2018 Apr Tutorials, Overviews
All (99) | Courses, Education (4) | Meetings (10) | News, Features (11) | Opinions, Interviews (33) | Top Stories, Tweets (10) | Tutorials, Overviews (26) | Webcasts & Webinars (5)
- Choosing the Right Metric for Evaluating Machine Learning Models – Part 1 - Apr 27, 2018.
Each machine learning model is trying to solve a problem with a different objective using a different dataset and hence, it is important to understand the context before choosing a metric.
- Blockchain Explained in 7 Python Functions - Apr 27, 2018.
It wasn’t until I wrote my own simple Blockchain, that I truly understood what it is and the potential applications for it. So without further ado, lets set up our 7 functions!
- Building Convolutional Neural Network using NumPy from Scratch - Apr 26, 2018.
In this article, CNN is created using only NumPy library. Just three layers are created which are convolution (conv for short), ReLU, and max pooling.
- Implementing Deep Learning Methods and Feature Engineering for Text Data: The GloVe Model - Apr 25, 2018.
The GloVe model stands for Global Vectors which is an unsupervised learning model which can be used to obtain dense word vectors similar to Word2Vec.
- Top 16 Open Source Deep Learning Libraries and Platforms - Apr 24, 2018.
We bring to you the top 16 open source deep learning libraries and platforms. TensorFlow is out in front as the undisputed number one, with Keras and Caffe completing the top three.
- Swiftapply – Automatically efficient pandas apply operations - Apr 24, 2018.
Using Swiftapply, easily apply any function to a pandas dataframe in the fastest available manner.
- Why Deep Learning is perfect for NLP (Natural Language Processing) - Apr 20, 2018.
Deep learning brings multiple benefits in learning multiple levels of representation of natural language. Here we will cover the motivation of using deep learning and distributed representation for NLP, word embeddings and several methods to perform word embeddings, and applications.
- Neural Network based Startup Name Generator - Apr 20, 2018.
How to build a recurrent neural network to generate suggestions for your new company’s name.
- NLP – Building a Question Answering Model - Apr 20, 2018.
In this blog, I want to cover the main building blocks of a question answering model.
- Understanding What is Behind Sentiment Analysis – Part 2 - Apr 20, 2018.
Fine-tuning our sentiment classifier...
- Python Regular Expressions Cheat Sheet - Apr 19, 2018.
The tough thing about learning data is remembering all the syntax. While at Dataquest we advocate getting used to consulting the Python documentation, sometimes it's nice to have a handy reference, so we've put together this cheat sheet to help you out!
- Derivation of Convolutional Neural Network from Fully Connected Network Step-By-Step - Apr 19, 2018.
What are the advantages of ConvNets over FC networks in image analysis? How is ConvNet derived from FC networks? Where the term convolution in CNNs came from? These questions are to be answered in this article.
- Deep Learning With Apache Spark: Part 1 - Apr 18, 2018.
First part on a full discussion on how to do Distributed Deep Learning with Apache Spark. This part: What is Spark, basics on Spark+DL and a little more.
- Robust Word2Vec Models with Gensim & Applying Word2Vec Features for Machine Learning Tasks - Apr 17, 2018.
The gensim framework, created by Radim Řehůřek consists of a robust, efficient and scalable implementation of the Word2Vec model.
- Top 10 Technology Trends of 2018 - Apr 13, 2018.
In this article, we will focus on the modern trends that took off well on the market by the end of 2017 and discuss the major breakthroughs expected in 2018.
- Understanding What is Behind Sentiment Analysis – Part 1 - Apr 13, 2018.
Build your first sentiment classifier in 3 steps.
- 12 Useful Things to Know About Machine Learning - Apr 12, 2018.
This is a summary of 12 key lessons that machine learning researchers and practitioners have learned include pitfalls to avoid, important issues to focus on and answers to common questions.
- Getting Started with PyTorch Part 1: Understanding How Automatic Differentiation Works - Apr 11, 2018.
PyTorch has emerged as a major contender in the race to be the king of deep learning frameworks. What makes it really luring is it’s dynamic computation graph paradigm.
- Implementing Deep Learning Methods and Feature Engineering for Text Data: The Skip-gram Model - Apr 10, 2018.
Just like we discussed in the CBOW model, we need to model this Skip-gram architecture now as a deep learning classification model such that we take in the target word as our input and try to predict the context words.
- Loading Terabytes of Data from Postgres into BigQuery - Apr 9, 2018.
Despite the fact that an ETL task is pretty challenging when it comes to loading Big Data, there’s still the scenario in which you can load terabytes of data from Postgres into BigQuery relatively easy and very efficiently.
- Descriptive Statistics: The Mighty Dwarf of Data Science – Crest Factor - Apr 6, 2018.
No other mean of data description is more comprehensive than Descriptive Statistics and with the ever increasing volumes of data and the era of low latency decision making needs, its relevance will only continue to increase.
- Scalable Select of Random Rows in SQL - Apr 5, 2018.
Performance boosts are achieved by selecting random rows or the sampling technique. Let’s learn how to select random rows in SQL.
- Supervised vs. Unsupervised Learning - Apr 4, 2018.
Understanding the differences between the two main types of machine learning methods.
- How To Choose The Right Chart Type For Your Data - Apr 3, 2018.
The power of charts to assist in accurate interpretation is massive and that's why it is vital to select the correct type when you are trying to visualize data.
- Why You Should Start Using .npy Files More Often - Apr 3, 2018.
In this article, we demonstrate the utility of using native NumPy file format .npy over CSV for reading large numerical data set. It may be an useful trick if the same CSV data file needs to be read many times.
- Implementing Deep Learning Methods and Feature Engineering for Text Data: The Continuous Bag of Words (CBOW) - Apr 3, 2018.
The CBOW model architecture tries to predict the current target word (the center word) based on the source context words (surrounding words).