🤑📈 Top 15 Free Data Science Courses to Kick Start your Data Science Journey!

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1. Introduction to AI and ML

“The AI revolution is here – are you prepared to integrate it into your skillset? How can you leverage it in your current role? What are the different facets of AI and ML?”

courses.analyticsvidhya.com/courses/introd…

“The AI revolution is here – are you prepared to integrate it into your skillset? How can you leverage it in your current role? What are the different facets of AI and ML?”

courses.analyticsvidhya.com/courses/introd…

2. Introduction to Python

Do you want to enter the field of Data Science? Are you intimidated by the coding you would need to learn? Are you looking to learn Python to switch to a data science career?

courses.analyticsvidhya.com/courses/introd…

Do you want to enter the field of Data Science? Are you intimidated by the coding you would need to learn? Are you looking to learn Python to switch to a data science career?

courses.analyticsvidhya.com/courses/introd…

Learn Data Science in 180 days🤑📈 and start your data science career.

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First Month 🗓️

Day 1 to 15 - Learn Python for Data Science

Day 16 to 30 - Learn Statistics for Data Science

Day 1 to 15 - Learn Python for Data Science

Day 16 to 30 - Learn Statistics for Data Science

Second Month 🗓️

Day 31 to 45 - Explore Python Packages( Numpy, Pandas, Matplotlib, Seaborn, Scikit-Learn)

Day 16 to 30 - Implement EDA on real-world datasets.

Day 31 to 45 - Explore Python Packages( Numpy, Pandas, Matplotlib, Seaborn, Scikit-Learn)

Day 16 to 30 - Implement EDA on real-world datasets.

🧵 ¿Dónde aprender sobre IA y Machine Learning GRATIS?

Además, podés aprender sobre programación, SQL, visualización, etc.

kaggle.com/learn

kaggle.com/learn

AI, ML, deep learning y procesamiento de lenguaje natural:

deeplearning.ai/courses/

deeplearning.ai/courses/

1/ "Software is eating the world. Machine learning is eating software. Transformers are eating machine learning."

Let's understand what these Transformers are all about

#DataScience #MachineLearning #DeepLearning #100DaysOfMLCode #Python #pythoncode #AI #DataAnalytics

Let's understand what these Transformers are all about

#DataScience #MachineLearning #DeepLearning #100DaysOfMLCode #Python #pythoncode #AI #DataAnalytics

2/ #Transformers architecture follows Encoder and Decoder structure.

The encoder receives input sequence and creates intermediate representation by applying embedding and attention mechanism.

#DataScience #MachineLearning #DeepLearning #100DaysOfMLCode #Python #pythoncode #AI

The encoder receives input sequence and creates intermediate representation by applying embedding and attention mechanism.

#DataScience #MachineLearning #DeepLearning #100DaysOfMLCode #Python #pythoncode #AI

3/ Then, this intermediate representation or hidden state will pass through the decoder, and the decoder starts generating an output sequence.

#DataScience #MachineLearning #DeepLearning #100DaysOfMLCode #Python #pythoncode #AI #DataScientist #DataAnalytics #Statistics

#DataScience #MachineLearning #DeepLearning #100DaysOfMLCode #Python #pythoncode #AI #DataScientist #DataAnalytics #Statistics

But what p-value means in #MachineLearning - A thread

It tells you how likely it is that your data could have occurred under the null hypothesis

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#DataScience #DeepLearning #ComputerVision #100DaysOfMLCode #Python #DataScientist #Statistics #programming #Data #Math #Stat

It tells you how likely it is that your data could have occurred under the null hypothesis

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#DataScience #DeepLearning #ComputerVision #100DaysOfMLCode #Python #DataScientist #Statistics #programming #Data #Math #Stat

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What Is a Null Hypothesis?

A null hypothesis is a type of statistical hypothesis that proposes that no statistical significance exists in a set of given observations.

#DataScience #MachineLearning #100DaysOfMLCode #Python #stat #Statistics #Data #AI #Math #deeplearning

What Is a Null Hypothesis?

A null hypothesis is a type of statistical hypothesis that proposes that no statistical significance exists in a set of given observations.

#DataScience #MachineLearning #100DaysOfMLCode #Python #stat #Statistics #Data #AI #Math #deeplearning

3/n

A P-value is the probability of obtaining an effect at least as extreme as the one in your sample data, assuming the truth of the null hypothesis

#DataScience #MachineLearning #100DaysOfMLCode #Python #DataScientist #Statistics #Data #DataAnalytics #AI #Math

A P-value is the probability of obtaining an effect at least as extreme as the one in your sample data, assuming the truth of the null hypothesis

#DataScience #MachineLearning #100DaysOfMLCode #Python #DataScientist #Statistics #Data #DataAnalytics #AI #Math

1/ One way to test whether a time series is stationary is to perform an augmented Dickey-Fuller test - A Thread

#DataScience #MachineLearning #DeepLearning #100DaysOfMLCode #Python #pythoncode #AI #DataScientist #DataAnalytics #Statistics #programming #ArtificialIntelligence

#DataScience #MachineLearning #DeepLearning #100DaysOfMLCode #Python #pythoncode #AI #DataScientist #DataAnalytics #Statistics #programming #ArtificialIntelligence

2/ H0: The time series is non-stationary. In other words, it has some time-dependent structure and does not have constant variance over time.

HA: The time series is stationary.

#DataScience #MachineLearning #DeepLearning #100DaysOfMLCode #Python #pythoncode #AI #DataScientist

HA: The time series is stationary.

#DataScience #MachineLearning #DeepLearning #100DaysOfMLCode #Python #pythoncode #AI #DataScientist

3/ If the p-value from the test is less than some significance level (e.g. α = .05), then we can reject the null hypothesis and conclude that the time series is stationary.

#DataScience #MachineLearning #DeepLearning #100DaysOfMLCode #Python #pythoncode #AI #DataScientist

#DataScience #MachineLearning #DeepLearning #100DaysOfMLCode #Python #pythoncode #AI #DataScientist

Kullback-Leibler (KL) Divergence - A Thread

It is a measure of how one probability distribution diverges from another expected probability distribution.

#DataScience #Statistics #DeepLearning #ComputerVision #100DaysOfMLCode #Python #programming #ArtificialIntelligence #Data

It is a measure of how one probability distribution diverges from another expected probability distribution.

#DataScience #Statistics #DeepLearning #ComputerVision #100DaysOfMLCode #Python #programming #ArtificialIntelligence #Data

1/ When it is important to standardize variables in #DataScience #MachineLearning ? - A Thread

#DeepLearning #100DaysOfMLCode #Python #pythoncode #AI #DataScientist #DataAnalytics #Statistics #programming #ArtificialIntelligence #Data #Stats #Database #BigData #100DaysOfCode

#DeepLearning #100DaysOfMLCode #Python #pythoncode #AI #DataScientist #DataAnalytics #Statistics #programming #ArtificialIntelligence #Data #Stats #Database #BigData #100DaysOfCode

2/ It is important to standardize variables before running Cluster Analysis. It is because cluster analysis techniques depend on the concept of measuring the distance between the different observations we're trying to cluster.

#DataScience #MachineLearning #DeepLearning

#DataScience #MachineLearning #DeepLearning

3/ If a variable is measured at a higher scale than the other variables, then whatever measure we use will be overly influenced by that variable.

#DataScience #MachineLearning #DeepLearning #100DaysOfMLCode #Python #pythoncode #AI #DataScientist #DataAnalytics #Statistics

#DataScience #MachineLearning #DeepLearning #100DaysOfMLCode #Python #pythoncode #AI #DataScientist #DataAnalytics #Statistics

Did you know how TensorFlow can run on a single mobile device as well as on an entire data center? Read this thread

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#TensorFlow #DataScience #DeepLearning #MachineLearning #ComputerVision #100DaysOfMLCode #Python #DataScientist #Statistics #programming #Data

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#TensorFlow #DataScience #DeepLearning #MachineLearning #ComputerVision #100DaysOfMLCode #Python #DataScientist #Statistics #programming #Data

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Google has designed TensorFlow such that it is capable of dividing a large model graph whenever needed.

#TensorFlow #DataScience #DeepLearning #MachineLearning #ComputerVision #100DaysOfMLCode #Python #DataScientist #Statistics #programming #Data #Math #Stat #AI

Google has designed TensorFlow such that it is capable of dividing a large model graph whenever needed.

#TensorFlow #DataScience #DeepLearning #MachineLearning #ComputerVision #100DaysOfMLCode #Python #DataScientist #Statistics #programming #Data #Math #Stat #AI

3/n

It assigns special SEND and RECV nodes whenever a graph is divided between multiple devices (CPUs or GPUs).

#TensorFlow #DataScience #DeepLearning #MachineLearning #ComputerVision #100DaysOfMLCode #Python #DataScientist #Statistics #programming #Data #Math #Stat #AI

It assigns special SEND and RECV nodes whenever a graph is divided between multiple devices (CPUs or GPUs).

#TensorFlow #DataScience #DeepLearning #MachineLearning #ComputerVision #100DaysOfMLCode #Python #DataScientist #Statistics #programming #Data #Math #Stat #AI

A thread on AUC Score (Area under the ROC Curve) Interpretation in #DataScience #MachineLearning

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#DeepLearning #100DaysOfMLCode #Python #DataScientist #Statistics #programming #Math #Data #DataAnalytics #pythoncode #AI #ArtificialIntelligence #TensorFlow #PyTorch #Pandas

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#DeepLearning #100DaysOfMLCode #Python #DataScientist #Statistics #programming #Math #Data #DataAnalytics #pythoncode #AI #ArtificialIntelligence #TensorFlow #PyTorch #Pandas

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"roc_auc_score" is defined as the area under the ROC curve, which is the curve having False Positive Rate on the x-axis and True Positive Rate on the y-axis at all classification thresholds.

#DataScience #MachineLearning #DeepLearning #100DaysOfMLCode #Python

"roc_auc_score" is defined as the area under the ROC curve, which is the curve having False Positive Rate on the x-axis and True Positive Rate on the y-axis at all classification thresholds.

#DataScience #MachineLearning #DeepLearning #100DaysOfMLCode #Python

3/16

AUC ranges in value from 0 to 1.

#DataScience #MachineLearning #DeepLearning #100DaysOfMLCode #Python #DataScientist #Statistics #programming #Math #Data #DataAnalytics #pythoncode #AI #ArtificialIntelligence #TensorFlow #PyTorch #Pandas #Stat #dataviz #learning

AUC ranges in value from 0 to 1.

#DataScience #MachineLearning #DeepLearning #100DaysOfMLCode #Python #DataScientist #Statistics #programming #Math #Data #DataAnalytics #pythoncode #AI #ArtificialIntelligence #TensorFlow #PyTorch #Pandas #Stat #dataviz #learning

Outlier Detection with Alibi Detect Library - A Thread

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#DataScience #DeepLearning #MachineLearning #ComputerVision #100DaysOfMLCode #Python #DataScientist #Statistics #programming #Data #Math #Stat #pythoncode #AI #ArtificialIntelligence

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#DataScience #DeepLearning #MachineLearning #ComputerVision #100DaysOfMLCode #Python #DataScientist #Statistics #programming #Data #Math #Stat #pythoncode #AI #ArtificialIntelligence

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Alibi Detect is a Python library for detecting outliers, adversarial data, and drift. Accommodates tabular data, text, images, and time series that can be used both online and offline. Both TensorFlow and PyTorch backends are supported

#DataScience #DeepLearning

Alibi Detect is a Python library for detecting outliers, adversarial data, and drift. Accommodates tabular data, text, images, and time series that can be used both online and offline. Both TensorFlow and PyTorch backends are supported

#DataScience #DeepLearning

3/n

Supports a variety of outlier detection techniques, including Mahalanobis distance, Isolation forest, and Seq2seq

#DataScience #DeepLearning #MachineLearning #ComputerVision #100DaysOfMLCode #Python #DataScientist #Statistics #programming #Data #Math #Stat #pythoncode

Supports a variety of outlier detection techniques, including Mahalanobis distance, Isolation forest, and Seq2seq

#DataScience #DeepLearning #MachineLearning #ComputerVision #100DaysOfMLCode #Python #DataScientist #Statistics #programming #Data #Math #Stat #pythoncode

1/ Can you classify something without seeing it before - that's what Zero-Shot Learning is all about - A Thread

👉 One of the popular methods for zero-shot learning is Natural Language Inference (NLI).

#DataScience #DeepLearning #MachineLearning #100DaysOfMLCode #Pytho

👉 One of the popular methods for zero-shot learning is Natural Language Inference (NLI).

#DataScience #DeepLearning #MachineLearning #100DaysOfMLCode #Pytho

2/ Zero-shot learning is when we test a model on a task for which it hasn't been trained.

#DataScience #MachineLearning #100DaysOfMLCode #DataScientist #Statistics #programming #ArtificialIntelligence #Data #pythoncode #AI #Stats #DeepLearning #100DaysOfCode #Database #BigData

#DataScience #MachineLearning #100DaysOfMLCode #DataScientist #Statistics #programming #ArtificialIntelligence #Data #pythoncode #AI #Stats #DeepLearning #100DaysOfCode #Database #BigData

3/ In Zero-shot classification, we ask the model to classify a sentence to one of the classes (label) that the model hasn't seen during training.

#DataScience #MachineLearning #100DaysOfMLCode #DataScientist #Statistics #programming #ArtificialIntelligence #Data #pythoncode #AI

#DataScience #MachineLearning #100DaysOfMLCode #DataScientist #Statistics #programming #ArtificialIntelligence #Data #pythoncode #AI

1/ Why do we need the bias term in ML algorithms such as linear regression and neural networks ? - A thread

#DataScience #MachineLearning #100DaysOfMLCode #DataScientist #Statistics #programming #ArtificialIntelligence #Data #pythoncode #AI #Stats #DeepLearning #100DaysOfCode

#DataScience #MachineLearning #100DaysOfMLCode #DataScientist #Statistics #programming #ArtificialIntelligence #Data #pythoncode #AI #Stats #DeepLearning #100DaysOfCode

2/ In linear regression, without the bias term your solution has to go through the origin. That is, when all of your features are zero, your predicted value would also have to be zero.

#DataScience #MachineLearning #100DaysOfMLCode #DataScientist #Statistics #programming

#DataScience #MachineLearning #100DaysOfMLCode #DataScientist #Statistics #programming

3/ However that may not be the attributes of the training dataset.

#DataScience #MachineLearning #100DaysOfMLCode #DataScientist #Statistics #programming #ArtificialIntelligence #Data #pythoncode #AI #Stats #DeepLearning #100DaysOfCode #Database #BigData #DataAnalytics

#DataScience #MachineLearning #100DaysOfMLCode #DataScientist #Statistics #programming #ArtificialIntelligence #Data #pythoncode #AI #Stats #DeepLearning #100DaysOfCode #Database #BigData #DataAnalytics

Google has released Imagen: a text-to-image diffusion model with an unprecedented degree of photorealism and a deep level of language understanding

#Python3 #MachineLearning #DataScience #100DaysOfCode #DataScience #DataAnalytics #100DaysOfMLCode #DataScientist #Statistics

#Python3 #MachineLearning #DataScience #100DaysOfCode #DataScience #DataAnalytics #100DaysOfMLCode #DataScientist #Statistics

Imagen builds on the power of large transformer language models in understanding text and hinges on the strength of diffusion models in high-fidelity image generation.

#Python3 #MachineLearning #DataScience #100DaysOfCode #DataScience #DataAnalytics #100DaysOfMLCode

#Python3 #MachineLearning #DataScience #100DaysOfCode #DataScience #DataAnalytics #100DaysOfMLCode

This generator is scarily accurate with super-resolution! "A photo of a raccoon wearing an astronaut helmet, looking out of the window at night."

#Python3 #MachineLearning #DataScience #100DaysOfCode #DataScience #DataAnalytics #100DaysOfMLCode #DataScientist #Statistics

#Python3 #MachineLearning #DataScience #100DaysOfCode #DataScience #DataAnalytics #100DaysOfMLCode #DataScientist #Statistics

What is p-value - A thread

It tells you how likely it is that your data could have occurred under the null hypothesis.

1/n

#DataScience #DeepLearning #MachineLearning #ComputerVision #100DaysOfMLCode #Python #DataScientist #Statistics #programming #Data #Math #Stat

It tells you how likely it is that your data could have occurred under the null hypothesis.

1/n

#DataScience #DeepLearning #MachineLearning #ComputerVision #100DaysOfMLCode #Python #DataScientist #Statistics #programming #Data #Math #Stat

2/n

What Is a Null Hypothesis?

A null hypothesis is a type of statistical hypothesis that proposes that no statistical significance exists in a set of given observations.

#DataScience #MachineLearning #100DaysOfMLCode #Python #stat #Statistics #Data #AI #Math #deeplearning

What Is a Null Hypothesis?

A null hypothesis is a type of statistical hypothesis that proposes that no statistical significance exists in a set of given observations.

#DataScience #MachineLearning #100DaysOfMLCode #Python #stat #Statistics #Data #AI #Math #deeplearning

3/n

A P-value is the probability of obtaining an effect at least as extreme as the one in your sample data, assuming the truth of the null hypothesis

#DataScience #MachineLearning #100DaysOfMLCode #Python #DataScientist #Statistics #Data #DataAnalytics #AI #Math

A P-value is the probability of obtaining an effect at least as extreme as the one in your sample data, assuming the truth of the null hypothesis

#DataScience #MachineLearning #100DaysOfMLCode #Python #DataScientist #Statistics #Data #DataAnalytics #AI #Math

1/ #MachineLearning #Interview questsion -

Why L1 regularizations causes parameter sparsity whereas L2 regularization does not?

#DataScience #MachineLearning #100DaysOfMLCode #DataScientist #Statistics #programming #ArtificialIntelligence #Data #pythoncode #AI #Stats

Why L1 regularizations causes parameter sparsity whereas L2 regularization does not?

#DataScience #MachineLearning #100DaysOfMLCode #DataScientist #Statistics #programming #ArtificialIntelligence #Data #pythoncode #AI #Stats

2/ L1 & L2 regularization add constraints to the optimization problem. The curve H0 is the hypothesis. The solution to this system is the set of points where the H0 meets the constraints.

#DataScience #MachineLearning #100DaysOfMLCode #DataScientist #Statistics #programming

#DataScience #MachineLearning #100DaysOfMLCode #DataScientist #Statistics #programming

3/ Regularizations in statistics or in the field of machine learning is used to include some extra information in order to solve a problem in a better way.

#DataScience #MachineLearning #100DaysOfMLCode #DataScientist #Statistics #programming #ArtificialIntelligence #Data

#DataScience #MachineLearning #100DaysOfMLCode #DataScientist #Statistics #programming #ArtificialIntelligence #Data

A thread on AUC Score (Area under the ROC Curve) Interpretation in #DataScience #MachineLearning

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#DeepLearning #100DaysOfMLCode #Python #DataScientist #Statistics #programming #Math #Data #DataAnalytics #pythoncode #AI #ArtificialIntelligence #TensorFlow #PyTorch #Pandas

1/16

#DeepLearning #100DaysOfMLCode #Python #DataScientist #Statistics #programming #Math #Data #DataAnalytics #pythoncode #AI #ArtificialIntelligence #TensorFlow #PyTorch #Pandas

2/16

"roc_auc_score" is defined as the area under the ROC curve, which is the curve having False Positive Rate on the x-axis and True Positive Rate on the y-axis at all classification thresholds.

#DataScience #MachineLearning #DeepLearning #100DaysOfMLCode #Python

"roc_auc_score" is defined as the area under the ROC curve, which is the curve having False Positive Rate on the x-axis and True Positive Rate on the y-axis at all classification thresholds.

#DataScience #MachineLearning #DeepLearning #100DaysOfMLCode #Python

3/16

AUC ranges in value from 0 to 1.

#DataScience #MachineLearning #DeepLearning #100DaysOfMLCode #Python #DataScientist #Statistics #programming #Math #Data #DataAnalytics #pythoncode #AI #ArtificialIntelligence #TensorFlow #PyTorch #Pandas #Stat #dataviz #learning

AUC ranges in value from 0 to 1.

#DataScience #MachineLearning #DeepLearning #100DaysOfMLCode #Python #DataScientist #Statistics #programming #Math #Data #DataAnalytics #pythoncode #AI #ArtificialIntelligence #TensorFlow #PyTorch #Pandas #Stat #dataviz #learning

Some tips to increase the Performance of Your Object Detection Models - A trhead

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#computervision #pytorch #deeplearning #deeplearningai #100daysofmlcode #neuralnetworks #machinelearning #datascience #pythonprogramming #python #AI #ArtificialIntelligence #pythonprogramming

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#computervision #pytorch #deeplearning #deeplearningai #100daysofmlcode #neuralnetworks #machinelearning #datascience #pythonprogramming #python #AI #ArtificialIntelligence #pythonprogramming

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Following tips may boost model performance across different network structures with up to 5% (mAP or mean Average Precision) without increasing computational costs in any way.

#computervision #pytorch #deeplearning #deeplearningai #100daysofmlcode #neuralnetworks #AI

Following tips may boost model performance across different network structures with up to 5% (mAP or mean Average Precision) without increasing computational costs in any way.

#computervision #pytorch #deeplearning #deeplearningai #100daysofmlcode #neuralnetworks #AI

3/n

Visually Coherent Image Mix-up for Object Detection. This has already been proven to be successful in lessening adversarial fears in network classification after testing it on COCO 2017 and PASCAL datasets with YOLOv3 models.

#computervision #pytorch

Visually Coherent Image Mix-up for Object Detection. This has already been proven to be successful in lessening adversarial fears in network classification after testing it on COCO 2017 and PASCAL datasets with YOLOv3 models.

#computervision #pytorch

20 GitHub Repos that Helps You to Win Hacktoberfest

A thread 🧵 👇

A thread 🧵 👇

1. Free Programming Books

In this repository, you can contribute by sharing free e-books books 📚

Discover free programming books from different languages, contribute your favourite ones if missing, making it more valuable.

github.com/EbookFoundatio…

In this repository, you can contribute by sharing free e-books books 📚

Discover free programming books from different languages, contribute your favourite ones if missing, making it more valuable.

github.com/EbookFoundatio…

2. 📒 App Ideas Collection

A collection of ideas to make a beginner life lot easier, make it more valuable by adding more ideas into it.

github.com/florinpop17/ap…

A collection of ideas to make a beginner life lot easier, make it more valuable by adding more ideas into it.

github.com/florinpop17/ap…

Hey Reader, I hope you are having a good day and will have a great life from now on.

Realized that I messed up the thread for day 4 so gonna tweet that again.

👇

Realized that I messed up the thread for day 4 so gonna tweet that again.

👇

Day 4 of #100DaysOfCode

1) List Comprehension:

is a useful way to create lists when elements obey simple rule.

e.g cubes = [i**3 for i in range(10)]

With conditional

cubes = [i**3 for i in range(10) if i%2==0]

1) List Comprehension:

is a useful way to create lists when elements obey simple rule.

e.g cubes = [i**3 for i in range(10)]

With conditional

cubes = [i**3 for i in range(10) if i%2==0]

2) String Formatting:

Syntax: 'some string {0} {1} {2}'.format(value1,value2,value3)

e.g.

msg = "{0} is my friend,{0} is my best friend. His nickname is {1} {2}".format('Bob','Tony','-hehe')

format can have any data that needs to be printed out.

Syntax: 'some string {0} {1} {2}'.format(value1,value2,value3)

e.g.

msg = "{0} is my friend,{0} is my best friend. His nickname is {1} {2}".format('Bob','Tony','-hehe')

format can have any data that needs to be printed out.

Day 3 of #100DaysOfCode

Python.

Motivation, Don't Repeat Yourself.

1)Defining a function

def function_name(parameters_if_any):

"""indentation identifies the code block of a function"""

return data_if_any

Python.

Motivation, Don't Repeat Yourself.

1)Defining a function

def function_name(parameters_if_any):

"""indentation identifies the code block of a function"""

return data_if_any

Parameters: are variables in function definition.

Arguments: are the values put into parameters when functions are called.

Calling a function: function_name(arguments_if_any)

Arguments: are the values put into parameters when functions are called.

Calling a function: function_name(arguments_if_any)

2)Modules

These are the codes written to perform useful tasks. Some modules are already part of standard library and others need to be

installed.

-To import an existing module e.g. math

import math #imports whole math module

These are the codes written to perform useful tasks. Some modules are already part of standard library and others need to be

installed.

-To import an existing module e.g. math

import math #imports whole math module

Sunday Morning Reading Thread ☕️

- Self-Supervised Learning: The Dark Matter of Intelligence 🧠

- SEER ⚙️

- Multimodal Neurons 👁️📚

- Do Transformer Modifications Transfer? ⚔️

- Ultra Data-Efficient GAN 🤯

Quote from each below: 👇

- Self-Supervised Learning: The Dark Matter of Intelligence 🧠

- SEER ⚙️

- Multimodal Neurons 👁️📚

- Do Transformer Modifications Transfer? ⚔️

- Ultra Data-Efficient GAN 🤯

Quote from each below: 👇

Self-Supervised Learning: The Dark Matter of Intelligence 🧠

"As babies, we learn how the world works largely by observation. We form generalized predictive models about objects in the world by learning concepts such as object permanence and gravity"

ai.facebook.com/blog/self-supe…

"As babies, we learn how the world works largely by observation. We form generalized predictive models about objects in the world by learning concepts such as object permanence and gravity"

ai.facebook.com/blog/self-supe…

Self-Supervised Pretraining of Visual Features in the Wild ⚙️

"a RegNetY with 1.3B parameters trained on 1B random images with 512 GPUs achieves 84.2% top-1 accuracy, surpassing the best self-supervised pretrained model by 1%"

arxiv.org/abs/2103.01988

"a RegNetY with 1.3B parameters trained on 1B random images with 512 GPUs achieves 84.2% top-1 accuracy, surpassing the best self-supervised pretrained model by 1%"

arxiv.org/abs/2103.01988

Bringing back AI Weekly Update! 🎉

Here is a preview/curation for March 8th (#27):

- Multimodal Neurons

- SSL: The Dark Matter of Intelligence

- SEER (x2)

- Generative Adversarial Transformers

- Ultra Data-Efficient GAN Training

Links Below: 👇

Here is a preview/curation for March 8th (#27):

- Multimodal Neurons

- SSL: The Dark Matter of Intelligence

- SEER (x2)

- Generative Adversarial Transformers

- Ultra Data-Efficient GAN Training

Links Below: 👇