Share this page!

Santiago Profile picture
Twitter logo
31 Jan, 4 tweets, 1 min read
What's your favorite machine learning book?
This is my recommendation:

This edition of the book does indeed uses TensorFlow 2.0 (notice the top right corner of the picture.)

For some people, reading a book may not be the best approach.

For others —like me— books have a special place. Not only their days aren't gone, but they are as relevant as they've always been.

Everyone has a different method.

• • •

Missing some Tweet in this thread? You can try to force a refresh
　

Keep Current with Santiago

Santiago Profile picture

Stay in touch and get notified when new unrolls are available from this author!

Read all threads

This Thread may be Removed Anytime!

PDF

Twitter may remove this content at anytime! Save it as PDF for later use!

Try unrolling a thread yourself!

how to unroll video
  1. Follow @ThreadReaderApp to mention us!

  2. From a Twitter thread mention us with a keyword "unroll"
@threadreaderapp unroll

Practice here first or read more on our help page!

More from @svpino

Santiago Profile picture
2 Feb
For the past few months, I've been trying to improve the quality of the content I publish.

There are a couple of ways I'm measuring this:

▫️ Efficiency
▫️ Engagement

Efficiency is about how many impressions and followers I get for every tweet I post.

👇 Image
I've gone from posting 3,126 tweets back in August down to 949 tweets last month.

I've cut a lot of the noise!

During the same period, I've doubled my impressions (up to 14.4M last month,) and I'm now converting 5.38 followers for every tweet (up from 2.52.)

👇
The second way I'm watching the quality of the content I'm posting is through the engagement rate.

This has gone down quite a bit since August (almost cut in half!)

As impressions increase, the more pressure I have to put engaging content out there.

👇 Image
Read 7 tweets
Santiago Profile picture
2 Feb
Here is a full Python 🐍 implementation of a neural network from scratch in less than 20 lines of code!

It shows how it can learn 5 logic functions. (But it's powerful enough to learn much more.)

An excellent exercise in learning how feedforward and backpropagation work! Image
A quick rundown of the code:

▫️ X → input
▫️ layer → hidden layer
▫️ output → output layer
▫️ W1 → set of weights between X and layer
▫️ W2 → set of weights between layer and output
▫️ error → how far is our prediction after every epoch
I'm using a sigmoid as the activation function. You will recognize it through this formula:

sigmoid(x) = 1 / 1 + exp(-x)

It would have been nicer to extract it as a separate function, but then the code wouldn't be as compact 😉
Read 6 tweets
Santiago Profile picture
1 Feb
Time spent developing better datasets is usually more productive than squeezing the algorithms that process them.
One thing to keep in mind is that "better datasets" is not equivalent to "more data."

Regardless of your ability to collect the data, properly pre-processing it will usually give you a very good bang for your buck.

Hopefully, credit is given for the ultimate predictive ability of the solution.

A machine learning system is not just a model. There are a lot of pieces that need to work together.

Read 5 tweets
Santiago Profile picture
1 Feb
Here is a simple example of a machine learning model.

I put it together a long time ago, and it was very helpful! I sliced it apart a thousand times until things started to make sense.

It's TensorFlow and Keras.

If you are starting out, this may be a good puzzle to solve. Image
The goal of this model is to learn to multiply one-digit numbers.

The dataset has two values (the ones we want to multiply.) That's why the input shape is 2D.

The input shape represents the input layer of our model. It connects to the first hidden layer: a 4-unit Dense layer.

Then you get another 4-unit Dense layer.

Read 4 tweets
Santiago Profile picture
31 Jan
"Hands-On Machine Learning..." is —without a doubt— my favorite machine learning book.

It's not only a great reference, but it's the type of book that you can easily read cover to cover!

If you want to start from a solid foundation, look no further.

💰 amzn.to/2KPuRAo
The book assumes that you have some experience with:

▫️ Python programming
▫️ NumPy
▫️ Pandas
▫️ Matplotlib

For a deeper dive, it expects you to have a reasonable understanding of calculus, linear algebra, probabilities, and statistics.
The book is organized into two parts:

▫️ The Fundamentals of Machine Learning
▫️ Neural Networks and Deep Learning

Here is the outline of what's covered: 👇
Read 9 tweets
Santiago Profile picture
29 Jan
A few reasons you want to use notebooks (Jupyter Labs, Google Colab, etc.)

A thread:
1. The ability to run any cell independently of the rest of the code makes them really useful for running experiments and exploratory data analysis.
2. Each cell is cached, so you only need to run it once and never spend the same time running that code again.

This is especially useful for long-running operations. You run it once and reuse the results forever.
Read 6 tweets

Did Thread Reader help you today?

Support us! We are indie developers!

This site is made by just two indie developers on a laptop doing marketing, support and development! Read more about the story.

Become a Premium Member ($3/month or $30/year) and get exclusive features!

Become Premium

Too expensive? Make a small donation by buying us coffee ($5) or help with server cost ($10)

Donate via Paypal Become our Patreon

Thank you for your support!

Follow Us on Twitter!

Like this author's thread?

Get emails when @svpino has new unrolls!

Check out other threads from @svpino!

Read all threads by @svpino