[unable to retrieve full-text content]This is a collection of introductory posts which present a basic overview of neural networks and deep learning. Start by learning some key terminology and gaining an understanding through some curated resources. Then look at summarized important research in the field before looking at a pair of concise case studies.
Original Post: Deep Learning and Neural Networks Primer: Basic Concepts for Beginners
[unable to retrieve full-text content]I am writing this article to show you the basics of using Instagram in a programmatic way. You can benefit from this if you want to use it in a data analysis, computer vision, or any other cool project you can think of.
Original Post: A Guide to Instagramming with Python for Data Analysis
[unable to retrieve full-text content]Whether you want to start learning deep learning for you career, to have a nice adventure (e.g. with detecting huggable objects) or to get insight into machines before they take over, this post is for you!
Original Post: First Steps of Learning Deep Learning: Image Classification in Keras
[unable to retrieve full-text content]This post surveys today’s foremost options for AI in the form of deep learning, examining each toolkit’s primary advantages as well as their respective industry supporters.
Original Post: A Guide to Understanding AI Toolkits
[unable to retrieve full-text content]This post introduces five perfectly valid ways of measuring distances between data points. We will also perform simple demonstration and comparison with Python and the SciPy library.
Original Post: Comparing Distance Measurements with Python and SciPy
[unable to retrieve full-text content]The validation step helps you find the best parameters for your predictive model and prevent overfitting. We examine pros and cons of two popular validation strategies: the hold-out strategy and k-fold.
Original Post: Making Predictive Models Robust: Holdout vs Cross-Validation
[unable to retrieve full-text content]This blog introduces the basics of reinforcement learning. We are going to see how reinforcement learning might help us to address these challenges; to work smarter at the edge when brute force technology advances will not suffice.
Original Post: Transforming from Autonomous to Smart: Reinforcement Learning Basics
[unable to retrieve full-text content]This collection of concise introductory data science tutorials cover topics including the difference between data mining and statistics, supervised vs. unsupervised learning, and the types pf patterns we can mine from data.
Original Post: Data Science Primer: Basic Concepts for Beginners
[unable to retrieve full-text content]Here we explain, what it Mathematical Optimisation? And how it can be applied in Business and Finance to make decisions.
Original Post: What Is Optimization And How Does It Benefit Business?
[unable to retrieve full-text content]While earlier entrants in this series covered elementary classification algorithms, another (more advanced) machine learning algorithm which can be used for classification is Support Vector Machines (SVM).
Original Post: The Machine Learning Abstracts: Support Vector Machines