Neural Network is a concept which tries to mimic human brain to solve computational issues. Neural network consists of basic units called Neurons. Each neuron has some functions and they together with other neurons solve complex issues. Neural network as a concept has been around since 1960’s (maybe before that too). But it lost its importance in 1990’s. Due to recent advancements in computational power and some algorithmic breakthrough’s, it has started to play an important role for many complex machine learning problems.
Neuron : Each Neuron can have one or more inputs and one or more outputs. Each connection between other neurons with some weights associated.
Feed forward : The input is processed through one or many layers of neurons to get the output.
Back Propagation : The Error which is the difference between the predicted output and the original target output, is propagated from the output to the input in a reverse manner, so that the weights of the neurons adjust itself to the error.
Full code :
Learning materials :
Read this if you are new to Machine learning.
In this post, I want to summarize the useful tutorials to learn machine learning using python. Python provides a comprehensive set of tools for ML. iPython Notebook is one of the best tools that I have come across in recent times.
- Pandas — Importing data and for preprocessing
- Numpy — Fast, Efficient Matrix manipulations
- Sklearn — Machine learning toolkit.
- iPython — Interactive environment to code.
These 2 videos will help you kickstart on iPython and Sklearn
Exploring Machine Learning with Scikit-learn
Diving Deeper into Machine Learning with Scikit-learn
If you are new to python, I would recommend the python course on Udacity
In the meantime, Kaggle also provides good tutorial set to try out python
If you find any other useful tutorials, please add it to the comment
I am new to Machine Learning / Data Science. As I searched for more resources to learn ML – the concept, tools, I found these interesting resources.
Complete Machine learning overview : Coursera Machine Learning by Andrew Ng. This is the best place to start learning it from the scratch
Complete Data Science : Data Science from Harvard .
As the name states, the difference between the 2 courses is that the first course is more oriented towards different algorithms for learning from the data, the second course is more oriented towards data collection, data pre-processing and understanding the data.
There are a variety of tools/languages that can be used. I have tried Octave, R, Weka, Python, Matlab, Julia. R is one of the widely used in the industry each has its own advantages/disadvantages. Finally I chose to use python since I am more comfortable to python language. (I have tried Octave, R, Weka, Python).
Kaggle is an active community for machine learning. you get lot of data sets with which you learn as well as compete with other kagglers. Start trying out the competitions that are posted in there