Breaking the curse of small datasets in Machine Learning: Part 1This is Part 1 of Breaking the curse…10 not so intuitive things…
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Footy Tipping with Machine Learning: Adding Player Data
There is the possibility of using something like this to add a feature to the main player-data model, but I…
Continue ReadingNormalization in Gradient`s Point of View [ Manual Back Prop in TF ]
Simply put after few iterations the weights are not changing much, especially layer two, three, and four.Batch NormalizationTop Left →…
Continue ReadingMaster Python through building real-world applications (Part 3)
website in line checks the first line of hosts file, now which website it is looking for?.It comes from the…
Continue ReadingHow neuroscientists analyze data from transparent fish brains : part 1, pre-processing.
A more efficient way to look at several neurons at the same time is to plot the activity map (x…
Continue ReadingSimple House Price Predictor using ML through TensorFlow in Python
The result here are input and output sets for both the test and train# Build the training set and the…
Continue ReadingFeature engineering, Explained
And this is important during the feature engineering as well.One common practice is to introduce a boolean feature indicating whether…
Continue ReadingPredictive Modeling and Its Social Issues
What’s surprising is that this proofs as to how much weightage data holds in mainstream business world; despite being accountable…
Continue ReadingDesigning a Machine Learning model and deploying it using Flask on Heroku
We created a machine learning model, trained it, created a web application to predict new data using the model and…
Continue ReadingBreaking the curse of small datasets in Machine Learning: Part 1
From the above figures, we can note that k-NN is highly influenced by the available data and more data may…
Continue ReadingBuild the Artificial Intelligence for detecting diabetes using Neural Networks and keras.
One approach focused on biological processes in the brain while the other focused on the application of neural networks to…
Continue ReadingTrade-offs: How to aim for the sweet spot.
Low Bias and high variance overfits data as the model pays way too much attention towards training data & doesn't…
Continue ReadingMonty Hall’s paradox — solve it by simulation!
D in our case is when the host choosing door B and there is no price behind it.Let’s create a…
Continue ReadingA Guide to Machine Learning in R for Beginners : Part 4
We will also study in detail about Linear Regression with code in RFunction: A function is a relationship where each…
Continue ReadingFirst Impressions of GPUs and PyData
Currently my favorite approach is to use Numpy functions as a lingua franca, and to allow the frameworks to hijack…
Continue ReadingHow to Do Data Science in your Company to Get The Most Out of it. Part II.
That is why we have started started to cut through projects from end-to-end right from the beginning with an initial…
Continue ReadingAll birds are black
And you’ve been asked (by your imaginary boss) to either:Describe how birds, in general, look like;Explain what it is that…
Continue ReadingAn Essential Guide to Numpy for Machine Learning in Python
Now as you might have guessed there would be many products which haven't been bought even a single time till…
Continue ReadingYou can’t just Google everything
But some questions hadn’t been asked, nor answered.After a movie is released, how much would a movie be able to…
Continue ReadingWhy Machine Learning is the BEST field in the world
On that day I decided that I want to work on things that it will be hard for me to…
Continue Reading30 Data Science Punchlines
30 Data Science PunchlinesA holiday reading list condensed into 30 quotesCassie KozyrkovBlockedUnblockFollowFollowingDec 20For those who like brainfood on your vacation, here’s…
Continue ReadingMachine Learning Explainability vs Interpretability: Two concepts that could help restore trust in AI
And how what do they actually mean for those of us doing data mining, analysis, science in 2019?In the context…
Continue ReadingThe Mathematics Behind Principal Component Analysis
The whole process of obtaining principle components from a raw dataset can be simplified in six parts :Take the whole dataset…
Continue ReadingMeasuring pedestrian accessibility
For now, let’s weight all amenities equally, and visualize distance to the fifth nearest amenity.plot_nearest_amenity('all',5)From network distances to walk timeLet’s make…
Continue ReadingMachine Learning and Music Classification: A Content-Based Filtering Approach
Clearly the Random Forest model was much more accurate than the K-Nearest Neighbors model, not surprising considering the simplicity of…
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