Machine Learning Research Blog
Machine Learning Research Blog
Welcome to the machine learning research blog (MLRB)! This is a series of tutorials on machine learning, starting from the very beginning of ML, and diving into deeper concepts graduallyp. Each post contains the description of the problem along with the implementation in TensorFlow and Python 2.7. To keep the length of the posts reasonable, sometimes, we will skip some of the obvious details of the code. But, as always, you can access the full code in my GitHub repository and they should work right away. If there is any specific tweaking necessary for a program, I will try to point it out in the post and also in the comment section of the code. Questions and suggestions are welcome!
Here is a list of Posts. Please check back for more posts in various ML topics.
A very first program of ML in TensorFlow
This post is basically a typical HelloWord program of ML to make sure your TensorFlow installation is working properly.
Simple linear classifier using TensorFlow
Here we describe a simplest form of a linear regression to understand what classification actually means.
A very basic units of neural networks, perceptron. We see what a single perceptron can do and when we need multiple of them.
Multi-layer perceptron, part 1: XOR
Simple 2 layer MLP to represent XOR operations.
Multi-layer perceptron, part 2: MNIST
MNIST dataset. Classify, visualize and more.
Caffe Installation for Ubuntu: Step-by-step
Install Caffe deep learning framework in Ububtu 14.04 and 16.04
Convolutional Neural Networks (CNN) in TensorFlow
A simple end-to-end CNN model and description of basic computations in CNNs
- The mighty TensorBoard
- AlexNet using pre-trained model
- Visualization of different layer features in TF