Machine learning and statistics with python

 Prelogue: Recently kdNuggets published this poll survey depicting that one of the hottest skills is MLOps nowadays. Although I knew ML Ops is devOps for ML; but knowing that one line is clearly lacking as a data scientist. And boy that's not good for my career. So buckle your seats as I take you through my internet voyage through knowing basics of ML Ops. Introduction: To begin with, one must read the awesome intro to ML Ops written by google clouds. This awesome article first introduces the concept of CI/CD/CT. which are respectively continuous integration, continuous delivery and the last option being specific to ml models: continuous training.  This post then goes on to define each of terms and establishes the facts that ml code is actually a very small part of a data science exercise; with starting from product designing and data storage; to data validation, data fetching, model deployment and many more concepts actually come together to create the whole picture of ml...

 Introduction: The part of machine learning which deals with language modeling and textual data; can be divided into three broad parts. These three parts are: (1) Natural language processing (NLP) : this is processing of unstructured text data into structured, analyzed and often quantized  data form.  (2) Natural language understanding or inferencing ( NLU/ NLI): this refers to working on algorithms and models which understand written languages rather than just statistically analyzing it. (3) Natural language generation (NLG): this refers to building models/frameworks which generate contents which mimic the quality of actual human writings. For years, NLP, NLU and NLG have progressed, with much variance and overlap in between them. With the introduction of RNN, LSTM, CNN etc, NLG systems have shown much better progress than previous times; just like the counterparts. In this article, we will test out the performance of NLG tasks using the transformers library; namely we...

Select Language Afrikaans Albanian Arabic Armenian Azerbaijani Basque Belarusian Bulgarian Catalan Chinese (Simplified) Chinese (Traditional) Croatian Czech Danish Dutch English Estonian Filipino Finnish French Galician Georgian German Greek Haitian Creole Hebrew Hindi Hungarian Icelandic Indonesian Irish Italian Japanese Korean Latvian Lithuanian Macedonian Malay Maltese Norwegian Persian Polish Portuguese Romanian Russian Serbian Slovak Slovenian Spanish Swahili Swedish Thai Turkish Ukrainian Urdu Vietnamese Welsh Yiddish Bengali Gujarati Marathi Nepali Punjabi Tamil Telugu Introduction: We will talk about only one big issue which seems to be hovering over the problem of summarization and that is the sequence length being at maximum 512/1024. This is a big drawback from the abstract documents. We will first discuss why this issue happens and then I will share some literature study on the practices to solve this problem.  Description: I have been working with tr...

Create a table in snowflake using external data Recently I needed to upload a data into snowflake as a table and perform some operations on it. Surprisingly as it is, I found that although I knew how to create a table in sql; I didn't know how to upload a data into snowflake. Obviously, I could have gone into internet to find out python libraries to solve this problem; i.e. access snowflake via a python session, then get the data and upload it in some probable template for tables. But it was a quick work and I wanted to do it fast. So I chose to upload it in a s3 data bucket and then create an empty table in snowflake; in which I would then put the data, pulling it into snowflake from s3. Now that is a nice and easy way to do; and let's see the exact procedure and how do we complete each of the parts. Upload the data into s3: Uploading data into s3 can be done again in two ways. The first one is to use a boto3 session and then upload the data into s3 via that boto session. If...

Select Language Afrikaans Albanian Arabic Armenian Azerbaijani Basque Belarusian Bulgarian Catalan Chinese (Simplified) Chinese (Traditional) Croatian Czech Danish Dutch English Estonian Filipino Finnish French Galician Georgian German Greek Haitian Creole Hebrew Hindi Hungarian Icelandic Indonesian Irish Italian Japanese Korean Latvian Lithuanian Macedonian Malay Maltese Norwegian Persian Polish Portuguese Romanian Russian Serbian Slovak Slovenian Spanish Swahili Swedish Thai Turkish Ukrainian Urdu Vietnamese Welsh Yiddish Bengali Gujarati Marathi Nepali Punjabi Tamil Telugu S ummarization is one of the several core tasks which are there in NLP. We will discuss about basic theories of summarization in this post. Summarization is the process of summarizing a text into a small, concise and important copy. A summarization of a text is expected to be bearing all the key points, while maintaining a text flow and coherence; and all the sentences created should be meaningful. A...