Oct2018 Nov Tutorials, Overviews
All (46) | Courses, Education (4) | Meetings (4) | News (7) | Opinions (10) | Top Stories, Tweets (4) | Tutorials, Overviews (13) | Webcasts & Webinars (4)
- The 5 Basic Statistics Concepts Data Scientists Need to Know
- Nov 13, 2018.
Today, we’re going to look at 5 basic statistics concepts that data scientists need to know and how they can be applied most effectively!
- Multi-Class Text Classification with Doc2Vec & Logistic Regression
- Nov 9, 2018.
Doc2vec is an NLP tool for representing documents as a vector and is a generalizing of the word2vec method. In order to understand doc2vec, it is advisable to understand word2vec approach.
- Deep Learning Performance Cheat Sheet
- Nov 8, 2018.
We outline a variety of simple and complex tricks that can help you boost your deep learning models accuracy, from basic optimization, to open source labeling software.
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10 Free Must-See Courses for Machine Learning and Data Science
- Nov 8, 2018.
Check out a collection of free machine learning and data science courses to kick off your winter learning season. - Introduction to PyTorch for Deep Learning
- Nov 7, 2018.
In this tutorial, you’ll get an introduction to deep learning using the PyTorch framework, and by its conclusion, you’ll be comfortable applying it to your deep learning models.
- Text Preprocessing in Python: Steps, Tools, and Examples
- Nov 6, 2018.
We outline the basic steps of text preprocessing, which are needed for transferring text from human language to machine-readable format for further processing. We will also discuss text preprocessing tools.
- Building Surveillance System Using USB Camera and Wireless-Connected Raspberry Pi
- Nov 6, 2018.
Read this post to learn how to build a surveillance system using a USB camera plugged into Raspberry Pi (RPi) which is connected a PC using its wireless interface.
- Mastering the Learning Rate to Speed Up Deep Learning
- Nov 6, 2018.
Figuring out the optimal set of hyperparameters can be one of the most time consuming portions of creating a machine learning model, and that’s particularly true in deep learning.
- Quantum Machine Learning: A look at myths, realities, and future projections
- Nov 5, 2018.
An overview of quantum computing and quantum algorithm design, including current state of the hardware and algorithm design within the existing systems.
- Machine Learning Classification: A Dataset-based Pictorial
- Nov 5, 2018.
In order to relate machine learning classification to the practical, let's see how this concept plays out, step by step (and with images), specifically in direct relation to a dataset.
- Top 13 Python Deep Learning Libraries
- Nov 2, 2018.
Part 2 of a new series investigating the top Python Libraries across Machine Learning, AI, Deep Learning and Data Science.
- Data Representation for Natural Language Processing Tasks
- Nov 2, 2018.
In NLP we must find a way to represent our data (a series of texts) to our systems (e.g. a text classifier). As Yoav Goldberg asks, "How can we encode such categorical data in a way which is amenable for us by a statistical classifier?" Enter the word vector.
- Multi-Class Text Classification Model Comparison and Selection
- Nov 1, 2018.
This is what we are going to do today: use everything that we have presented about text classification in the previous articles (and more) and comparing between the text classification models we trained in order to choose the most accurate one for our problem.
What does a data scientist REALLY look like?