What will you Learn in this Post? Neural Networks are function approximators. For example, in a supervised learning setting, given loads of inputs and loads of outputs in our training data, a neural network is capable of finding the hidden mapping between the those inputs and outputs. It can also, hopefully, generalize its acquired knowledge …
Train a Perceptron to Learn the AND Gate from Scratch in Python Read More »
Hello friends and welcome to MLDawn! So what sometimes concerns me is the fact that magnificent packages such as PyTorch (indeed amazing), TensorFlow (indeed a nightmare), and Keras (indeed great), have prevented machine learning enthusiasts to really learn the science behind how the machine learns. To be more accurate, there is nothing wrong with having …
This talk was delivered in PyData meetup. This was an amazing meetup with great talks and ideas being discussed! Thanks to organizers, I was also invited to give a talk. Below is the summary: In this talk we learn about what Artificial Neural Networks (ANNs) are, and find out how in general, Maximum Likelihood …
Birth of Error Functions in Artificial Neural Networks Read More »
Hello friends and welcome to MLDawn! So what sometimes concerns me is the fact that magnificent packages such as PyTorch (indeed amazing), TensorFlow (indeed a nightmare), and Keras (indeed great), have prevented machine learning enthusiasts to really learn the science behind how the machine learns. To be more accurate, there is nothing wrong with having …
What is this post about? In the previous post, we learned all about what over-fitting is, and why over-fitting to our training data could easily hinder the generalization power of the final trained model to the future unseen test data. In particular, we said that there are 2 cases where over-fitting can harm us: If …
What is this post about, and what is next? Hi there!!! Let’s cut to the chase, shall we?! You have this decision tree algorithm, coded beautifully through Scikitlearn (I applaud you!), or from scratch using numpy (I am proud of you!). You let the bloody thing train, and train and you see the training error …
Which attribute to choose? (Part-2) Today we are going to touch on something quite exciting. In our previous post we talked about Entropy of a set, E(S), and told you that entropy is nothing but a quantitative measure of how mixed up our set is! I also showed you that regardless of how the decision …
Which Attribute to Choose? (Part1)
1 2 3 4 5 | In our last post,&nbsp; we introduced the idea of the decision trees (DTs) and you understood the big picture. Now it is time to get into some of the details. For example, how does a DT choose the best attribute from the dataset? There must be a way for the DT to compare the worth of each attribute and figure out which attribute can help us get to more pure sub-tables (i.e., more certainty). There is indeed a famous quantitative measure for this called Information Gain. But in order to understand it, we have to first learn about the concept of Entropy. As a reminder here is our training set:</p> <img width="1024" height="580" src="https://www.mldawn.com/wp-content/uploads/2019/02/which-attribute-to-choose-1024x580.png" alt="" srcset="https://www.mldawn.com/wp-content/uploads/2019/02/which-attribute-to-choose-1024x580.png 1024w, https://www.mldawn.com/wp-content/uploads/2019/02/which-attribute-to-choose-300x170.png 300w, https://www.mldawn.com/wp-content/uploads/2019/02/which-attribute-to-choose-768x435.png 768w, https://www.mldawn.com/wp-content/uploads/2019/02/which-attribute-to-choose.png 1092w" sizes="(max-width: 1024px) 100vw, 1024px" /> <h2>What is Entropy?</h2> Entropy of a set of examples, can tell us how pure that set is! For example, if we have 2 sets of fruits: 1) 5 apples 5 oranges, and 2) 9 apples and 1 orange, we say that set 2 is much more pure (i.e., has much less entropy) than set 1 as it almost purely consists of apples. However, set 1 is a half-half situation and is so impure (i.e., has much more entropy) as neither apples nor oranges can dominate! Now, back to the adults' world and enough with fruits :-) |
In a binary classification problem, such as the dataset above, we have 2 sets of examples 1) Positive (Yes) and 2) Negative (No), and this is how we compute the entropy of our dataset: Please note that the base of the Logarithm is 2. As for the proportions, let’s …
What will you learn? You will learn how a decision tree works but without going to the details. For example, you will NOT learn about Entropy and Information Gain in this post. However, you will have a broad and nice understanding about how a decision tree works. I do hope that you will find it …
The Decision Tree Algorithm: A Gentle Introduction Read More »
A Quick Recap Hello everyone and welcome! In our previous post, we talked about the first algorithm that uses the “more-general-thank-or-equal-to” operation to smooth out the search in the hypothesis space: Find-S Algorithm. As a reminder, below you can see the steps in this algorithm: Like all the other algorithms, and I mean it when …
Concept Learning and General to Specific Ordering-Part(5) Read More »