawking – Physicist).
What sparked this new Hype? How is Deep Learning different from previous approaches? Are the advancing AI technologies really a threat for humanity? Let’s look behind the curtain and unravel the reality. This talk will explore why Sundar Pichai (CEO Google) recently announced that “machine learning is a core transformative way by which Google is rethinking everything they are doing” and explain why "Deep Learning is probably one of the most exciting things that is happening in the computer industry” (Jen-Hsun Huang – CEO NVIDIA).
Either a new AI “winter is coming” (Ned Stark – House Stark) or this new wave of innovation might turn out as the “last invention humans ever need to make” (Nick Bostrom – AI Philosoph). Or maybe it’s just another great technology helping humans to achieve more.

Supervised and unsupervised learning

This document discusses and provides examples of supervised and unsupervised learning. Supervised learning involves using labeled training data to learn relationships between inputs and outputs and make predictions. An example is using data on patients' attributes to predict the likelihood of a heart attack. Unsupervised learning involves discovering hidden patterns in unlabeled data by grouping or clustering items with similar attributes, like grouping fruits by color without labels. The goal of supervised learning is to build models that can make predictions when new examples are presented.

Deep Learning Explained

This document summarizes Melanie Swan's presentation on deep learning. It began with defining key deep learning concepts and techniques, including neural networks, supervised vs. unsupervised learning, and convolutional neural networks. It then explained how deep learning works by using multiple processing layers to extract higher-level features from data and make predictions. Deep learning has various applications like image recognition and speech recognition. The presentation concluded by discussing how deep learning is inspired by concepts from physics and statistical mechanics.

boosting algorithm

This presentation provides an overview of boosting approaches for classification problems. It discusses combining classifiers through bagging and boosting to create stronger classifiers. The AdaBoost algorithm is explained in detail, including its training and classification phases. An example is provided to illustrate how AdaBoost works over multiple rounds, increasing the weights of misclassified examples to improve classification accuracy. In conclusion, AdaBoost is highlighted as an effective approach for classification problems where misclassification has severe consequences by producing highly accurate strong classifiers.

Support Vector Machine - How Support Vector Machine works | SVM in Machine Le...

This document discusses support vector machines (SVM) and provides an example of using SVM for classification. It begins with common applications of SVM like face detection and image classification. It then provides an overview of SVM, explaining how it finds the optimal separating hyperplane between two classes by maximizing the margin between them. An example demonstrates SVM by classifying people as male or female based on height and weight data. It also discusses how kernels can be used to handle non-linearly separable data. The document concludes by showing an implementation of SVM on a zoos dataset to classify animals as crocodiles or alligators.

Machine Learning with Decision trees

Decision tree is a type of supervised learning algorithm (having a pre-defined target variable) that is mostly used in classification problems. It is a tree in which each branch node represents a choice between a number of alternatives, and each leaf node represents a decision.

Ensemble learning

Ensemble Learning is a technique that creates multiple models and then combines them to produce improved results.
Ensemble learning usually produces more accurate solutions than a single model would.

Decision Trees

Decision Trees

Deep neural networks

Deep neural networks

Feedforward neural network

Feedforward neural network

Perceptron (neural network)

Perceptron (neural network)

Support vector machines (svm)

Support vector machines (svm)

Convolutional Neural Networks (CNN)

Convolutional Neural Networks (CNN)

Decision trees in Machine Learning

Decision trees in Machine Learning

Cnn

Cnn

Activation function

Activation function

Random Forest Algorithm - Random Forest Explained | Random Forest In Machine ...

Random Forest Algorithm - Random Forest Explained | Random Forest In Machine ...

Ensemble methods in machine learning

Ensemble methods in machine learning

Support Vector Machines

Support Vector Machines

03 Machine Learning Linear Algebra

03 Machine Learning Linear Algebra

Deep learning - A Visual Introduction

Deep learning - A Visual Introduction

Supervised and unsupervised learning

Supervised and unsupervised learning

Deep Learning Explained

Deep Learning Explained

boosting algorithm

boosting algorithm

Support Vector Machine - How Support Vector Machine works | SVM in Machine Le...

Support Vector Machine - How Support Vector Machine works | SVM in Machine Le...

Machine Learning with Decision trees

Machine Learning with Decision trees

Ensemble learning

Ensemble learning

Spectral clustering - Houston ML Meetup

This document outlines the roadmap and agenda for a machine learning meetup covering clustering algorithms. The meetup will include sessions on k-means clustering, DBSCAN, hierarchical clustering, mean shift, spectral clustering and dimension reduction. Spectral clustering will be covered in two sessions focusing on the mathematical foundations and applications in computer vision. The meetup aims to provide an overview of machine learning techniques and their applications in domains such as business analytics, recommendation systems, natural language processing and the energy industry.

Kaggle nlp approaches

The document discusses various natural language processing (NLP) approaches used on Kaggle competitions, including text classification challenges like Jigsaw toxic comment classification and regression challenges like Mercari Price Suggestion. It provides summaries of top approaches for each competition, such as logistic regression with character n-grams for Jigsaw and LightGBM for Mercari. Winning approaches often involve extensive feature engineering and ensemble methods like stacking. Common deep learning models tested include LSTMs, GRUs, and convolutional neural networks.

Classification of Grasp Patterns using sEMG

This document summarizes research on classifying grasp patterns using surface electromyography (sEMG) data. The goal was to build a classification model that identifies spherical and tip grasps. A male subject performed each grasp type 100 times daily for 3 days, providing 600 total instances. A hidden Markov model was used to classify the grasps, with 90% of data for training and 10% for testing. The model achieved 73.3% overall accuracy, with higher accuracy for spherical grasps. Suggestions for improving the model included adding more features and stratifying the training/test sets.

Apache con big data 2015 - Data Science from the trenches

ApacheBigData - Budapest, 2015
Data Science from the trenches
What are the issues?
How to select best algorithm?
How to tune?
What are the problems with visualization?
How does Zeppelin help

Introduction to Recurrent Neural Network

Basic concepts of RNN and introduction to Long short term memory network; Presented at Houston Machine Learning meetup.

Saturn: Joint Optimization for Large-Model Deep Learning

Large models such as GPT-3 & ChatGPT have transformed deep learning (DL), powering applications that have captured the public’s imagination. These models are rapidly being adopted across domains for analytics on various modalities, often by finetuning pre-trained base models. Such models need multiple GPUs due to both their size and computational load, driving the development of a bevy of “model parallelism” techniques & tools. Navigating such parallelism choices, however, is a new burden for end users of DL such as data scientists, domain scientists, etc. who may lack the necessary systems knowhow. The need for model selection, which leads to many models to train due to hyper-parameter tuning or layer-wise finetuning, compounds the situation with two more burdens: resource apportioning and scheduling. In this work, we tackle these three burdens for DL users in a unified manner by formalizing them as a joint problem that we call SPASE: Select a Parallelism, Allocate resources, and Schedule. We propose a new information system architecture to tackle the SPASE problem holistically, representing a key step toward enabling wider adoption of large DL models. We devise an extensible template for existing parallelism schemes and combine it with an automated empirical profiler for runtime estimation. We then formulate SPASE as an MILP. We find that direct use of an MILP-solver is significantly more effective than several baseline heuristics.We optimize the system runtime further with an introspective scheduling approach. We implement all these techniques into a new data system we call Saturn. Experiments with benchmark DL workloads show that Saturn achieves 39-49% lower model selection runtimes than typical current DL practice.

Saturn - UCSD CNS Research Review

Saturn is a new system for large-model training. This presentation was delivered at UCSD's annual Center for Networked Systems Research Review.

Prediction as a service with ensemble model in SparkML and Python ScikitLearn

Watch the recording of the speech done at Spark Summit Brussles 2016 here:
https://www.youtube.com/watch?v=wyfTjd9z1sY
Data Science with SparkML on DataBricks is a perfect platform for application of Ensemble Learning on massive a scale. This presentation describes Prediction-as-a-Service platform which can predict trends on 1 billion observed prices daily. In order to train ensemble model on a multivariate time series in thousands/millions dimensional space, one has to fragment the whole space into subspaces which exhibit a significant similarity. In order to achieve this, the vastly sparse space has to undergo dimensionality reduction into a parameters space which then is used to cluster the observations. The data in the resulting clusters is modeled in parallel using machine learning tools capable of coefficient estimation at the massive scale (SparkML and Scikit Learn). The estimated model coefficients are stored in a database to be used when executing predictions on demand via a web service. This approach enables training models fast enough to complete the task within a couple of hours, allowing daily or even real time updates of the coefficients. The above machine learning framework is used to predict the airfares used as support tool for the airline Revenue Management systems.

Machine Learning for Everyone

This is an introductory workshop for machine learning. Introduced machine learning tasks such as supervised learning, unsupervised learning and reinforcement learning.

Spark Summit EU talk by Josef Habdank

Prediction as a Service with Ensemble Model Trained in SparkML on 1 billion Observed Flight Prices Daily

An overview of gradient descent optimization algorithms.pdf

This document provides an overview of gradient descent optimization algorithms. It discusses various gradient descent variants including batch gradient descent, stochastic gradient descent (SGD), and mini-batch gradient descent. It describes the trade-offs between these methods in terms of accuracy, time, and memory usage. The document also covers challenges with mini-batch gradient descent like choosing a proper learning rate. It then discusses commonly used optimization algorithms to address these challenges, including momentum, Nesterov accelerated gradient, Adagrad, Adadelta, RMSprop, and Adam. It provides visualizations to explain how momentum and Nesterov accelerated gradient work to help accelerate SGD.

MSCV Capstone Spring 2020 Presentation - RL for AD

The document describes research on using reinforcement learning for self-driving cars. It discusses using the Soft Actor Critic algorithm to train an agent in a simulated environment. Experiments are conducted in navigation tasks with and without dynamic actors. The agent is able to complete the simple navigation task but struggles in the more complex task with actors. Future work focuses on improving algorithm stability and using image inputs instead of a manual state space.

Training Neural Networks

Part 2 of the Deep Learning Fundamentals Series, this session discusses Tuning Training (including hyperparameters, overfitting/underfitting), Training Algorithms (including different learning rates, backpropagation), Optimization (including stochastic gradient descent, momentum, Nesterov Accelerated Gradient, RMSprop, Adaptive algorithms - Adam, Adadelta, etc.), and a primer on Convolutional Neural Networks. The demos included in these slides are running on Keras with TensorFlow backend on Databricks.

Session-aware Linear Item-Item Models for Session-based Recommendation (WWW 2...

This is the official slide for the WWW 2021 paper: Session-aware Linear Item-Item Models for Session-based Recommendation
If you have any questions, please contact zxcvxd@skku.edu.

GANs for Anti Money Laundering

The document discusses using generative adversarial networks (GANs) to improve anti-money laundering (AML) detection. It describes training a GAN on a large transaction dataset using Spark for feature engineering and TensorFlow. The GAN was able to classify transactions as either suspected of money laundering or clean. It also discusses challenges of training GANs, such as mode collapse, and techniques to address them like using multiple generators. Finally, it proposes candidate features for an AML model, such as graph-based, frequency, amount, time-since, and velocity-change features.

Data mining with Weka

This document provides an overview of a hands-on tutorial on using the open-source data mining toolbox Weka. The tutorial introduces the basic functionality of Weka, including how to load datasets in ARFF format, explore and visualize data, run machine learning algorithms for classification and clustering, and understand the resulting models. It also briefly discusses data mining and machine learning concepts like supervised vs. unsupervised learning, evaluation metrics, and common algorithms like decision trees.

Scaling Face Recognition with Big Data - Key Notes at DevTalks Bucharest 2017

A walk through the challenge, opportunities and particularities of designing, building and training the VisageCloud face recognition architecture.

InfoEducatie - Face Recognition Architecture

Scaling Face Recognition with Big Data discusses how to scale machine learning for face recognition. It covers how to learn from data using techniques like convolutional neural networks and preparing data through cleaning, normalization and filtering. Defining learning objectives like classification, clustering and identification is also important. When scaling learning, techniques like using GPUs and partitioning data across servers can be effective. Common challenges like local optima and data biases must also be addressed through evaluation against benchmarks. The document outlines VisageCloud's architecture and use cases for scaling face recognition through a processing pipeline and partitioning data across application and database layers.

Efficient Online Testing for DNN-Enabled Systems using Surrogate-Assisted and...

This document proposes SAMOTA, a surrogate-assisted many-objective optimization approach for online testing of DNN-enabled systems. SAMOTA uses global and local surrogate models to replace expensive function evaluations. It clusters local data points and builds individual surrogate models for each cluster, rather than one model for all data. An evaluation on a DNN-enabled autonomous driving system shows SAMOTA achieves better test effectiveness and efficiency than alternative approaches, and clustering local data points leads to more effective local searches than using a single local model. SAMOTA is an effective method for online testing of complex DNN systems.

"Energy-efficient Hardware for Embedded Vision and Deep Convolutional Neural ...

"Energy-efficient Hardware for Embedded Vision and Deep Convolutional Neural ...Edge AI and Vision Alliance

For the full video of this presentation, please visit:
http://www.embedded-vision.com/platinum-members/embedded-vision-alliance/embedded-vision-training/videos/pages/sept-2016-member-meeting-mit
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Vivienne Sze, Assistant Professor at MIT, delivers the presentation "Energy-efficient Hardware for Embedded Vision and Deep Convolutional Neural Networks" at the September 2016 Embedded Vision Alliance Member Meeting. Sze describes the results of her team's recent research on optimized hardware for deep learning.Spectral clustering - Houston ML Meetup

Spectral clustering - Houston ML Meetup

Kaggle nlp approaches

Kaggle nlp approaches

Classification of Grasp Patterns using sEMG

Classification of Grasp Patterns using sEMG

Apache con big data 2015 - Data Science from the trenches

Apache con big data 2015 - Data Science from the trenches

Introduction to Recurrent Neural Network

Introduction to Recurrent Neural Network

Saturn: Joint Optimization for Large-Model Deep Learning

Saturn: Joint Optimization for Large-Model Deep Learning

Saturn - UCSD CNS Research Review

Saturn - UCSD CNS Research Review

Prediction as a service with ensemble model in SparkML and Python ScikitLearn

Prediction as a service with ensemble model in SparkML and Python ScikitLearn

Machine Learning for Everyone

Machine Learning for Everyone

Spark Summit EU talk by Josef Habdank

Spark Summit EU talk by Josef Habdank

An overview of gradient descent optimization algorithms.pdf

An overview of gradient descent optimization algorithms.pdf

MSCV Capstone Spring 2020 Presentation - RL for AD

MSCV Capstone Spring 2020 Presentation - RL for AD

Training Neural Networks

Training Neural Networks

Session-aware Linear Item-Item Models for Session-based Recommendation (WWW 2...

Session-aware Linear Item-Item Models for Session-based Recommendation (WWW 2...

GANs for Anti Money Laundering

GANs for Anti Money Laundering

Data mining with Weka

Data mining with Weka

Scaling Face Recognition with Big Data - Key Notes at DevTalks Bucharest 2017

Scaling Face Recognition with Big Data - Key Notes at DevTalks Bucharest 2017

InfoEducatie - Face Recognition Architecture

InfoEducatie - Face Recognition Architecture

Efficient Online Testing for DNN-Enabled Systems using Surrogate-Assisted and...

Efficient Online Testing for DNN-Enabled Systems using Surrogate-Assisted and...

"Energy-efficient Hardware for Embedded Vision and Deep Convolutional Neural ...

"Energy-efficient Hardware for Embedded Vision and Deep Convolutional Neural ...

Kaggle winning solutions: Retail Sales Forecasting

This document summarizes several winning solutions from Kaggle competitions related to retail sales forecasting. It describes the data and metrics used in the competitions and highlights some common techniques from top solutions, including feature engineering of recent and temporal data, using gradient boosted trees and ensembles of models, and incorporating additional contextual data like weather and promotions.

Basics of Dynamic programming

Richard Bellman coined the term "dynamic programming" to describe his mathematical research at RAND Corporation. Dynamic programming is a method for solving complex problems by breaking them down into simpler subproblems. The document provides examples of using dynamic programming to solve the Fibonacci sequence, longest common subsequence, wildcard matching, and matrix chain multiplication problems. It also discusses using dynamic programming and hidden Markov models for part-of-speech tagging via the Viterbi algorithm.

Walking through Tensorflow 2.0

This document provides an overview of TensorFlow 2.0 and discusses several key features:
- TensorFlow 2.0 allows for deployment anywhere and supports eager execution for interactive development.
- Keras APIs can be used for both symbolic and imperative model building. Estimators provide high-level tools for working with models at scale.
- TensorFlow Hub contains pre-trained models that can be used for transfer learning. Examples of image and text models are listed.
- Custom models can be built using GradientTape for automatic differentiation and custom training loops. Data can be loaded from files, datasets, or TensorFlow Datasets.

Practical contextual bandits for business

This document summarizes Yan Xu's presentation on practical applications of multi-armed bandits. Bandits can be used for personalized recommendation, such as recommending news articles, by balancing exploration of new articles with exploitation of known good articles. Amazon's bandit algorithm allows for real-time optimization of multiple variables by modeling interactions between variables. The algorithm was able to increase website conversion by 21% after a single week of optimization.

Introduction to Multi-armed Bandits

This document discusses various algorithms for multi-armed bandit problems including k-armed bandits, action value methods like epsilon-greedy, tracking non-stationary problems, optimistic initial values, upper confidence bound action selection, gradient bandit algorithms, contextual bandits, and Thomson sampling. The k-armed bandit problem involves choosing actions to maximize reward over time without knowing the expected reward of each action. The document outlines methods for balancing exploration of unknown actions with exploitation of best known actions.

A Data-Driven Question Generation Model for Educational Content - by Jack Wang

Deep learning workshop at Houston machine learning meetup. Video at: https://youtu.be/8fmcn7ull8M?t=3

Deep Learning Approach in Characterizing Salt Body on Seismic Images - by Zhe...

Deep learning workshop at Houston machine learning meetup. Video at: https://youtu.be/8fmcn7ull8M?t=1922

Deep Hierarchical Profiling & Pattern Discovery: Application to Whole Brain R...

Deep learning workshop at Houston machine learning meetup. Video at: https://youtu.be/bAkBGKF2s2I?t=19

Detecting anomalies on rotating equipment using Deep Stacked Autoencoders - b...

Deep learning workshop at Houston machine learning meetup. Video uploaded: https://youtu.be/bAkBGKF2s2I?t=2320

Introduction to Autoencoders

The document provides an introduction and overview of auto-encoders, including their architecture, learning and inference processes, and applications. It discusses how auto-encoders can learn hierarchical representations of data in an unsupervised manner by compressing the input into a code and then reconstructing the output from that code. Sparse auto-encoders and stacking multiple auto-encoders are also covered. The document uses handwritten digit recognition as an example application to illustrate these concepts.

State of enterprise data science

Sr. Architect Pradeep Reddy, from Qubole, presents the state of Data Science in the enterprise industries today, followed by deep dive of an end-to-end real world machine learning use case. We'll explore the best practices and challenges of big data operations when developing new machine learning features and advanced analytics products at scale in the cloud.

Long Short Term Memory

The document provides an overview of LSTM (Long Short-Term Memory) networks. It first reviews RNNs (Recurrent Neural Networks) and their limitations in capturing long-term dependencies. It then introduces LSTM networks, which address this issue using forget, input, and output gates that allow the network to retain information for longer. Code examples are provided to demonstrate how LSTM remembers information over many time steps. Resources for further reading on LSTMs and RNNs are listed at the end.

Deep Feed Forward Neural Networks and Regularization

Deep feedforward networks use regularization techniques like L2/L1 regularization, dropout, batch normalization, and early stopping to reduce overfitting. They employ techniques like data augmentation to increase the size and variability of training datasets. Backpropagation allows information about the loss to flow backward through the network to efficiently compute gradients and update weights with gradient descent.

Linear algebra and probability (Deep Learning chapter 2&3)

Linear algebra and probability concepts are summarized in 3 sentences:
Scalars, vectors, matrices, and tensors are introduced as the basic components of linear algebra. Common linear algebra operations like transpose, addition, and multiplication are described. Probability concepts such as random variables, probability distributions, moments, and the central limit theorem are covered to lay the foundation for understanding deep learning techniques.

HML: Historical View and Trends of Deep Learning

The document provides a historical view and trends of deep learning. It discusses that deep learning models have evolved in several waves since the 1940s, with key developments including the backpropagation algorithm in 1986 and deep belief networks with pretraining in 2006. Current trends include growing datasets, increasing numbers of neurons and connections per neuron, and higher accuracy on tasks involving vision, NLP and games. Research trends focus on generative models, domain alignment, meta-learning, using graphs as inputs, and program induction.

Secrets behind AlphaGo

This document discusses deep reinforcement learning and how it was applied in AlphaGo to master the game of Go. It provides an overview of deep learning, reinforcement learning, and how AlphaGo combined the two approaches. AlphaGo used deep neural networks to mimic human expert moves and play games against itself to estimate win probabilities. It had a policy network to choose moves and a value network to estimate game outcomes. Through deep reinforcement learning, AlphaGo was able to achieve superhuman performance at the game of Go.

Convolutional neural network

Deep learning, Convolutional neural network presented by Hengyang Lu at Houston machine learning meetup

Introduction to Neural Network

The document summarizes a presentation on building artificial neural networks. It discusses an overview of machine learning algorithms that will be covered in upcoming sessions, including supervised and unsupervised learning methods as well as deep learning. It then provides details on feedforward neural networks, including their structure, how data is fed through the network, and how weights are learned through backpropagation and gradient descent. Applications discussed include voice recognition, object recognition, conversation bots, auto-driving cars, and gaming.

Nonlinear dimension reduction

The document summarizes Yan Xu's upcoming presentation at the Houston Machine Learning Meetup on dimension reduction techniques. Yan will cover linear methods like PCA and nonlinear methods such as ISOMAP, LLE, and t-SNE. She will explain how these methods work, including preserving variance with PCA, using geodesic distances with ISOMAP, and modeling local neighborhoods with LLE and t-SNE. Yan will also demonstrate these methods on a dataset of handwritten digits. The meetup is part of a broader roadmap of machine learning topics that will be covered in future sessions.

Mean shift and Hierarchical clustering

Mean shift clustering finds clusters by locating peaks in the probability density function of the data. It iteratively moves data points to the mean of nearby points until convergence. Hierarchical clustering builds clusters gradually by either merging or splitting clusters at each step. There are two types: divisive which splits clusters, and agglomerative which merges clusters. Agglomerative clustering starts with each point as a cluster and iteratively merges the closest pair of clusters until all are merged based on a chosen linkage method like complete or average linkage. The choice of distance metric and linkage method impacts the resulting clusters.

Kaggle winning solutions: Retail Sales Forecasting

Kaggle winning solutions: Retail Sales Forecasting

Basics of Dynamic programming

Basics of Dynamic programming

Walking through Tensorflow 2.0

Walking through Tensorflow 2.0

Practical contextual bandits for business

Practical contextual bandits for business

Introduction to Multi-armed Bandits

Introduction to Multi-armed Bandits

A Data-Driven Question Generation Model for Educational Content - by Jack Wang

A Data-Driven Question Generation Model for Educational Content - by Jack Wang

Deep Learning Approach in Characterizing Salt Body on Seismic Images - by Zhe...

Deep Learning Approach in Characterizing Salt Body on Seismic Images - by Zhe...

Deep Hierarchical Profiling & Pattern Discovery: Application to Whole Brain R...

Deep Hierarchical Profiling & Pattern Discovery: Application to Whole Brain R...

Detecting anomalies on rotating equipment using Deep Stacked Autoencoders - b...

Detecting anomalies on rotating equipment using Deep Stacked Autoencoders - b...

Introduction to Autoencoders

Introduction to Autoencoders

State of enterprise data science

State of enterprise data science

Long Short Term Memory

Long Short Term Memory

Deep Feed Forward Neural Networks and Regularization

Deep Feed Forward Neural Networks and Regularization

Linear algebra and probability (Deep Learning chapter 2&3)

Linear algebra and probability (Deep Learning chapter 2&3)

HML: Historical View and Trends of Deep Learning

HML: Historical View and Trends of Deep Learning

Secrets behind AlphaGo

Secrets behind AlphaGo

Convolutional neural network

Convolutional neural network

Introduction to Neural Network

Introduction to Neural Network

Nonlinear dimension reduction

Nonlinear dimension reduction

Mean shift and Hierarchical clustering

Mean shift and Hierarchical clustering

Science-Technology Quiz (School Quiz 2024)

Science-Technology Quiz (School Quiz 2024)

antenna-fundamentals an introductions to basics

fundamentals of EM

Hydrogen sulfide and metal-enriched atmosphere for a Jupiter-mass exoplanet

We observed two transits of HD 189733b in JWST program 1633 using JWST
NIRCam grism F444W and F322W2 filters on August 25 and 29th 2022. The first
visit with F444W used SUBGRISM64 subarray lasting 7877 integrations with 4
BRIGHT1 groups per integration. Each effective integration is 2.4s for a total effective exposure time of 18780.9s and a total exposure duration of 21504.2s (∼6 hrs)
including overhead. The second visit with F322W2 used SUBGRISM64 subarray
lasting 10437 integrations with 3 BRIGHT1 groups per integration. Each effective
integration is 1.7s for a total effective exposure time of 17774.7s and a total exposure
duration of 21383.1s (∼6 hrs) including overhead. The transit duration of HD189733
b is ∼1.8 hrs and both observations had additional pre-ingress baseline relative to
post-egress baseline in anticipating the potential ramp systematics at the beginning
of the exposure from NIRCam infrared detectors.

20240710 ACMJ Diagrams Set 3.docx . Apache, Csharp, Mysql, Javascript stack a...

Diagrams of made early prototypes of ACMJ components, to do with electricity.

Review Article:- A REVIEW ON RADIOISOTOPES IN CANCER THERAPY

A REVIEW ON RADIOISOTOPES IN CANCER THERAPY

largeintestinepathologiesconditions-240627071428-3c936a47 (2).pptx

Large Intestine, movements

Direct instructions, towards hundred fold yield,layering,budding,grafting,pla...

Fertility, plants, layering, growth, health of seeds

Gasification and Pyrolyssis of plastic Waste under a Circular Economy perpective

Review on Gasification LCA. Presentation given by Cecilia Hofmann at Advanced Recycling Conference in Cologne, 2023.

The cryptoterrestrial hypothesis: A case for scientific openness to a conceal...

Recent years have seen increasing public attention and indeed concern regarding Unidentified
Anomalous Phenomena (UAP). Hypotheses for such phenomena tend to fall into two classes: a
conventional terrestrial explanation (e.g., human-made technology), or an extraterrestrial explanation
(i.e., advanced civilizations from elsewhere in the cosmos). However, there is also a third minority
class of hypothesis: an unconventional terrestrial explanation, outside the prevailing consensus view of
the universe. This is the ultraterrestrial hypothesis, which includes as a subset the “cryptoterrestrial”
hypothesis, namely the notion that UAP may reflect activities of intelligent beings concealed in stealth
here on Earth (e.g., underground), and/or its near environs (e.g., the moon), and/or even “walking
among us” (e.g., passing as humans). Although this idea is likely to be regarded sceptically by most
scientists, such is the nature of some UAP that we argue this possibility should not be summarily
dismissed, and instead deserves genuine consideration in a spirit of epistemic humility and openness.

morphology and reproduction of Thuja.pptx

morphology anatomy and reproduction of Thuja.pptx

seed drying lecture, different types of dryers

Drying of seed

BIOPHYSICS Interactions of molecules in 3-D space-determining binding and.pptx

Interaction of molecules

Collaborative Team Recommendation for Skilled Users: Objectives, Techniques, ...

Collaborative team recommendation involves selecting users with certain skills to form a team who will, more likely than not, accomplish a complex task successfully. To automate the traditionally tedious and error-prone manual process of team formation, researchers from several scientific spheres have proposed methods to tackle the problem. In this tutorial, while providing a taxonomy of team recommendation works based on their algorithmic approaches to model skilled users in collaborative teams, we perform a comprehensive and hands-on study of the graph-based approaches that comprise the mainstream in this field, then cover the neural team recommenders as the cutting-edge class of approaches. Further, we provide unifying definitions, formulations, and evaluation schema. Last, we introduce details of training strategies, benchmarking datasets, and open-source tools, along with directions for future works.

Possible Anthropogenic Contributions to the LAMP-observed Surficial Icy Regol...

This work assesses the potential of midsized and large human landing systems to deliver water from their exhaust
plumes to cold traps within lunar polar craters. It has been estimated that a total of between 2 and 60 T of surficial
water was sensed by the Lunar Reconnaissance Orbiter Lyman Alpha Mapping Project on the floors of the larger
permanently shadowed south polar craters. This intrinsic surficial water sensed in the far-ultraviolet is thought to be
in the form of a 0.3%–2% icy regolith in the top few hundred nanometers of the surface. We find that the six past
Apollo Lunar Module midlatitude landings could contribute no more than 0.36 T of water mass to this existing,
intrinsic surficial water in permanently shadowed regions (PSRs). However, we find that the Starship landing
plume has the potential, in some cases, to deliver over 10 T of water to the PSRs, which is a substantial fraction
(possibly >20%) of the existing intrinsic surficial water mass. This anthropogenic contribution could possibly
overlay and mix with the naturally occurring icy regolith at the uppermost surface. A possible consequence is that
the origin of the intrinsic surficial icy regolith, which is still undetermined, could be lost as it mixes with the
extrinsic anthropogenic contribution. We suggest that existing and future orbital and landed assets be used to
examine the effect of polar landers on the cold traps within PSRs

Phytoremediation: Harnessing Nature's Power with Phytoremediation

This document provides an overview of phytoremediation, which uses plants to remove contaminants from soil, sediment, or water. It discusses the need for new remediation techniques, describes various phytoremediation processes like phytoextraction and rhizofiltration, and covers important concepts like hyperaccumulators, biotechnology applications, case studies, and advantages/limitations. The author aims to explain the mechanisms, history, types of plants used, and future research directions of this eco-friendly approach to environmental cleanup.

Travis Hills of Minnesota Sets a New Standard in Carbon Credits With Livestoc...

Travis Hills of Minnesota is revolutionizing the carbon credit industry with a unique approach that underscores both transparency and value. Unlike conventional carbon credits, Travis ensures that each credit generated by Livestock Water & Energy is meticulously verified and validated. Each credit is assigned a distinct serial number, enhancing its authenticity and marketability. This unique feature provides traceability and accountability, instilling confidence among buyers and investors. Moreover, the rigorous verification process ensures that the credits meet stringent international standards, making them highly sought after in global carbon markets.

The Dynamical Origins of the Dark Comets and a Proposed Evolutionary Track

So-called ‘dark comets’ are small, morphologically inactive near-Earth objects
(NEOs) that exhibit nongravitational accelerations inconsistent with radiative
effects. These objects exhibit short rotational periods (minutes to hours), where
measured. We find that the strengths required to prevent catastrophic disintegration are consistent with those measured in cometary nuclei and expected in
rubble pile objects. We hypothesize that these dark comets are the end result
of a rotational fragmentation cascade, which is consistent with their measured
physical properties. We calculate the predicted size-frequency distribution for
objects evolving under this model. Using dynamical simulations, we further
demonstrate that the majority of these bodies originated from the 𝜈6
resonance,
implying the existence of volatiles in the current inner main belt. Moreover, one of
the dark comets, (523599) 2003 RM, likely originated from the outer main belt,
although a JFC origin is also plausible. These results provide strong evidence
that volatiles from a reservoir in the inner main belt are present in the near-Earth
environment.

MCQ in Electrostatics. for class XII pptx

Physics Multiple choice questions and answers with explanation. (Class XII Physics TN State board)

poikilocytosis 23765437865210857453257844.pptx

Poikilocytosis, different types, abnormalutirs

Science-Technology Quiz (School Quiz 2024)

Science-Technology Quiz (School Quiz 2024)

antenna-fundamentals an introductions to basics

antenna-fundamentals an introductions to basics

Hydrogen sulfide and metal-enriched atmosphere for a Jupiter-mass exoplanet

Hydrogen sulfide and metal-enriched atmosphere for a Jupiter-mass exoplanet

20240710 ACMJ Diagrams Set 3.docx . Apache, Csharp, Mysql, Javascript stack a...

20240710 ACMJ Diagrams Set 3.docx . Apache, Csharp, Mysql, Javascript stack a...

Review Article:- A REVIEW ON RADIOISOTOPES IN CANCER THERAPY

Review Article:- A REVIEW ON RADIOISOTOPES IN CANCER THERAPY

largeintestinepathologiesconditions-240627071428-3c936a47 (2).pptx

largeintestinepathologiesconditions-240627071428-3c936a47 (2).pptx

Direct instructions, towards hundred fold yield,layering,budding,grafting,pla...

Direct instructions, towards hundred fold yield,layering,budding,grafting,pla...

Gasification and Pyrolyssis of plastic Waste under a Circular Economy perpective

Gasification and Pyrolyssis of plastic Waste under a Circular Economy perpective

The cryptoterrestrial hypothesis: A case for scientific openness to a conceal...

The cryptoterrestrial hypothesis: A case for scientific openness to a conceal...

morphology and reproduction of Thuja.pptx

morphology and reproduction of Thuja.pptx

seed drying lecture, different types of dryers

seed drying lecture, different types of dryers

BIOPHYSICS Interactions of molecules in 3-D space-determining binding and.pptx

BIOPHYSICS Interactions of molecules in 3-D space-determining binding and.pptx

Collaborative Team Recommendation for Skilled Users: Objectives, Techniques, ...

Collaborative Team Recommendation for Skilled Users: Objectives, Techniques, ...

Possible Anthropogenic Contributions to the LAMP-observed Surficial Icy Regol...

Possible Anthropogenic Contributions to the LAMP-observed Surficial Icy Regol...

Phytoremediation: Harnessing Nature's Power with Phytoremediation

Phytoremediation: Harnessing Nature's Power with Phytoremediation

Adjusted NuGOweek 2024 Ghent programme flyer

Adjusted NuGOweek 2024 Ghent programme flyer

Travis Hills of Minnesota Sets a New Standard in Carbon Credits With Livestoc...

Travis Hills of Minnesota Sets a New Standard in Carbon Credits With Livestoc...

The Dynamical Origins of the Dark Comets and a Proposed Evolutionary Track

The Dynamical Origins of the Dark Comets and a Proposed Evolutionary Track

MCQ in Electrostatics. for class XII pptx

MCQ in Electrostatics. for class XII pptx

poikilocytosis 23765437865210857453257844.pptx

poikilocytosis 23765437865210857453257844.pptx

- 1. Optimization in Deep Learning Houston Machine Learning Deep Learning Series
- 2. How to get to the lake
- 3. Roadmap • Tour of machine learning algorithms (1 session) • Feature engineering (1 session) • Feature selection - Yan • Supervised learning (4 sessions) • Regression models -Yan • SVM and kernel SVM - Yan • Tree-based models - Dario • Bayesian method - Xiaoyang • Ensemble models - Yan • Unsupervised learning (3 sessions) • K-means clustering • DBSCAN - Cheng • Mean shift • Agglomerative clustering – Kunal • Spectral clustering – Yan • Dimension reduction for data visualization - Yan • Deep learning • Neural network - Yan • Convolutional neural network – Hengyang Lu • Recurrent neural networks – Yan • Hands-on session with deep nets - Yan Slides posted on: http://www.slideshare.net/xuyangela
- 4. More deep learning coming up! • Optimization in Deep learning (today’s session) • Behind AlphaGo • Mastering the game of Go with deep neural networks and tree search • Attention network • Application of Deep Learning and showcase
- 5. Outline • Gradient Descent • Stochastic Gradient Descent (SGD) • Variants of SGD • Use “momentum” • Nestrov’s Accelerated Gradient (NAG) • Adaptive Gradient (AdaGrad) • Root Mean Square Propagation (RMSProp) • Adaptive Moment Estimation (Adam)
- 8. Scaling to large N
- 9. Stochastic Gradient Descent (SGD)
- 10. Mini-batch SGD
- 12. SGD recommendation • Randomly shuffle training samples • Monitor training and validation error • Experiment learning rates using small sample of training set • Leverage sparsity of training samples • Varying learning rate:
- 13. Variants of SGD • Use “momentum” • Nestrov’s Accelerated Gradient (NAG) • Adaptive Gradient (AdaGrad) • Root Mean Square Propagation (RMSProp) • Adaptive Moment Estimation (Adam) Ref: https://moodle2.cs.huji.ac.il/nu15/pluginfile.php/316969/mod_resource/conte nt/1/adam_pres.pdf
- 17. The momentum method by Dr. Geoffrey Hinton https://www.youtube.com/watch?v=LdkkZglLZ0Q&list=PLoRl3Ht4JOcdU872GhiYWf6jwrk_SNhz9&index=27
- 18. SGD with momentum Start with 0.5
- 19. NAG (Nesterov’s Accelerated Gradient)
- 20. AdaGrad Adaptive learning rate: • weights that receive high gradients will have their effective learning rate reduced • weights that receive small or infrequent updates will have their effective learning rate increased
- 21. RMSProp
- 22. Adam
- 23. Adam
- 24. Comparisons of Different Optimization Methods
- 25. MINIST Comparisons of Different Optimization Methods
- 26. CIFAR-10 Comparisons of Different Optimization Methods
- 27. Summary of learning methods for DL https://www.youtube.com/watch?v=defQQqkXEfE&list=PLoRl3Ht4JOcdU872GhiYWf6jwrk_SNhz9&index=29 from:7:33
- 28. Try it out! From hands-on session: https://www.dropbox.com/s/92sckhnf1hjgjlo/CNN.zip?dl=0 model = Sequential() model.add(Conv2D(32, (3, 3), input_shape=input_shape)) model.add(Activation('relu')) model.add(MaxPooling2D(pool_size=(2, 2))) ……. model.add(Flatten()) model.add(Dense(64)) model.add(Activation('relu')) model.add(Dropout(0.5)) model.add(Dense(1)) model.add(Activation('sigmoid')) model.compile(loss='binary_crossentropy', optimizer='rmsprop', metrics=['accuracy']) Optimizer: SGD, RMSprop, Adagrad, Adam…. (https://keras.io/optimizers/)
- 29. Summary Full-batch GD SGD Momentum SGD NAG AdaGrad RMSProp Adam Speed up by momentum Adaptive learning rate
- 30. More deep learning coming up! • Optimization in Deep learning (today’s session) • Behind AlphaGo • Mastering the game of Go with deep neural networks and tree search • Attention network • Application of Deep Learning and showcase • Any proposal?
- 31. Thank you Slides will be posted at: http://www.slideshare.net/xuyangela Leave a group review please

- Perpendicular contour