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Santiago Profile picture
@svpino
Sep 15, 2020 • 5 tweets • 2 min read • Read on X
TensorFlow is currently the most popular end-to-end platform for Machine Learning.

Here you have a free 7-hour TensorFlow 2.0 course that's packed with everything you need to get started.

(A single hour per day can get you through this course in a week! Just one week!)

🧵👇
The course is structured in 8 different modules that cover different aspects of Machine Learning and focus on how to apply TensorFlow 2.0 to solve different problems.

Here is the list of modules:

1⃣ Machine Learning Fundamentals
2⃣ Introduction to TensorFlow

👇
3⃣ Core Learning Algorithms
4⃣ Neural Networks with TensorFlow
5⃣ Deep Computer Vision - Convolutional Neural Networks
6⃣ Natural Language Processing with RNNs
7⃣ Reinforcement Learning with Q-Learning
8⃣ Conclusion and Next Steps

👇
In the video description, you'll find a set of Google Colab notebooks with all the code discussed in the modules.

This is an incredible resource that you get for free and will get you started in one of the most exciting open-source tools in the market today!

👇
Title: TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners Tutorial.

YouTube Link:

The video was filmed by Tim from @TechWithTimm in conjunction with @freeCodeCamp and publish on their youtube channel. (Thank you so much for this!)

👇

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More from @svpino

Santiago Profile picture
Sep 16
How can you build a good understanding of math for machine learning?

Here is a complete roadmap for you.

In essence, three fields make this up:

• Calculus
• Linear algebra
• Probability theory

Let's take a quick look at them! Image
This thread is courtesy of @TivadarDanka.

3 years ago, he started writing a book about the mathematics of Machine Learning.

It's the best book you'll ever read:



Nobody explains complex ideas like he does.tivadardanka.com/books/mathemat…
1. Linear algebra.

In machine learning, data is represented by vectors. Essentially, training a learning algorithm is finding more descriptive representations of data through a series of transformations.

Linear algebra is the study of vector spaces and their transformations. Image
Read 9 tweets
Santiago Profile picture
Aug 12
The single most undervalued fact of linear algebra:

Matrices are graphs, and graphs are matrices.

Encoding matrices as graphs is a cheat code, making complex behavior simple to study.

Let me show you how! Image
By the way, this thread is courtesy of @TivadarDanka. He allowed me to republish it.

3 years ago, he started writing a book about the mathematics of Machine Learning.

It's the best book you'll ever read:



Nobody explains complex ideas like he does.tivadardanka.com/books/mathemat…
If you look at this example, you probably figured out the rule.

Each row is a node, and each element represents a directed and weighted edge. We omit any edges of zero elements.

The element in the 𝑖-th row and 𝑗-th column corresponds to an edge going from 𝑖 to 𝑗. Image
Read 18 tweets
Santiago Profile picture
Jul 12
A common fallacy:

If it's raining, the sidewalk is wet. But if the sidewalk is wet, is it raining?

Reversing the implication is called "affirming the consequent." We usually fall for this.

But surprisingly, it's not entirely wrong!

Let's explain it using Bayes Theorem:

1/10 Image
This explanation is courtesy of @TivadarDanka. He allowed me to republish it.

He is writing a book about the mathematics of Machine Learning. It's the best book I've read:



Nobody explains complex ideas like he does.

2/10tivadardanka.com/books/mathemat…
We call propositions of the form "if A, then B" implications.

We write them as "A → B," and they form the bulk of our scientific knowledge.

For example:

"If X is a closed system, then the entropy of X cannot decrease" is the second law of thermodynamics.

3/10
Read 10 tweets
Santiago Profile picture
Jun 12
Some of the skills you need to start building AI applications:

• Python and SQL
• Transformer and diffusion models
• LLMs and fine-tuning
• Retrieval Augmented Generation
• Vector databases

Here is one of the most comprehensive programs that you'll find online:
"Generative AI for Software Developers" is a 4-month online course.

It's a 5 to 10-hour weekly commitment, but you can dedicate as much time as you want to finish early.

Here is the link to the program:

I also have a PDF with the syllabus:bit.ly/4aNOJdy


I'm a huge fan of online education, but most of it is all over the place and mostly theoretical.

This program is different:

You'll work on 4 different hands-on projects. You'll learn practical skills you can use at the office right away.cdn.sanity.io/files/tlr8oxjg…
Read 6 tweets
Santiago Profile picture
Jun 10
There's a stunning, simple explanation behind matrix multiplication.

This is the first time this clicked on my brain, and it will be the best thing you read all week.

Here is a breakdown of the most crucial idea behind modern machine learning:

1/15 Image
This explanation is courtesy of @TivadarDanka. He allowed me to republish it

3 years ago, he started writing a book about the mathematics of Machine Learning.

It's the best book you'll ever read:



Nobody explains complex ideas like he does.

2/15tivadardanka.com/books/mathemat…
Let's start with the raw definition of the product of A and B.

This looks horrible and complicated.

Let's unwrap it step by step.

3/15 Image
Read 15 tweets
Santiago Profile picture
May 28
This assistant has 169 lines of code:

• Gemini Flash
• OpenAI Whisper
• OpenAI TTS API
• OpenCV

GPT-4o is slower than Flash, more expensive, chatty, and very stubborn (it doesn't like to stick to my prompts).

Next week, I'll post a step-by-step video on how to build this.
The first request takes longer (warming up), but things work faster from that point.

Few opportunities to improve this:

1. Stream answers from the model (instead of waiting for the full answer.)

2. Add the ability to interrupt the assistant.

3. Whisper running on GPU
Unfortunately, no local modal supports text+images (as far as I know,) so I'm stuck running online models.

The TTS API (synthesizing text to audio) can also be replaced by a local version. I tried, but the available voices suck (too robotic), so I kept OpenAI's.
Read 4 tweets

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