The state of cybersecurity startup and VC funding

Eighteen months ago Venture Beat mapped 120 Israeli cybersecurity startups to serve as an internal guide for everyone and as a public resource for visitors to the Israeli cyber security scene.

Today, the landscape has grown to encompass 150 companies, including 40 new startups. We divide these companies into 16 distinct categories, with two new categories: “deception” and “software development life cycle.”

More info can be gleaned from the article below:

 

Source: http://venturebeat.com/2017/02/03/elon-musk-other-ceos-voice-travel-ban-concerns-to-trump/

 

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Hot reads for this week in machine learning and deep learning

December – 27th – 31st December

H2O Deep Learning Workshop London: Deep Learning Workshop Anomaly Detection with H2O and Deep Learning – article Google DeepMind github Repo Open Source Deep Learning for iOS, OS X and tvOS DeepLearningKit.org Apache Spark Machine Learning with Spark: Determining Credibility of a Customer Part 1 Part 2 Part 3 Part 4 Part 5 Deep Learning on Databricks Integrating with TensorFlow, Caffe, MXNet, and Theano Partitioning in Spark 9 steps in learning Spark Using Spark to analyze IPL cricket games Practical Deep Learning For Coders – fast.ai Lesson 1 Lesson 2 Lesson 3 Lesson 4 Lesson 5 Lesson 6 Lesson 7 Autonomous Car Driving Lane Finding Demo: A demonstration of how to find lane lines with OpenCV Nvidia – Deep Learning Embedded Kits Jetson TX1 Jetson TK1

December – 19th – 26th December

A curated list of Deep Learning Resources: Awesome Deep Learning The Future Of Machine Learning – McKinsey 2016 Analytics Study  Tensorflow TF Lessons for Beginners Inception: Model how to in TF Workshop – random-forest K-means clustering in TF Facebook Incubator Beringei is a high performance, in-memory storage engine for time series data LSTM: Understanding Long Shot Term Memory Networks Artificial intelligence is going to make it easier than ever to fake images and video TrademarkVision uses machine learning to make finding logos as easy as a reverse image search 50+ Data Science, Machine Learning Cheat Sheets, updated GLM SparkGLM – github  Products pipeline.io : End-to-End Spark ML and Tensorflow AI Data Pipelines Python: Tidy Data in Python – Article Github

December – 12th – 18th December

Tensorflow Demo: Projector.tensorflow.org Workshop – random-forest K-means clustering in TF TF-Learn Deepmind Learning to Learn OpenAI OAI initiative releases Universe Generative Models Fantasy Football FF tiers by Boris Chen – Web SF Data Science Group hits 10K Members (Acknowledgement) Global Innovation Results 2015 (Graph/Blog/Report)

November – 5th – 11th December

DL/ML Resource @Lab41 OpenAI initiative releases Universe Google DeepMind open source their lab work Jigsaw self-supervised CNN Caffe implementation of Jigsaw self-supervised CNN Google DeepMind Lab Source code at github An Interactive Tutorial on Numerical Optimization Image Classification, OpenCV, TF and Slim Image Classification and Segmentation with Tensorflow and TF-Slim Upsampling and Image Segmentation with Tensorflow and TF-Slim Image Completion with Deep Learning in TensorFlow Image-to-Image Translation with Conditional Adversarial Nets Neon/Nervanasystems Neon Meetups Audio Pattern Analysis in Neon  StitchFix fauxtograph at github Deep Style: Inferring the Unknown to Predict the Future of Fashion t-SNE: How to Use t-SNE Effectively Arthur Juliani: Visualizing Deep Learning with t-SNE Gist/Github Make Your Own 3D t-SNE Visualizations (Download) Thousands of bird sounds visualized using machine learning Research Papers: Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles

November – 28th – 4th December

The hard thing about Deep Learning – O’Reilly Article Neural Net for Handwritten Digit Recognition in JavaScript Discover the world of scientific research papers – Resources to research Person, Head, Body, Face and Posture tracking resources Various Image Classification dataset results MNIST CIFAR-10 The CIFAR-10 dataset Details CIFAR-100 STL-10 SVHN ILSVRC2012 task 1 BIDMach Python Numpy Tutorials – CS231n Stanford Python Projects on Github ELI5 – Python package to debug classifiers & explain predictions Lime: Explaining the predictions of any machine learning classifier ReInspect – End to end detection in crowded scenes TensorBox: Object detection and segmentation in TensorFlow TensorFlow: MNIST For ML Beginners TFLearn: Tensorflow Learn by Google TensorFlow: tf.contrib.learn to construct a neural network classifier and train it on the Iris data set Deep Learning Tutorials in Keras Keras: The Ultimate Beginner’s Guide to Deep Learning in Python Keras: Handwritten Digit Recognition using CNN With Simple CNN Model With Larger CNN Model Keras: Detailed List of Examples H2O Articles: Anomaly Detection: Increasing Classification Accuracy with H2O’s Autoencoder and R  MIT Tech Review  Python Image Research dd

November – 21st – 27th

Google AI Google AI Experiments – Quick Draw The Non-Technical Guide to Machine Learning & Artificial Intelligence Deep Learning with cuDNN Tutorials on topics in Machine learning New neural network building block allows faster and more accurate text understanding RLightGBM, R interface to Light Gradient Boosting Machine library Random forest interpretation with scikit-learn Is Artificial Intelligence Permanently Inscrutable?

November – 14th – 20th

Machine Learning Current state of machine learning (Machine learning 3.0) News: GE acquires wise.io Articles: How Shazam  works Semantic Scholar, a new AI-based search engine that could challenge Google Google shows the power of machine learning by enhancing blurry, low-res pictures Neural Networks: Preventing Overfitting in Neural Networks – Part 1  Preventing Overfitting in Neural Networks – Part 2 Tools: A command-line tool for launching Apache Spark clusters.

Hot reading this week 11/21-11/27

• Google AI Experiments – Quick Draw
• The Non-Technical Guide to Machine Learning & Artificial Intelligence
• Deep Learning with cuDNN
• Tutorials on topics in Machine learning

Hot reading this week (11/14 – 11/18)

Message: Most importantly, those who become successful initiate. They reach out to people, ask questions, make recommendations, offer to help, and pitch their ideas. (more)

Current state of machine learning (Machine learning 3.0) GE acquires wise.io How Shazam  works Semantic Scholar, a new AI-based search engine that could challenge Google Google shows the power of machine learning by enhancing blurry, low-res pictures Preventing Overfitting in Neural Networks – Part 1  Preventing Overfitting in Neural Networks – Part 2

The present and future of Hadoop from its creator Doug Cutting

At Starta + Hadoop world, Hadoop creator Dough Cutting explained Hadoop and talked more about its present and future. Doug talked about:

• What is Hadoop?
• How the name “Hadoop” came from?
• What a Hadoop application look like?
• Ethical use of Data
• Quick plans and future strategy

Here is the full interview:

Big Data – Transition from Velocity, Variety and Volume to adding Variability and Complexity in the mix

Previous Definition: Velocity, Variety and Volume

New Definition: Velocity, Variety and Volume + Variability and Complexity

A collection of Big Data Books from Packt Publication

I found that Packt publication have few great books on Big Data and here is a collection of few books which I found very useful:

Packt is giving its readers a chance to dive into their comprehensive catalog of over 2000 books and videos for the next 7 days with LevelUp program:

Packt is offering all of its eBooks and Videos at just $10 each or less –

The more EXP customers want to gain, the more they save:

• Any 1 or 2 eBooks/Videos – $10 each
• Any 3 to 5 eBooks/Videos – $8 each
• Any 6 or more eBooks/Videos – $6 each

More Information is available at bit.ly/Yj6oWq  |  bit.ly/1yu4679

For more information please visit : www.packtpub.com/packt/offers/levelup

Big Data 1B dollars Club – Top 20 Players

Here is a list of top players in Big Data world having influence over billion dollars (or more) Big Data projects directly or indirectly (not in order):

1. Microsoft
2. Google
3. Amazon
4. IBM
5. HP
6. Oracle
7. VMWare
8. Terradata
9. EMC
10. Facebook
11. GE
12. Intel
13. Cloudera
14. SAS
15. 10Gen
16. SAP
17. Hortonworks
18. MapR
19. Palantir
20. Splunk

The list is based on each above companies involvement in Big data directly or indirectly along with a direct product or not. All of above companies are involved in Big Data projects worth considering Billion+ …

Watch Spark Summit 2014 on UStream

You can check out the Spark Summit 2014 agenda here: http://spark-summit.org/2014/agenda

 

 

 

 

 

 

U Stream Sessions :

http://www.ustream.tv/channel/spark-summit-2014

• Track A : http://www.ustream.tv/channel/track-a1
• Track B: http://www.ustream.tv/channel/track-b1
• Track C: http://www.ustream.tv/channel/track-c1

Please register yourself at the summit site to get more details information.

Keywords: Spark Summit, Hadoop, Spark,