Found this discussion on reddit, its sure helpful for data science roadmap. https://www.reddit.com/r/MachineLearning/comments/5z8110/d_a_super_harsh_guide_to_machine_learning/ Keep Learning
In the last post we have seen neural network with only two layers that is "Input layer" and "Output layer", which is like a logistic regression algorithm. However in this post we are going to code a Neural network with one more layer that is "hidden layer". You will learn how to: Implement a 2-class … Continue reading CODE A NEURAL NETWORK IN PLAIN NUMPY Part 2: Planar data classification with one hidden layer
You will learn to: Build the general architecture of a learning algorithm, including: Initializing parameters Calculating the cost function and its gradient Using an optimization algorithm (gradient descent) Gather all three functions above into a main model function, in the right order. Overview of the Problem set Problem Statement: You are given a dataset containing: … Continue reading Code a Neural Network in plain NumPy: Part 1 (with no Hidden Layer)
Early Stopping is to stop the Training of Neural Networks at the Right Time or Stop training when a monitored quantity has stopped improving. A major concern with training neural networks is in the choice of the number of training epochs to use. Too many epochs can lead to overfitting of the training dataset, whereas … Continue reading Early Stopping in Neural Networks
I am listing down some of the very basic things that will help you to progress in your machine learning journey.. 1. Get a hold over the language to be used for implementation – practice the basics till you are comfortable. 2. Practice and become comfortable with data cleaning and processing – this is essential. … Continue reading How to learn Data Science
" The two key ideas of deep learning for computer vision — convolutional neural networks and backpropagation were already well understood in 1989. The Long Short Term Memory (LSTM) algorithm, which is fundamental to deep learning for timeseries, was developed in 1997 and has barely changed since. So why did deep learning only take off … Continue reading Why deep learning? Why now?
We know that machine learning is about mapping inputs (such as images) to targets (such as the label “cat”), which is done by observing many examples of input and targets. We also know that deep neural networks do this input-to-target mapping via a deep sequence of simple data transformations (layers) and that these data transformations … Continue reading Understanding how deep learning works: