Santiago Profile picture
31 Jan, 9 tweets, 2 min read
"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: 👇
Part I - The Fundamentals of Machine Learning

▫️ What is machine learning?
▫️ Steps in a machine learning project
▫️ Learning by fitting a model to data
▫️ Optimizing a cost function
▫️ Handling, cleaning and preparing data
▫️ Selecting and engineering features
(...)
(...)
▫️ Selecting a model
▫️ Tuning hyperparameters
▫️ Cross-validation
▫️ Challenges with machine learning
▫️ Common learning algorithms
▫️ Curse of dimensionality
▫️ Unsupervised learning techniques
Part II - Neural Networks and Deep Learning

▫️ What is a neural net?
▫️ Building and training neural nets
▫️ TensorFlow and Keras
▫️ Feedforward neural nets
▫️ Convolutional networks
▫️ Recurrent networks
▫️ LSTM
▫️ Encoder / Decoders
▫️ Transformers
(...)
(...)
▫️ Autoencoders
▫️ Generative adversarial networks
▫️ Training techniques
▫️ Reinforcement learning
▫️ Loading and preprocessing a large amount of data
▫️ Training and deploying models at scale
When I bought the book, I felt intimidated by it: it's thick and heavy!

At the time, almost every machine learning book I had was hard to read —full of formulas and lingo.

This book is different.

This book is for you and me.
Now, I open it every week.

I'm either looking for something specific, or I simply read a couple of pages.

It never ceases to amaze me how much knowledge it packs!

Thanks, @aureliengeron! I really love what you did here!

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More from @svpino

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
28 Jan
If I were starting with machine learning today, I'd focus on learning how to use Jupyter right away.

It took me a while to understand the value of notebooks. That was a mistake.
I mentioned "Jupyter" in this tweet because it's the core that powers many notebook solutions out there.

Google Colab is an excellent tool!
I hear you. Notebooks are not a good fit for a lot of different tasks.

But they are uniquely well suited for running experiments, documenting the process, and presenting your results.

Read 4 tweets
28 Jan
Interviews aren't only about technical skills.

Here are some questions to help you prepare.

🧵👇
Explain what you have been working on for the past few weeks.

What are the most exciting parts about that work?

What portion of it do you consider boring and why?

[2 / 10]
What specific libraries and frameworks are you familiar with?

What's the minimum set of libraries and frameworks that you'd recommend to any practitioner?

[3 / 10]
Read 11 tweets
27 Jan
An easy change that will improve your communication immediately:

▫️ When explaining your progress, start from the end.

After that, take time to explain the next steps and go through the process you followed if needed.

🧵👇
If you only had a couple of sentences to communicate what you did, how would you structure the message?

You wouldn't want to waste it listing out every single step you went through, right?

There's a much better way.

[2 / 7]
You'll always be better off getting to the core of your findings right away.

▫️ What is the thing that everyone cares about?
▫️ What do they want to understand?

Only after that should you get into adjacent details.

[3 / 7]
Read 7 tweets
26 Jan
11 short programming problems to stretch your imagination and make sure you are staying on your toes.

(Starting with the simple ones, they get more fun as you move towards the end.)

🧵👇
1. Write a function that reverses an array in place.

In other words, the function should not use an auxiliary array to do the work.
2. Write a function that finds the missing number in an unsorted array containing every one of the other 99 numbers ranging from 1 to 100.
Read 14 tweets
25 Jan
After well over a decade fully dedicated to web development, I learned a thing or two.

Here are a few thoughts that may be helpful:

🧵👇
Obviously, HTML, JavaScript, and CSS are table stakes.

There are plenty of jobs out there where you only require these, but it's always smart to go beyond the basics.

Don't get comfortable because the money is coming in.
A good way of increasing your value is by adventuring outside your comfort zone.

Whenever you are ready, I'd recommend you look into:

▫️ Databases
▫️ REST APIs
▫️ UX

There's much more, but this is a great place to start.
Read 18 tweets

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