BC MACHINE LEARNING

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Jan Giacomelli
@jangiacomelli 5 years, 9 months ago 1512 views

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🤔 Python decorators

What are they? How do you use them?

🧵 Let's find out 👇

1️⃣ Decorator is a function or a class that wraps another function or class modifying its behavior.

So how does that work?

The first thing to know is that everything in Python is an object - functions too

2️⃣ That means they can be passed to another function as an argument or returned from a function

Functions that take other functions as an argument are called higher-order functions

3️⃣ In Python, you can define a function inside other function - such functions are called inner functions

4️⃣ To create a decorator you just need to apply all of that together

log_enter_leave is a decorator.

my_function is the function.

To alter my_function's behavior we reassign it applying log_enter_leave decorator.

5️⃣ To simplify usage of decorators Python offers us syntactic sugar

A "pie-decorator" syntax using @

@decorator_name

6️⃣ The only problem here is that my_function now identify as the wrapper function

To solve that we just need to use wraps from functools

7️⃣ You can decorate classes too.

For example, you can dataclass decorator on your class to automatically generate its __init__ and __repr__ methods

8️⃣ You can also use a class as a decorator

Decorator class needs methods:
- __init__
- __call__ (it makes class callable)

9️⃣ For example, decorators are used for registering view functions to the Flask application

1️⃣0️⃣ Read more:

https://t.co/eBMh1Gv0xZ

https://t.co/2YdA2ZIQoV

https://t.co/Wb0jSjmzlc

1️⃣1️⃣ Script with all of the examples:

https://t.co/Tw1qrbN0nN

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Can you get a job in data science and machine learning without a college degree?

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Short Answer: Yes.

Long Answer, keep reading 👇

(Advice from industry experts who I talked to.)

Companies are looking for people who add value.
In order to add value, you'll need skills. Simple as that.

Get the skills to provide value and you'll get the job.

In the age of the internet where everything is pretty much free, why do college degrees matter?

A college degree makes it easier to get the skills to get a job in machine learning or data science.

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College degrees have their own place

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Important paper from Google on large batch optimization. They do impressively careful experiments measuring # iterations needed to achieve target validation error at various batch sizes. The main "surprise" is the lack of surprises. [thread]

https://t.co/7QIx5CFdfJ

The paper is a good example of lots of elements of good experimental design. They validate their metric by showing lots of variants give consistent results. They tune hyperparamters separately for each condition, check that optimum isn't at the endpoints, and measure sensitivity.

They have separate experiments where the hold fixed # iterations and # epochs, which (as they explain) measure very different things. They avoid confounds, such as batch norm's artificial dependence between batch size and regularization strength.

When the experiments are done carefully enough, the results are remarkably consistent between different datasets and architectures. Qualitatively, MNIST behaves just like ImageNet.

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Pratham Prasoon
@PrasoonPratham

Here's everything you need to know about the math for machine learning.

(+ free resources )
🧵👇

Before diving into the math, I suggest first having solid programming skills.

For example👇

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In Python, these are the concepts which you must know:

- Object oriented programming in Python : Classes, Objects, Methods
- List slicing
- String formatting
- Dictionaries & Tuples
- Basic terminal commands
- Exception handling

(3 / 18)

If you want to learn those concepts for python, these courses are freecodecamp could be of help to you.

🔗Basics:youtube∙com/watch?v=rfscVS0vtbw
🔗Intermediate :youtube∙com/watch?v=HGOBQPFzWKo

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👉You need to have really strong fundamentals in programming, because machine learning involves a lot of it.

It is 100% compulsory.

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Pratham Prasoon
@PrasoonPratham

A simple guide on how to train your first neural network in under 10 minutes to kick-start your machine learning journey.

(+ no setup required and free extra resources)
🧵👇

For this exercise, you'll need :
- A computer/phone
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What you do not need:
- Complex math
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Math will come later on in your machine learning journey :)

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Here's our problem,we are given data in which we are given the number of flats in a house and its corresponding price. Like a house with one flat is worth 10000, and 20000 for a house with 2 flats.

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We can clearly tell that the price of the house increases by 10000 per extra flat however our computer does not know this and we won't it tell it about this, it'll have to figure things out on its own.

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Here's the code👇

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Santiago
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An introduction to one of the the most basic structures used in machine learning: a tensor.

🧵👇

Tensors are the data structure used by machine learning systems, and getting to know them is an essential skill you should build early on.

A tensor is a container for numerical data. It is the way we store the information that we'll use within our system.

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Three primary attributes define a tensor:

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The rank of a tensor refers to the tensor's number of axes.

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