Recently, I came up with an idea for a new Optimizer (an algorithm for training neural network). In theory, it looked great but when I implemented it and tested it, it didn’t turn out to be good.
Some of my learning are:
Neural Networks are hard to predict.
Figuring out how to customize TensorFlow is hard because the main documentation is messy.
Theory and Practical are two different things. The more hands-on you are, the higher are your chances of trying out an idea and thus iterating faster.
I am sharing my algorithm here. Even though this algorithm may not be of much use to you but it would give you ideas on how to implement your own optimizer using Tensorflow Keras.
A neural network is basically a set of neurons connected to input and output. We need to adjust the connection strengths such that it gives the least error for a given set of input. To adjust the weight we use the algorithms. One brute force algorithm could be to try all possible combinations of weights (connections strength) but that will be too time-consuming. So, we usually use the greedy algorithm most of these are variants of Gradient Descent. In this article, we will write our custom algorithm to train a neural network. In other words, we will learn how to write our own custom optimizer using TensorFlow Keras.
Say you come up with a wonderful idea such as a really great phone service. You would want this phone service to be available to the APIs in various languages. Whether people are using Python, C++, Java or any other programming language, the users should be able to use your service. Also, you would want the users to be able to access globally. In such scenarios, you should create the Thrift Service. Thrift lets you create a generic interface which can be implemented on the server. The clients of this generic interface can be automatically generated in all kinds of languages.
Let us get started! Here we are going to create a very simple service that just prints the server time.
These Machine Learning Interview Questions, are the real questions that are asked in the top interviews.
For hiring machine learning engineers or data scientists, the typical process has multiple rounds.
A basic screening round – The objective is to check the minimum fitness in this round.
Algorithm Design Round – Some companies have this round but most don’t. This involves checking the coding / algorithmic skills of the interviewee.
ML Case Study – In this round, you are given a case study problem of machine learning on the lines of Kaggle. You have to solve it in an hour.
Bar Raiser / Hiring Manager – This interview is generally with the most senior person in the team or a very senior person from another team (at Amazon it is called Bar raiser round) who will check if the candidate fits in the company-wide technical capabilities. This is generally the last round.
Recently, a friend whose company is working on large scale project reached out to us to seek a solution to a simple problem of finding a list of phrases (approximately 80,000) in a huge set of rich text documents (approx 6 million).
The problem at first looked simple. The way engineers had solved it is by simply loading the two documents in Apache Spark’s DataFrame and joining those using “like”. Something on these lines:
select phrase.id, docs.id from phrases, docs where docs.txt like ‘%’ + phrases.phrase + ‘%’
But it was taking huge time even on the small subset of the data and processing is done in distributed fashion. Any Guesses, why?
They had also tried to use Apache Spark’s broadcast mechanism on the smaller dataset but still, it was taking a long while finishing even a small task.
Apache Flume is a distributed, reliable, and available system for efficiently collecting, aggregating & moving large data from many different sources to a centralized data store.
Flume supports a large variety of sources Including:
tail (like unix tail -f),
log4j – allowing Java applications to write logs to HDFS via flume
Flume nodes can be arranged in arbitrary topologies.Typically there is a node running on each source machine, with tiers of aggregating nodes that the data flows through on its way to HDFS.
What is Flume
Flume: Use Case
Flume: Use Case – Agents
Flume: Multiple Agents
Flume: Delivery Reliability
Introduction to Flume Presentation
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[This blog is from KnowBigData.com. It is pretty old. Many things have changed since then. People have moved to MLLib. We have also moved to CloudxLab.com.]
What is Machine Learning?
Machine Learning is programming computers to optimize a Performance using example data or past experience, it is a branch of Artificial Intelligence.
Types of Machine Learning
Machine learning is broadly categorized into three buckets:
Supervised Learning – Using Labeled training data, to create a classifier that can predict the output for unseen inputs.
Unsupervised Learning – Using Unlabeled training data to create a function that can predict the output.
Semi-Supervised Learning – Make use of unlabeled data for training – typically a small amount of labeled data with a large amount of unlabeled data.
Machine Learning Applications
Recommend Friends, Dates, Products to end-user.
Classify content into pre-defined groups.
Find Similar content based on Object Properties.
Identify key topics in large Collections of Text.
Detect Anomalies within given data.
Ranking Search Results with User Feedback Learning.
Classifying DNA sequences.
Sentiment Analysis/ Opinion Mining
Natural Language Processing,
Speech and HandWriting Recognition.
Mahout – Keeper/Driver of Elephants. Mahout is a Scalable Machine Learning Library built on Hadoop, written in Java and its Driven by Ng et al.’s paper “MapReduce for Machine Learning on Multicore”. Development of Mahout Started as a Lucene sub-project and it became Apache TLP in Apr’10.
Introduction to Machine Learning and Mahout
Machine Learning- Types
Machine Learning- Applications
Machine Learning- Tools
Mahout – Recommendation Example
Mahout – Use Cases
Mahout Live Example
Mahout – Other Recommender Algos
Machine Learning with Mahout Presentation
Machine Learning with Mahout Videohttps://www.youtube.com/embed/PZsTLIlSZhI
In this post, we will show you how you can install Hortonworks Data Platform on AWS.
You can also watch the video of this tutorial here
We start with three machines. We could install Hadoop on these machines by manually downloading and configuring them, but that’s very insufficient. So either we could use Cloudera manager or Ambari. In this tutorial, we are going to use Ambari. On the first machine, we are going to install the Ambari server. For that, we need to buy these three instances at Amazon and we will follow the Ambari guidelines.
Ambari will then install all the components that are required in other two machines.
Please note, we will use 16 GB ram machines so that installation goes smoothly.