.

Here I present the backpropagation algorithm for a continuous target variable and no activation function in hidden layer: although simpler than the one used for the logistic cost function, it's a proficuous field for math lovers.

Added by Rubens Zimbres on November 19, 2016 at 9:30am — 3 Comments

This post is the outcome of my studies in Neural Networks and a sketch for application of the Backpropagation algorithm. It's a binary classification task with N = 4 cases in a Neural Network with a single hidden layer. After the hidden layer and the output layer there are sigmoid activation functions. Different colors were used in the Matrices, same color as the Neural Network structure (bias, input, hidden, output) to make it easier to understand.…

ContinueAdded by Rubens Zimbres on November 16, 2016 at 11:00am — 4 Comments

Last year I started developing a Face Recognition model. I started with static pictures and using Wolfram Mathematica. This year I found out we can do the same job using OpenCV in Python, or creating specific filters in R and applying Weierstrass and Gaussian transformation.

There are lots of difficulties in recognizing faces of the same person, like: position, rotation of face, age, feeling, brightness, gamma, contrast, gamma, saturation, obstacles like hands,hair and so…

ContinueAdded by Rubens Zimbres on October 15, 2016 at 4:00am — No Comments

Lately I've been doing some experiences with Theano and Deep Learning. One thing that I really thought could help is to understand the workflow of a Theano algorithm through visualization of tensors' connections. After developing the model, I printed the prediction algorithm for a deep learning Neural Net with 2 hidden layers, 2 inputs X1 and X2, and a continuous output Y. I used Graphviz and pydot to generate the graphic with this line of…

ContinueAdded by Rubens Zimbres on October 7, 2016 at 3:00am — 3 Comments

Before assessing R and Python, I will start with Wolfram Mathematica. It's a powerful software, similar to MatLab. You can handle lists and matrices easily, you have all the best mathematical functions, backup of Wolfram Alpha and extremely sophisticated graphics visualizations, that allow you, for instance, to make and visualize an animated gradient descent, animate different weights for a given neural network, choose a specific…

ContinueAdded by Rubens Zimbres on October 3, 2016 at 8:00am — 7 Comments

Complexity, in my point of view, is the key for disruptive evolutions in Data Science and Machine Learning. Approaches as the one from Edgar Morin allow us to see the world through a completely different point of view, analyzing and deconstructing commom sense, leading to a completely new epistemologic view of the world and problems.

Epistemology deals with fundamentals of truth, Socrates, Plato (the cave), Bachelard, Morin, Descartes and so on. An agent-based model is a model where…

ContinueAdded by Rubens Zimbres on June 26, 2016 at 7:01am — No Comments

- rubens (5)
- zimbres (5)
- networks (4)
- neural (4)
- learning (3)
- deep (2)
- Graphviz (1)
- backpropagation (1)
- computer (1)
- cost (1)
- derivatives (1)
- face (1)
- facial (1)
- function (1)
- hidden (1)
- layers (1)
- machine (1)
- mathematica (1)
- opencv (1)
- picture (1)
- pydot (1)
- python (1)
- recognition (1)
- tensors (1)
- theano (1)
- vision (1)
- weights (1)
- wolfram (1)

- 10 Scrum benefits and how to realize them
- 5 proven patterns for resilient software architecture design
- Microsoft's acquisition of Nuance clears antitrust hurdle
- The right to disconnect vs. America's always-on culture
- The enterprise advantages of automated data collection
- Today's blockchain use cases and industry applications
- Salesforce Industries rolls out banking AI compliance tools
- DataRobot integrates AI modeling tools with Snowflake
- M&A wave rising in hot AI market
- AWS analytics helps nonprofit fight climate change

Posted 17 June 2021

© 2021 TechTarget, Inc. Powered by

Badges | Report an Issue | Privacy Policy | Terms of Service

**Most Popular Content on DSC**

To not miss this type of content in the future, subscribe to our newsletter.

- Book: Applied Stochastic Processes
- Long-range Correlations in Time Series: Modeling, Testing, Case Study
- How to Automatically Determine the Number of Clusters in your Data
- New Machine Learning Cheat Sheet | Old one
- Confidence Intervals Without Pain - With Resampling
- Advanced Machine Learning with Basic Excel
- New Perspectives on Statistical Distributions and Deep Learning
- Fascinating New Results in the Theory of Randomness
- Fast Combinatorial Feature Selection

**Other popular resources**

- Comprehensive Repository of Data Science and ML Resources
- Statistical Concepts Explained in Simple English
- Machine Learning Concepts Explained in One Picture
- 100 Data Science Interview Questions and Answers
- Cheat Sheets | Curated Articles | Search | Jobs | Courses
- Post a Blog | Forum Questions | Books | Salaries | News

**Archives:** 2008-2014 |
2015-2016 |
2017-2019 |
Book 1 |
Book 2 |
More

**Most popular articles**

- Free Book and Resources for DSC Members
- New Perspectives on Statistical Distributions and Deep Learning
- Time series, Growth Modeling and Data Science Wizardy
- Statistical Concepts Explained in Simple English
- Machine Learning Concepts Explained in One Picture
- Comprehensive Repository of Data Science and ML Resources
- Advanced Machine Learning with Basic Excel
- Difference between ML, Data Science, AI, Deep Learning, and Statistics
- Selected Business Analytics, Data Science and ML articles
- How to Automatically Determine the Number of Clusters in your Data
- Fascinating New Results in the Theory of Randomness
- Hire a Data Scientist | Search DSC | Find a Job
- Post a Blog | Forum Questions