How To Do A Project
•Download as PPT, PDF•
2 likes•3,402 views
The document provides guidance on how to plan and execute a project. It recommends first picking a title and defining the project scope. It then discusses performing requirements analysis, designing the development environment and overall system architecture, coding and testing the project, and managing the project schedule and resources. Finally, it provides some example project ideas and tools to support the development process.
Report
Share
Report
Share
![Pick up a Title ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)
Recommended
Challenges in Large Scale Machine Learning
When data size grows in terms of sample count, feature count and model parameter count, things go crazy. The slideshow presents an overview of what to expect and how to handle them.
Distributed deep learning
Slides for my short talk at work about distributed deep learning and recent trends about large size mini batch for SGD training.
MALT: Distributed Data-Parallelism for Existing ML Applications (Distributed ...
Machine learning methods, such as SVM and neural net- works, often improve their accuracy by using models with more parameters trained on large numbers of examples. Building such models on a single machine is often impracti- cal because of the large amount of computation required.
We introduce MALT, a machine learning library that inte- grates with existing machine learning software and provides data parallel machine learning. MALT provides abstractions for fine-grained in-memory updates using one-sided RDMA, limiting data movement costs during incremental model up- dates. MALT allows machine learning developers to specify the dataflow and apply communication and representation optimizations. Through its general-purpose API, MALT can be used to provide data-parallelism to existing ML appli- cations written in C++ and Lua and based on SVM, ma- trix factorization and neural networks. In our results, we show MALT provides fault tolerance, network efficiency and speedup to these applications.
Day 2 (Lecture 5): A Practitioner's Perspective on Building Machine Product i...
Day 2 (Lecture 5): A Practitioner's Perspective on Building Machine Product i...Aseda Owusua Addai-Deseh
Lecture on "A Practitioner's Perspective on Building Machine Product in Real Life" given by Richard Ackon, Software Engineer at mPharma.
Josh Patterson, Advisor, Skymind – Deep learning for Industry at MLconf ATL 2016
DL4J and DataVec for Enterprise Deep Learning Workflows: Applications in NLP, sensor processing (IoT), image processing, and audio processing have all emerged as prime deep learning applications. In this session we will take a look at a practical review of building practical and secure Deep Learning workflows in the enterprise. We’ll see how DL4J’s DataVec tool enables scalable ETL and vectorization pipelines to be created for a single machine or scale out to Spark on Hadoop. We’ll also see how Deep Networks such as Recurrent Neural Networks are able to leverage DataVec to more quickly process data for modeling.
Nikhil Garg, Engineering Manager, Quora at MLconf SF 2016
Building a Machine Learning Platform at Quora: Each month, over 100 million people use Quora to share and grow their knowledge. Machine learning has played a critical role in enabling us to grow to this scale, with applications ranging from understanding content quality to identifying users’ interests and expertise. By investing in a reusable, extensible machine learning platform, our small team of ML engineers has been able to productionize dozens of different models and algorithms that power many features across Quora.
In this talk, I’ll discuss the core ideas behind our ML platform, as well as some of the specific systems, tools, and abstractions that have enabled us to scale our approach to machine learning.
Recommended
Challenges in Large Scale Machine Learning
When data size grows in terms of sample count, feature count and model parameter count, things go crazy. The slideshow presents an overview of what to expect and how to handle them.
Distributed deep learning
Slides for my short talk at work about distributed deep learning and recent trends about large size mini batch for SGD training.
MALT: Distributed Data-Parallelism for Existing ML Applications (Distributed ...
Machine learning methods, such as SVM and neural net- works, often improve their accuracy by using models with more parameters trained on large numbers of examples. Building such models on a single machine is often impracti- cal because of the large amount of computation required.
We introduce MALT, a machine learning library that inte- grates with existing machine learning software and provides data parallel machine learning. MALT provides abstractions for fine-grained in-memory updates using one-sided RDMA, limiting data movement costs during incremental model up- dates. MALT allows machine learning developers to specify the dataflow and apply communication and representation optimizations. Through its general-purpose API, MALT can be used to provide data-parallelism to existing ML appli- cations written in C++ and Lua and based on SVM, ma- trix factorization and neural networks. In our results, we show MALT provides fault tolerance, network efficiency and speedup to these applications.
Day 2 (Lecture 5): A Practitioner's Perspective on Building Machine Product i...
Day 2 (Lecture 5): A Practitioner's Perspective on Building Machine Product i...Aseda Owusua Addai-Deseh
Lecture on "A Practitioner's Perspective on Building Machine Product in Real Life" given by Richard Ackon, Software Engineer at mPharma.
Josh Patterson, Advisor, Skymind – Deep learning for Industry at MLconf ATL 2016
DL4J and DataVec for Enterprise Deep Learning Workflows: Applications in NLP, sensor processing (IoT), image processing, and audio processing have all emerged as prime deep learning applications. In this session we will take a look at a practical review of building practical and secure Deep Learning workflows in the enterprise. We’ll see how DL4J’s DataVec tool enables scalable ETL and vectorization pipelines to be created for a single machine or scale out to Spark on Hadoop. We’ll also see how Deep Networks such as Recurrent Neural Networks are able to leverage DataVec to more quickly process data for modeling.
Nikhil Garg, Engineering Manager, Quora at MLconf SF 2016
Building a Machine Learning Platform at Quora: Each month, over 100 million people use Quora to share and grow their knowledge. Machine learning has played a critical role in enabling us to grow to this scale, with applications ranging from understanding content quality to identifying users’ interests and expertise. By investing in a reusable, extensible machine learning platform, our small team of ML engineers has been able to productionize dozens of different models and algorithms that power many features across Quora.
In this talk, I’ll discuss the core ideas behind our ML platform, as well as some of the specific systems, tools, and abstractions that have enabled us to scale our approach to machine learning.
10 more lessons learned from building Machine Learning systems
Presentation at #mlconf 2015 in San Francisco
Jean-François Puget, Distinguished Engineer, Machine Learning and Optimizatio...
Why Machine Learning Algorithms Fall Short (And What You Can Do About It): Many think that machine learning is all about the algorithms. Want a self-learning system? Get your data, start coding or hire a PhD that will build you a model that will stand the test of time. Of course we know that this is not enough. Models degrade over time, algorithms that work great on yesterday’s data may not be the best option, new data sources and types are made available. In short, your self-learning system may not be learning anything at all. In this session, we will examine how to overcome challenges in creating self-learning systems that perform better and are built to stand the test of time. We will show how to apply mathematical optimization algorithms that often prove superior to local optimization methods favored by typical machine learning applications and discuss why these methods can crate better results. We will also examine the role of smart automation in the context of machine learning and how smart automation can create self-learning systems that are built to last.
Funda Gunes, Senior Research Statistician Developer & Patrick Koch, Principal...
Local Search Optimization for Hyper-Parameter Tuning: Many machine learning algorithms are sensitive to their hyper-parameter settings, lacking good universal rule-of-thumb defaults. In this talk we discuss the use of black-box local search optimization (LSO) for machine learning hyper-parameter tuning. Viewed as a black-box objective function of hyper-parameters, machine learning algorithms create a difficult class of optimization problems. The corresponding objective functions involved tend to be nonsmooth, discontinuous, unpredictably computationally expensive, requiring support for both continuous, categorical, and integer variables. Further evaluations can fail for a variety of reasons such as early exits due to node failure or hitting max time. Additionally, not all hyper-parameter combinations are compatible (creating so called “hidden constraints”). In this context, we apply a parallel hybrid derivative-free optimization algorithm that can make progress despite these difficulties providing significantly improved results over default settings with minimal user interaction. Further, we will address efficient parallel paradigms for different types of machine learning problems, while exploring the importance of validation to avoid overfitting and emphasizing that even for small data problems, the need to perform cross validations can create computationally intense functions that benefit from a distributed/threaded environment.
Daniel Shank, Data Scientist, Talla at MLconf SF 2016
Neural Turing Machines: Perils and Promise: Daniel Shank is a Senior Data Scientist at Talla, a company developing a platform for intelligent information discovery and delivery. His focus is on developing machine learning techniques to handle various business automation tasks, such as scheduling, polls, expert identification, as well as doing work on NLP. Before joining Talla as the company’s first employee in 2015, Daniel worked with TechStars Boston and did consulting work for ThriveHive, a small business focused marketing company in Boston. He studied economics at the University of Chicago.
Melanie Warrick, Deep Learning Engineer, Skymind.io at MLconf SF - 11/13/15
Attention Neural Net Model Fundamentals: Neural networks have regained popularity over the last decade because they are demonstrating real world value in different applications (e.g. targeted advertising, recommender engines, Siri, self driving cars, facial recognition). Several model types are currently explored in the field with recurrent neural networks (RNN) and convolution neural networks (CNN) taking the top focus. The attention model, a recently developed RNN variant, has started to play a larger role in both natural language processing and image analysis research.
This talk will cover the fundamentals of the attention model structure and how its applied to visual and speech analysis. I will provide an overview of the model functionality and math including a high-level differentiation between soft and hard types. The goal is to give you enough of an understanding of what the model is, how it works and where to apply it.
Misha Bilenko, Principal Researcher, Microsoft at MLconf SEA - 5/01/15
Many Shades of Scale: Big Learning Beyond Big Data: In the machine learning research community, much of the attention devoted to ‘big data’ in recent years has been manifested as development of new algorithms and systems for distributed training on many examples. This focus has led to significant advances in the field, from basic but operational implementations on popular platforms to highly sophisticated prototypes in the literature. In the meantime, other aspects of scaling up learning have received relatively little attention, although they are often more pressing in practice. The talk will survey these less-studied facets of big learning: scaling to an extremely large number of features, to many components in predictive pipelines, and to multiple data scientists collaborating on shared experiments.
Deploying signature verification with deep learning
Presentation covered building a signature verification system and deploying it to production. This includes resources usage as well as how the model was picked.
Meetup held in Tokyo with Deep learning Otemachi.
Narayanan Sundaram, Research Scientist, Intel Labs at MLconf SF - 11/13/15
GraphMat: Bridging the Productivity-Performance Gap in Graph Analytics: With increasing interest in large-scale distributed graph analytics for machine learning and data mining, more data scientists and developers are struggling to achieve high performance without sacrificing productivity on large graph problems. In this talk, I will discuss our solution to this problem: GraphMat. Using generalized sparse matrix-based primitives, we are able to achieve performance that is very close to hand-optimized native code, while allowing users to write programs using the familiar vertex-centric programming paradigm. I will show how we optimized GraphMat to achieve this performance on distributed platforms and provide programming examples. We have integrated GraphMat with Apache Spark in a manner that allows the combination to outperform all other distributed graph frameworks. I will explain the reasons for this performance and show that our approach achieves very high hardware efficiency in both single-node and distributed environments using primitives that are applicable to many machine learning and HPC problems. GraphMat is open source software and available for download.
MLConf 2016 SigOpt Talk by Scott Clark
MLConf 2016 SigOpt Talk on Bayesian Global Optimization: Using Optimal Learning to Tune ML Models.
Deep learning with Keras
Interest in Deep Learning has been growing in the past few years. With advances in software and hardware technologies, Neural Networks are making a resurgence. With interest in AI based applications growing, and companies like IBM, Google, Microsoft, NVidia investing heavily in computing and software applications, it is time to understand Deep Learning better!
In this workshop, we will discuss the basics of Neural Networks and discuss how Deep Learning Neural networks are different from conventional Neural Network architectures. We will review a bit of mathematics that goes into building neural networks and understand the role of GPUs in Deep Learning. We will also get an introduction to Autoencoders, Convolutional Neural Networks, Recurrent Neural Networks and understand the state-of-the-art in hardware and software architectures. Functional Demos will be presented in Keras, a popular Python package with a backend in Theano and Tensorflow.
Feature Engineering
Tips & Tricks for Feature Engineering / Applied Machine Learning. This presentation was given as part of the Sao Paulo ML meetup.
Deeplearning on Hadoop @OSCON 2014
Distributed Deep Learning on Hadoop
Deep-learning is useful in detecting anomalies like fraud, spam and money laundering; identifying similarities to augment search and text analytics; predicting customer lifetime value and churn; recognizing faces and voices.
Deeplearning4j is an infinitely scalable deep-learning architecture suitable for Hadoop and other big-data structures. It includes a distributed deep-learning framework and a normal deep-learning framework; i.e. it runs on a single thread as well. Training takes place in the cluster, which means it can process massive amounts of data. Nets are trained in parallel via iterative reduce, and they are equally compatible with Java, Scala and Clojure. The distributed deep-learning framework is made for data input and neural net training at scale, and its output should be highly accurate predictive models.
The framework's neural nets include restricted Boltzmann machines, deep-belief networks, deep autoencoders, convolutional nets and recursive neural tensor networks.
CNN Quantization
For real world application, convolutional neural network(CNN) model can take more than 100MB of space and can be computationally too expensive. Therefore, there are multiple methods to reduce this complexity in the state of art. Ristretto is a plug-in to Caffe framework that employs several model approximation methods. For this projects, first a CNN model is trained for Cifar-10 dataset with Caffe, then Ristretto will be use to generate multiple approximated version of the trained model using different schemes. The goal of this projects is comparison of the models in terms of execution performance, model size and cache utilizations in the test or inference phase. The same steps are done with Tensorflow and Quantisation tool. The quantisation schemes of Tensorflow and Ristretto are then compared.
Data Workflows for Machine Learning - Seattle DAML
First public meetup at Twitter Seattle, for Seattle DAML:
http://www.meetup.com/Seattle-DAML/events/159043422/
We compare/contrast several open source frameworks which have emerged for Machine Learning workflows, including KNIME, IPython Notebook and related Py libraries, Cascading, Cascalog, Scalding, Summingbird, Spark/MLbase, MBrace on .NET, etc. The analysis develops several points for "best of breed" and what features would be great to see across the board for many frameworks... leading up to a "scorecard" to help evaluate different alternatives. We also review the PMML standard for migrating predictive models, e.g., from SAS to Hadoop.
General Tips for participating Kaggle Competitions
The slides of a talk at Spark Taiwan User Group to share my experience and some general tips for participating kaggle competitions.
Separating Hype from Reality in Deep Learning with Sameer Farooqui
Deep Learning is all the rage these days, but where does the reality of what Deep Learning can do end and the media hype begin? In this talk, I will dispel common myths about Deep Learning that are not necessarily true and help you decide whether you should practically use Deep Learning in your software stack.
I’ll begin with a technical overview of common neural network architectures like CNNs, RNNs, GANs and their common use cases like computer vision, language understanding or unsupervised machine learning. Then I’ll separate the hype from reality around questions like:
• When should you prefer traditional ML systems like scikit learn or Spark.ML instead of Deep Learning?
• Do you no longer need to do careful feature extraction and standardization if using Deep Learning?
• Do you really need terabytes of data when training neural networks or can you ‘steal’ pre-trained lower layers from public models by using transfer learning?
• How do you decide which activation function (like ReLU, leaky ReLU, ELU, etc) or optimizer (like Momentum, AdaGrad, RMSProp, Adam, etc) to use in your neural network?
• Should you randomly initialize the weights in your network or use more advanced strategies like Xavier or He initialization?
• How easy is it to overfit/overtrain a neural network and what are the common techniques to ovoid overfitting (like l1/l2 regularization, dropout and early stopping)?
Deep learning with keras
Keras is a high level framework that runs on top of AI library such as Tensorflow, Theano, or CNTK. The key feature of Keras is that it allow to switch out the underlying library without performing any code changes. Keras contains commonly used neural-network building blocks such as layers, optimizer, activation functions etc and keras has support for convolutional and recurrent neural networks. In addition keras contains datasets and some pre-trained deep learnig applications that make it easier to learn for beginners. Essentially Keras is democrasting deep learning by reducing barrier into deep learning.
Keras Tutorial For Beginners | Creating Deep Learning Models Using Keras In P...
** AI & Deep Learning Training: https://www.edureka.co/ai-deep-learning-with-tensorflow ** )
This Edureka Tutorial on "Keras Tutorial" (Deep Learning Blog Series: https://goo.gl/4zxMfU) provides you a quick and insightful tutorial on the working of Keras along with an interesting use-case! We will be checking out the following topics:
Agenda:
What is Keras?
Who makes Keras?
Who uses Keras?
What Makes Keras special?
Working principle of Keras
Keras Models
Understanding Execution
Implementing a Neural Network
Use-Case with Keras
Coding in Colaboratory
Session in a minute
Check out our Deep Learning blog series: https://bit.ly/2xVIMe1
Check out our complete Youtube playlist here: https://bit.ly/2OhZEpz
Follow us to never miss an update in the future.
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
More Related Content
What's hot
10 more lessons learned from building Machine Learning systems
Presentation at #mlconf 2015 in San Francisco
Jean-François Puget, Distinguished Engineer, Machine Learning and Optimizatio...
Why Machine Learning Algorithms Fall Short (And What You Can Do About It): Many think that machine learning is all about the algorithms. Want a self-learning system? Get your data, start coding or hire a PhD that will build you a model that will stand the test of time. Of course we know that this is not enough. Models degrade over time, algorithms that work great on yesterday’s data may not be the best option, new data sources and types are made available. In short, your self-learning system may not be learning anything at all. In this session, we will examine how to overcome challenges in creating self-learning systems that perform better and are built to stand the test of time. We will show how to apply mathematical optimization algorithms that often prove superior to local optimization methods favored by typical machine learning applications and discuss why these methods can crate better results. We will also examine the role of smart automation in the context of machine learning and how smart automation can create self-learning systems that are built to last.
Funda Gunes, Senior Research Statistician Developer & Patrick Koch, Principal...
Local Search Optimization for Hyper-Parameter Tuning: Many machine learning algorithms are sensitive to their hyper-parameter settings, lacking good universal rule-of-thumb defaults. In this talk we discuss the use of black-box local search optimization (LSO) for machine learning hyper-parameter tuning. Viewed as a black-box objective function of hyper-parameters, machine learning algorithms create a difficult class of optimization problems. The corresponding objective functions involved tend to be nonsmooth, discontinuous, unpredictably computationally expensive, requiring support for both continuous, categorical, and integer variables. Further evaluations can fail for a variety of reasons such as early exits due to node failure or hitting max time. Additionally, not all hyper-parameter combinations are compatible (creating so called “hidden constraints”). In this context, we apply a parallel hybrid derivative-free optimization algorithm that can make progress despite these difficulties providing significantly improved results over default settings with minimal user interaction. Further, we will address efficient parallel paradigms for different types of machine learning problems, while exploring the importance of validation to avoid overfitting and emphasizing that even for small data problems, the need to perform cross validations can create computationally intense functions that benefit from a distributed/threaded environment.
Daniel Shank, Data Scientist, Talla at MLconf SF 2016
Neural Turing Machines: Perils and Promise: Daniel Shank is a Senior Data Scientist at Talla, a company developing a platform for intelligent information discovery and delivery. His focus is on developing machine learning techniques to handle various business automation tasks, such as scheduling, polls, expert identification, as well as doing work on NLP. Before joining Talla as the company’s first employee in 2015, Daniel worked with TechStars Boston and did consulting work for ThriveHive, a small business focused marketing company in Boston. He studied economics at the University of Chicago.
Melanie Warrick, Deep Learning Engineer, Skymind.io at MLconf SF - 11/13/15
Attention Neural Net Model Fundamentals: Neural networks have regained popularity over the last decade because they are demonstrating real world value in different applications (e.g. targeted advertising, recommender engines, Siri, self driving cars, facial recognition). Several model types are currently explored in the field with recurrent neural networks (RNN) and convolution neural networks (CNN) taking the top focus. The attention model, a recently developed RNN variant, has started to play a larger role in both natural language processing and image analysis research.
This talk will cover the fundamentals of the attention model structure and how its applied to visual and speech analysis. I will provide an overview of the model functionality and math including a high-level differentiation between soft and hard types. The goal is to give you enough of an understanding of what the model is, how it works and where to apply it.
Misha Bilenko, Principal Researcher, Microsoft at MLconf SEA - 5/01/15
Many Shades of Scale: Big Learning Beyond Big Data: In the machine learning research community, much of the attention devoted to ‘big data’ in recent years has been manifested as development of new algorithms and systems for distributed training on many examples. This focus has led to significant advances in the field, from basic but operational implementations on popular platforms to highly sophisticated prototypes in the literature. In the meantime, other aspects of scaling up learning have received relatively little attention, although they are often more pressing in practice. The talk will survey these less-studied facets of big learning: scaling to an extremely large number of features, to many components in predictive pipelines, and to multiple data scientists collaborating on shared experiments.
Deploying signature verification with deep learning
Presentation covered building a signature verification system and deploying it to production. This includes resources usage as well as how the model was picked.
Meetup held in Tokyo with Deep learning Otemachi.
Narayanan Sundaram, Research Scientist, Intel Labs at MLconf SF - 11/13/15
GraphMat: Bridging the Productivity-Performance Gap in Graph Analytics: With increasing interest in large-scale distributed graph analytics for machine learning and data mining, more data scientists and developers are struggling to achieve high performance without sacrificing productivity on large graph problems. In this talk, I will discuss our solution to this problem: GraphMat. Using generalized sparse matrix-based primitives, we are able to achieve performance that is very close to hand-optimized native code, while allowing users to write programs using the familiar vertex-centric programming paradigm. I will show how we optimized GraphMat to achieve this performance on distributed platforms and provide programming examples. We have integrated GraphMat with Apache Spark in a manner that allows the combination to outperform all other distributed graph frameworks. I will explain the reasons for this performance and show that our approach achieves very high hardware efficiency in both single-node and distributed environments using primitives that are applicable to many machine learning and HPC problems. GraphMat is open source software and available for download.
MLConf 2016 SigOpt Talk by Scott Clark
MLConf 2016 SigOpt Talk on Bayesian Global Optimization: Using Optimal Learning to Tune ML Models.
Deep learning with Keras
Interest in Deep Learning has been growing in the past few years. With advances in software and hardware technologies, Neural Networks are making a resurgence. With interest in AI based applications growing, and companies like IBM, Google, Microsoft, NVidia investing heavily in computing and software applications, it is time to understand Deep Learning better!
In this workshop, we will discuss the basics of Neural Networks and discuss how Deep Learning Neural networks are different from conventional Neural Network architectures. We will review a bit of mathematics that goes into building neural networks and understand the role of GPUs in Deep Learning. We will also get an introduction to Autoencoders, Convolutional Neural Networks, Recurrent Neural Networks and understand the state-of-the-art in hardware and software architectures. Functional Demos will be presented in Keras, a popular Python package with a backend in Theano and Tensorflow.
Feature Engineering
Tips & Tricks for Feature Engineering / Applied Machine Learning. This presentation was given as part of the Sao Paulo ML meetup.
Deeplearning on Hadoop @OSCON 2014
Distributed Deep Learning on Hadoop
Deep-learning is useful in detecting anomalies like fraud, spam and money laundering; identifying similarities to augment search and text analytics; predicting customer lifetime value and churn; recognizing faces and voices.
Deeplearning4j is an infinitely scalable deep-learning architecture suitable for Hadoop and other big-data structures. It includes a distributed deep-learning framework and a normal deep-learning framework; i.e. it runs on a single thread as well. Training takes place in the cluster, which means it can process massive amounts of data. Nets are trained in parallel via iterative reduce, and they are equally compatible with Java, Scala and Clojure. The distributed deep-learning framework is made for data input and neural net training at scale, and its output should be highly accurate predictive models.
The framework's neural nets include restricted Boltzmann machines, deep-belief networks, deep autoencoders, convolutional nets and recursive neural tensor networks.
CNN Quantization
For real world application, convolutional neural network(CNN) model can take more than 100MB of space and can be computationally too expensive. Therefore, there are multiple methods to reduce this complexity in the state of art. Ristretto is a plug-in to Caffe framework that employs several model approximation methods. For this projects, first a CNN model is trained for Cifar-10 dataset with Caffe, then Ristretto will be use to generate multiple approximated version of the trained model using different schemes. The goal of this projects is comparison of the models in terms of execution performance, model size and cache utilizations in the test or inference phase. The same steps are done with Tensorflow and Quantisation tool. The quantisation schemes of Tensorflow and Ristretto are then compared.
Data Workflows for Machine Learning - Seattle DAML
First public meetup at Twitter Seattle, for Seattle DAML:
http://www.meetup.com/Seattle-DAML/events/159043422/
We compare/contrast several open source frameworks which have emerged for Machine Learning workflows, including KNIME, IPython Notebook and related Py libraries, Cascading, Cascalog, Scalding, Summingbird, Spark/MLbase, MBrace on .NET, etc. The analysis develops several points for "best of breed" and what features would be great to see across the board for many frameworks... leading up to a "scorecard" to help evaluate different alternatives. We also review the PMML standard for migrating predictive models, e.g., from SAS to Hadoop.
General Tips for participating Kaggle Competitions
The slides of a talk at Spark Taiwan User Group to share my experience and some general tips for participating kaggle competitions.
Separating Hype from Reality in Deep Learning with Sameer Farooqui
Deep Learning is all the rage these days, but where does the reality of what Deep Learning can do end and the media hype begin? In this talk, I will dispel common myths about Deep Learning that are not necessarily true and help you decide whether you should practically use Deep Learning in your software stack.
I’ll begin with a technical overview of common neural network architectures like CNNs, RNNs, GANs and their common use cases like computer vision, language understanding or unsupervised machine learning. Then I’ll separate the hype from reality around questions like:
• When should you prefer traditional ML systems like scikit learn or Spark.ML instead of Deep Learning?
• Do you no longer need to do careful feature extraction and standardization if using Deep Learning?
• Do you really need terabytes of data when training neural networks or can you ‘steal’ pre-trained lower layers from public models by using transfer learning?
• How do you decide which activation function (like ReLU, leaky ReLU, ELU, etc) or optimizer (like Momentum, AdaGrad, RMSProp, Adam, etc) to use in your neural network?
• Should you randomly initialize the weights in your network or use more advanced strategies like Xavier or He initialization?
• How easy is it to overfit/overtrain a neural network and what are the common techniques to ovoid overfitting (like l1/l2 regularization, dropout and early stopping)?
Deep learning with keras
Keras is a high level framework that runs on top of AI library such as Tensorflow, Theano, or CNTK. The key feature of Keras is that it allow to switch out the underlying library without performing any code changes. Keras contains commonly used neural-network building blocks such as layers, optimizer, activation functions etc and keras has support for convolutional and recurrent neural networks. In addition keras contains datasets and some pre-trained deep learnig applications that make it easier to learn for beginners. Essentially Keras is democrasting deep learning by reducing barrier into deep learning.
Keras Tutorial For Beginners | Creating Deep Learning Models Using Keras In P...
** AI & Deep Learning Training: https://www.edureka.co/ai-deep-learning-with-tensorflow ** )
This Edureka Tutorial on "Keras Tutorial" (Deep Learning Blog Series: https://goo.gl/4zxMfU) provides you a quick and insightful tutorial on the working of Keras along with an interesting use-case! We will be checking out the following topics:
Agenda:
What is Keras?
Who makes Keras?
Who uses Keras?
What Makes Keras special?
Working principle of Keras
Keras Models
Understanding Execution
Implementing a Neural Network
Use-Case with Keras
Coding in Colaboratory
Session in a minute
Check out our Deep Learning blog series: https://bit.ly/2xVIMe1
Check out our complete Youtube playlist here: https://bit.ly/2OhZEpz
Follow us to never miss an update in the future.
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
What's hot (20)
10 more lessons learned from building Machine Learning systems
10 more lessons learned from building Machine Learning systems
Jean-François Puget, Distinguished Engineer, Machine Learning and Optimizatio...
Jean-François Puget, Distinguished Engineer, Machine Learning and Optimizatio...
Funda Gunes, Senior Research Statistician Developer & Patrick Koch, Principal...
Funda Gunes, Senior Research Statistician Developer & Patrick Koch, Principal...
Daniel Shank, Data Scientist, Talla at MLconf SF 2016
Daniel Shank, Data Scientist, Talla at MLconf SF 2016
Melanie Warrick, Deep Learning Engineer, Skymind.io at MLconf SF - 11/13/15
Melanie Warrick, Deep Learning Engineer, Skymind.io at MLconf SF - 11/13/15
Misha Bilenko, Principal Researcher, Microsoft at MLconf SEA - 5/01/15
Misha Bilenko, Principal Researcher, Microsoft at MLconf SEA - 5/01/15
Deploying signature verification with deep learning
Deploying signature verification with deep learning
Narayanan Sundaram, Research Scientist, Intel Labs at MLconf SF - 11/13/15
Narayanan Sundaram, Research Scientist, Intel Labs at MLconf SF - 11/13/15
Data Workflows for Machine Learning - Seattle DAML
Data Workflows for Machine Learning - Seattle DAML
General Tips for participating Kaggle Competitions
General Tips for participating Kaggle Competitions
Separating Hype from Reality in Deep Learning with Sameer Farooqui
Separating Hype from Reality in Deep Learning with Sameer Farooqui
Keras Tutorial For Beginners | Creating Deep Learning Models Using Keras In P...
Keras Tutorial For Beginners | Creating Deep Learning Models Using Keras In P...
Similar to How To Do A Project
Introduction to Agile Software Development & Python
These slides were used to teach the module "Introduction to Agile Software Development & Python" as a sub-section of the major course "Software Engineering" for the 3rd year undergraduates of the Department of Computer Engineering, University of Peradeniya in 2010.
Agile & DevOps - It's all about project success
A collection of materials I use to discuss devops and agile and their role in software development
Info Session GDSC USICT
GDSC USICT organized an “INFO SESSION”. In this event the leads of all the teams introduced themselves to all the students and informed them about the benefits of joining GDSC. Leads gave students a broad idea about the technologies they would be working on and how it would help the students to solve real-life problems of society and to grow themselves.
MongoDB World 2018: How an Idea Becomes a MongoDB Feature
Speaker: Ian Whalen, Director, Server Program Management, MongoDB
Implementing a Design System in a Small Team by SnapTravel
This session will provide a blueprint for how a team of 2 Designers and 3 Frontend engineers can work together, in a lean way, to build and implement a design system within 6 months while still working on other important company initiatives/features.
Test team dynamics, Антон Мужайло
Software Testing meetup of "Open Technology Week" by Sigma Software, Kyiv 2017.
DevOps Dilemma - Make Dev work with Ops!
Every business runs on software and demanding more, faster and better from their IT teams. Current IT operating models are struggling to support the high velocity needs to the business. In this session we run through the steps that brings real meaning to the DevOps journey to make achieve faster and better turnaround for your projects, features and operations.
Critical Capabilities to Shifting Left the Right Way
The concept of testing earlier in the SDLC isn't new, but the term "shift left" has reignited its importance. See how shifting left can help you, and how to do it right.
Why Can't We All Just Get Along? Improving Designer/Developer Collaboration
Presented at the Agile2011 conference.
Delivering High Quality Software Through Agile
Delivering high quality software is a risky undertaking. Development and Quality Assurance Managers are in a tight partnership over the war against software bugs. Software quality issues not only delay the time to market for a solution while increasing costs, but they also delay the subsequent benefits to the business. In a business world of increasing focus on compliance and control, Development and QA Managers must balance the need for agility against the reality of quality control. High quality software is the outcome of applying the combination of sound methodologies, best practices and supportive tooling.
This session will outline how to deliver high quality software through Agile and will demonstrate how to apply its practices to your projects and having them supported through tools like Visual Studio and Team Foundation Server.
Similar to How To Do A Project (20)
Introduction to Agile Software Development & Python
Introduction to Agile Software Development & Python
Agile Methodologies And Extreme Programming - Svetlin Nakov
Agile Methodologies And Extreme Programming - Svetlin Nakov
MongoDB World 2018: How an Idea Becomes a MongoDB Feature
MongoDB World 2018: How an Idea Becomes a MongoDB Feature
Implementing a Design System in a Small Team by SnapTravel
Implementing a Design System in a Small Team by SnapTravel
Critical Capabilities to Shifting Left the Right Way
Critical Capabilities to Shifting Left the Right Way
Why Can't We All Just Get Along? Improving Designer/Developer Collaboration
Why Can't We All Just Get Along? Improving Designer/Developer Collaboration
More from Sudarsun Santhiappan
Search Engine Demystified
Search Engines Demystified. The presentation covers about types of engines, search engine internal, comparative study, indexing, searching, information retrieval, inverted index, clustering, meta search engines, semantic search, search engine optimization, search evaluation, how to do search, search architecture and more.
Distributed Computing
Comprehensive study of parallel, cluster, distributed, grid and cloud computing paradigms
What it takes to be the Best IT Trainer
Discussions about the current engineering education scenario existing in self-financing colleges in Tamilnadu (2007-2008). All problems and ideas discussed are purely based on my personal experiences only.
Using Behavioral Patterns In Treating Autistic
The development of behavioral therapy
regimens for autistic patients is relatively challenging as these
patients may not be able to express feedback to the applied
treatment. The response to a treatment course is mostly
estimated qualitatively and with little systematic feedback
between therapy and response. Collecting and analyzing data
about a patient’s daily activities could yield patterns linking
these activities, thereby providing therapists with some
foreknowledge of likely possible behavioral outcomes related
to their therapies. We propose a method for anomaly
detection system, which can monitor behavior patterns of the
patient based on the data collected on a daily basis. The
knowledge gathered about the patient could prove suggestive
of the patient’s feedback to the applied therapy. Upon mining
the behavioral patterns, the system could predict the response
of a patient to a stimulus, given a list of recently displayed
behaviors and/or completed activities. The knowledge thus
gathered could also be used to treat other patients of similar
disability.
Topic Models Based Personalized Spam Filter
Spam filtering poses a critical problem in
text categorization as the features of text is
continuously changing. Spam evolves continuously and
makes it difficult for the filter to classify the evolving
and evading new feature patterns. Most practical
applications are based on online user feedback, the
task calls for fast, incremental and robust learning
algorithms. This paper presents a system for
automatically detection and filtering of unsolicited
electronic messages. In this paper, we have developed
a content-based classifier, which uses two topic models
LSI and PLSA complemented with a text patternmatching
based natural language approach. By
combining these powerful statistical and NLP
techniques we obtained a parallel content based Spam
filter, which performs the filtration in two stages. In
the first stage each model generates its individual
predictions, which are combined by a voting
mechanism as the second stage.
Latent Semantic Indexing For Information Retrieval
Introducing Latent Semantic Analysis through Singular Value Decomposition on Text Data for Information Retrieval
Object Oriented Design
The objective is to explain how a software design may be represented as a set of interacting objects that manage their own state and operations and to introduce various models that describe an object-oriented design.
More from Sudarsun Santhiappan (12)
Latent Semantic Indexing For Information Retrieval
Latent Semantic Indexing For Information Retrieval
Recently uploaded
Pushing the limits of ePRTC: 100ns holdover for 100 days
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
GraphSummit Singapore | Graphing Success: Revolutionising Organisational Stru...
Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank
Discover how Standard Chartered Bank harnessed the power of Neo4j to transform complex data access challenges into a dynamic, scalable graph database solution. This keynote will cover their journey from initial adoption to deploying a fully automated, enterprise-grade causal cluster, highlighting key strategies for modelling organisational changes and ensuring robust disaster recovery. Learn how these innovations have not only enhanced Standard Chartered Bank’s data infrastructure but also positioned them as pioneers in the banking sector’s adoption of graph technology.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
UiPath Test Automation using UiPath Test Suite series, part 4
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Microsoft - Power Platform_G.Aspiotis.pdf
Revolutionizing Application Development
with AI-powered low-code, presentation by George Aspiotis, Sr. Partner Development Manager, Microsoft
GraphRAG is All You need? LLM & Knowledge Graph
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
DevOps and Testing slides at DASA Connect
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Securing your Kubernetes cluster_ a step-by-step guide to success !
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
PHP Frameworks: I want to break free (IPC Berlin 2024)
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...
Leonard Jayamohan, Partner & Generative AI Lead, Deloitte
This keynote will reveal how Deloitte leverages Neo4j’s graph power for groundbreaking digital twin solutions, achieving a staggering 100x performance boost. Discover the essential role knowledge graphs play in successful generative AI implementations. Plus, get an exclusive look at an innovative Neo4j + Generative AI solution Deloitte is developing in-house.
National Security Agency - NSA mobile device best practices
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
State of ICS and IoT Cyber Threat Landscape Report 2024 preview
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
20240605 QFM017 Machine Intelligence Reading List May 2024
Everything I found interesting about machines behaving intelligently during May 2024
A tale of scale & speed: How the US Navy is enabling software delivery from l...
Rapid and secure feature delivery is a goal across every application team and every branch of the DoD. The Navy’s DevSecOps platform, Party Barge, has achieved:
- Reduction in onboarding time from 5 weeks to 1 day
- Improved developer experience and productivity through actionable findings and reduction of false positives
- Maintenance of superior security standards and inherent policy enforcement with Authorization to Operate (ATO)
Development teams can ship efficiently and ensure applications are cyber ready for Navy Authorizing Officials (AOs). In this webinar, Sigma Defense and Anchore will give attendees a look behind the scenes and demo secure pipeline automation and security artifacts that speed up application ATO and time to production.
We will cover:
- How to remove silos in DevSecOps
- How to build efficient development pipeline roles and component templates
- How to deliver security artifacts that matter for ATO’s (SBOMs, vulnerability reports, and policy evidence)
- How to streamline operations with automated policy checks on container images
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdf
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
FIDO Alliance Osaka Seminar: The WebAuthn API and Discoverable Credentials.pdf
FIDO Alliance Osaka Seminar
By Design, not by Accident - Agile Venture Bolzano 2024
As presented at the Agile Venture Bolzano, 4.06.2024
Recently uploaded (20)
Pushing the limits of ePRTC: 100ns holdover for 100 days
Pushing the limits of ePRTC: 100ns holdover for 100 days
GraphSummit Singapore | Graphing Success: Revolutionising Organisational Stru...
GraphSummit Singapore | Graphing Success: Revolutionising Organisational Stru...
Transcript: Selling digital books in 2024: Insights from industry leaders - T...
Transcript: Selling digital books in 2024: Insights from industry leaders - T...
UiPath Test Automation using UiPath Test Suite series, part 4
UiPath Test Automation using UiPath Test Suite series, part 4
Securing your Kubernetes cluster_ a step-by-step guide to success !
Securing your Kubernetes cluster_ a step-by-step guide to success !
PHP Frameworks: I want to break free (IPC Berlin 2024)
PHP Frameworks: I want to break free (IPC Berlin 2024)
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...
FIDO Alliance Osaka Seminar: Passkeys at Amazon.pdf
FIDO Alliance Osaka Seminar: Passkeys at Amazon.pdf
National Security Agency - NSA mobile device best practices
National Security Agency - NSA mobile device best practices
State of ICS and IoT Cyber Threat Landscape Report 2024 preview
State of ICS and IoT Cyber Threat Landscape Report 2024 preview
20240605 QFM017 Machine Intelligence Reading List May 2024
20240605 QFM017 Machine Intelligence Reading List May 2024
A tale of scale & speed: How the US Navy is enabling software delivery from l...
A tale of scale & speed: How the US Navy is enabling software delivery from l...
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdf
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdf
FIDO Alliance Osaka Seminar: The WebAuthn API and Discoverable Credentials.pdf
FIDO Alliance Osaka Seminar: The WebAuthn API and Discoverable Credentials.pdf
FIDO Alliance Osaka Seminar: Passkeys and the Road Ahead.pdf
FIDO Alliance Osaka Seminar: Passkeys and the Road Ahead.pdf
By Design, not by Accident - Agile Venture Bolzano 2024
By Design, not by Accident - Agile Venture Bolzano 2024
How To Do A Project
- 1. How to do a Project ? Sudarsun S., M.Tech Director – R & D Checktronix India Pvt Ltd