Tensorflow for Beginners walkthrough - Udacity Machine Learning Nanodegree Deep Learning

Difference between Batch Gradient Descent and Stochastic Gradient Descent - Udacity Machine Learning Nanodegree Coursera

Recommend this great 13 minutes crystal clear video by Andrew Ng on Coursera explaining the differences between batch gradient descent aka gradient descent aka normal gradient descent versus Stochastic Gradient Descent. https://www.coursera.org/learn/machine-learning/lecture/DoRHJ/stochastic-gradient-descent It's clear simple and easy to understand without prerequisite. Andrew Ng shows you how the formula differs, how the step by step train strategy differs and a visualization of the trajectory to find global minimum (the center of all the ellipse in his graph).

• Summary
• Gradient Descent may have issues when the scale of the data is large
• If the number of training samples m is large
• Gradient Descent algorithm requires summing over all of m
• e.g. US population census population of 300MM
• Stochastic Gradient Descent is a modification of gradient descent
• In other words, the cost functions are different
• Stochastic every iteration is faster
• Steps: randomly shuffle dataset, optimize one training data at a time, improve parameters early one at a time, instead of looking at the examples together as a batch
• Weakness: generally moves towards global minimum, but doesn't always go there, can reach the general vicinity of the global minimum. Does not converge as nicely as gradient descent. 
• In reality, practical data science, once it gets close to the global minimum its parameters are good enough. In real life, it works out.

K Means Clustering Unsupervised Learning - Udacity Machine Learning Nanodegree Flash Card

• Draw a line connecting two centroids and use the half way line as a division line for two hyperplanes (if two clusters). Results vary greatly.

• Initial positions of centroid can strongly influence result. Different initial positions give completely different results.

• Analogy "Rubber Band"

• Center of the cluster is called a centroid

• Number of centroids at initiation can heavily influence the result. 

• Great for ... PROS:

• Bad for ... CONS ... limitations:
• Hill climbing algorithm.
• Result depends on initiation
• If initiation is close to local optima, may be sticky. Never move away. Ignore global optima. Bad initial centroids exist
• If there are more potential clusters, there are more local optima. Run iterate the algorithm many times to avoid being stuck. 

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Udacity Digital Marketing Nanodegree Reviews (updating in progress)

This review is updated continuously throughout the program. Yay I just joined the Udacity Nanodegree for Digital Marketing! I am such an Udacity and learning junkie LOL. What grabbed my attention was the line-up of partners, the real world projects and also Avinash Kaushik's presence. I wonder what's the oracle of Google Analytics doing promoting this course.

• First impression, clean beautiful videos, unlike some of the programming Georgia Tech videos Udacity has
• The partners really do show up early in the syllabus and seems like they will participate
• Though jobs are not guaranteed, there are mentions of hiring partners
• Classmates are young and energetic marketing veterans. Already very active on slack
• Meet the students use hashtag #ImInDMND on instagram
• Realworld like non-trivial business cases and owner / user statements
• What are the projects like? Udacity allows you to use Udacity as a real-world marketing project.
• Amazing speakers, famous authors and speakers including author of crossing the chasm, avinash kaushik Google Analytics evangelist
• Mentorship - mentorship is available. My mentor has been unresponsive and unhelpful so far. I do not recommend.
• Mini interviews with industry giants
• The Facebook Ad project is extremely useful. The real world project experience can have tangible results. It is resume worthy. I got a sizable view and conversion of which I am comfortable to talk about in future interviews.
• Does it directly translate into full time jobs? Probably not, Udacity took away the guarantee a long time ago. It does give exposure to Moz, Google Ads, which are not as accessible experience as Facebook ads and mailchimp, which any one can try on one's own. 

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Udacity Machine Learning Nanodegree - Projects Step by Step Walkthrough High Level Cheat Sheet

High level steps to solve Udacity Machine Learning Nanodegree projects:

• Import dependencies: numpy, pandas, sklearn, matplotlib
• Data cleaning:
• Replace all data with numeric value such as binaries 0 and 1 or scale down to between -1 to 1, or 0 to 1 (normalization). 
• Replace yes/no binary answers with 1,0
• Replace categorical data A, B, C with dummy columns |A|B|C| use 1 if true, 0 if false
• Split data into features and target aka label
• Perform initial exploration, turns data CSV into Pandas.DataFrame
• Computer summary stats: mean, counts etc.
• from sklearn import model
• clf = sklearnmodel.model() #specify the classifier
• clf.fit( ... ) #fit the model wither parameters
• clf.predict() #make predictions
• Metrics:
• R^2 R squared - great for linear regression 0 to 1, 1 being the best
• Errors:
• This list is under construction

Sklearn machine learning model cheat sheet
What are the best algorithms to use for each machine learning problem?
Classification versus regression
Supervised versus unsupervised

Machine Learning with Tensorflow 101 - 01 Python Seed File Walkthrough

Here's Sraj's pro and fast explanation of the same problem