2018 Nov Tutorials, Overviews
All (105) | Courses, Education (10) | Meetings (9) | News (13) | Opinions (32) | Top Stories, Tweets (9) | Tutorials, Overviews (24) | Webcasts & Webinars (8)
- Deep Learning for the Masses (… and The Semantic Layer) - Nov 30, 2018.
Deep learning is everywhere right now, in your watch, in your television, your phone, and in someway the platform you are using to read this article. Here I’ll talk about how can you start changing your business using Deep Learning in a very simple way. But first, you need to know about the Semantic Layer.
- Variational Autoencoders Explained in Detail - Nov 30, 2018.
We explain how to implement VAE - including simple to understand tensorflow code using MNIST and a cool trick of how you can generate an image of a digit conditioned on the digit.
- Sales Forecasting Using Facebook’s Prophet - Nov 28, 2018.
In this tutorial we’ll use Prophet, a package developed by Facebook to show how one can achieve this.
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Deep Learning Cheat Sheets - Nov 28, 2018.
Check out this collection of high-quality deep learning cheat sheets, filled with valuable, concise information on a variety of neural network-related topics. -
Intro to Data Science for Managers - Nov 23, 2018.
This mindmap contains a condensed introduction to the key data science concepts and techniques that have revolutionized the business landscape and became essential for making beneficial data-driven decisions -
An Introduction to AI - Nov 21, 2018.
We provide an introduction to AI key terminologies and methodologies, covering both Machine Learning and Deep Learning, with an extensive list including Narrow AI, Super Intelligence, Classic Artificial Intelligence, and more. - Using a Keras Long Short-Term Memory (LSTM) Model to Predict Stock Prices - Nov 21, 2018.
LSTMs are very powerful in sequence prediction problems because they’re able to store past information. This is important in our case because the previous price of a stock is crucial in predicting its future price.
- Top 10 Python Data Science Libraries - Nov 16, 2018.
The third part of our series investigating the top Python Libraries across Machine Learning, AI, Deep Learning and Data Science.
- Sorry I didn’t get that! How to understand what your users want - Nov 16, 2018.
Creating a chatbot is difficult, it involves knowledge in many AI-Hard tasks, such as Natural Language Understanding, Machine Comprehension, Inference, or Automatic Language Generation (in fact, solving these tasks is close to solving AI) and large human effort is required.
- Best Deals in Deep Learning Cloud Providers: From CPU to GPU to TPU - Nov 15, 2018.
A detailed comparison of the best places to train your deep learning model for the lowest cost and hassle, including AWS, Google, Paperspace, vast.ai, and more.
- Mastering The New Generation of Gradient Boosting - Nov 15, 2018.
Catboost, the new kid on the block, has been around for a little more than a year now, and it is already threatening XGBoost, LightGBM and H2O.
- 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.
- 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.
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