@khanacademy

@khanacademy

@freeCodeCamp

Gina Acosta

@ginacostag_

👨‍💻 Data Analysis Roadmap with FREE resources 💸

#BigData #Analytics #DataScience #AI #MachineLearning #IoT #PyTorch #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #React #Serverless #DataScientist #Programming #100DaysofCode #DevOps #SQL #CyberSecurity #Flutter #PHP

1. Math and Statistics
Intro to Statistics - Udacity
🌐 bit.ly/3GMZe5n

Statistics - Udacity
🌐 bit.ly/3VKKzfB

Statistical Learning - Stanford University
🌐 bit.ly/3VqA5Sj

2. Excel
Introduction to MS Excel - Simplilearn
🌐 bit.ly/3u1h1OJ

Microsoft Excel: Online Tutorial for Beginners - Chandoo
🌐 bit.ly/3XzUHJO

Microsoft Excel Tutorial for Beginners - Free Code Camp
🌐 bit.ly/3Vax74G

Read 7 tweets

Gina Acosta

@ginacostag_

🎮 6 Games to Learn Programming

#BigData #Analytics #DataScience #AI #MachineLearning #IoT #PyTorch #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #React #Serverless #DataScientist #Linux #Programming #Coding #100DaysofCode #DevOps #GitHub #SQL #CyberSecurity #Developer

🕹 1. CodinGame
lnkd.in/eXaTcZvv

🕹 2. CodeCombat
codecombat.com

Read 8 tweets

Albert Vilella

@AlbertVilella

On #biocompute news, $TSLA Tesla AI day gave some technical details on how they do their training and video labelling. It seems $NVDA Nvidia GPUs are the norm, with a 14,000 HPC heavily optimised on the software side. #pytorch #AVX2 #CUDA #SMT #smol #CUDNN

None of this is for the purpose of #Bioinformatics applications, rather here it's for Full Self-Driving software, but the technical details show some choices for high-throughput #AI training that one could compare to the #ComputationalBiology #ComputeAcceleration world.

More at bit.ly/biocompute

Read 12 tweets

Hunain Shiwani

@HunainShiwani

Interested in learning more about #DataScience, #MachineLearning or #AI? I’ve got a few places and resources for medics to start with. Anyone can do it with enough time and effort! Soon enough you’ll be making your own neural networks

1/16. A thread 🧵.

2/16. Everyone has their preferences with programming languages. However if you’re starting from scratch, I highly recommend #Python. It is easy to learn, has a wide variety of applications and you will find it is much easier to perform even the most basic of statistics.

3/16. It also gives you access to multiple libraries that are used heavily by the machine learning community such as #Keras, #TensorFlow and #PyTorch.

Read 19 tweets

Akshay 🚀

@akshay_pachaar

🔴 PyTorch 101
🟡 Auto differentiation in #PyTorch
#DataScience #MachineLearning #100daysOfCode

💫 Building a simple neural network ⬇️

💫 What is computational graph ❓⬇️

Read 7 tweets

Marlene Mhangami

@marlene_zw

A few weeks ago I shared how you can animate a picture of yourself in #Python. Well!!! you can also animate a video of yourself too🐍✨

#animeganv2forvideos is an open ML project that uses #PyTorch to let you do this. Here's what it looks like in my terminal. Full code below👇🏿💕

This code is based on code in the project's public collab notebook✨ I edited it to run on a cpu and to be able to use it in my terminal. Pictures are in order👩🏿‍💻

You can also have a look at the original code here github.com/nateraw/animeg…

@huggingface

As a final note, I found this awesome project through @huggingface's spaces🤗💕

If you don't want to write the code and just want to convert your video automatically or turn your webcam stream directly to animated video use the app here
huggingface.co/spaces/nateraw…

Read 4 tweets

Rohan Paul

@rohanpaul_ai

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

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

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

Read 16 tweets

Rohan Paul

@paulr_rohan

Read 17 tweets

Rohan Paul

@paulr_rohan

Some tips to increase the Performance of Your Object Detection Models - A trhead
1/n
#computervision #pytorch #deeplearning #deeplearningai #100daysofmlcode #neuralnetworks #machinelearning #datascience #pythonprogramming #python #AI #ArtificialIntelligence #pythonprogramming

2/n
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

Read 13 tweets

PyImageSearch

@PyImageSearch

🤖PyTorch is one of the most popular Deep Learning frameworks in use today. We have received numerous requests to cover topics related to Computer Vision in #PyTorch.

We hear you, here are our top free resources that will get you started. 🚀

What is PyTorch?

🔗pyimagesearch.com/2021/07/05/wha…

✨When to choose PyTorch
✨Understand the differences with other DL libraries
✨GPU support
✨Is PyTorch more suitable for research?

Your First Neural Network with PyTorch

🔗pyimagesearch.com/2021/07/12/int…

✨Create NN architecture from scratch using PyTorch
✨Initialize optimizers and loss function
✨Looping over training epochs
✨Backpropagate the network

Read 7 tweets

Atharva Ingle

@AtharvaIngle7

Memory Efficient Coding in #PyTorch ⚡
20 tricks to optimize your PyTorch code

Let's break some of the bad habits while writing PyTorch code 👇

A thread 🧵

1. PyTorch dataloader supports asynchronous data loading in a separate worker subprocess. Set pin_memory=True to instruct the DataLoader to use pinned memory which enables faster and asynchronous memory copy from host to GPU

2. Disable gradient calculation for validation or inference. Gradients aren't needed for inference or validation, so perform them within torch.no_grad() context manager.

Read 25 tweets

Naina Chaturvedi

@NainaChaturved8

Pandas is a fast, powerful, flexible and open source data analysis and manipulation tool.

A Mega thread 🧵covering 10 amazing Pandas hacks and how to efficiently use it(with Code Implementation)👇🏻
#Python #DataScientist #Programming #MachineLearning #100DaysofCode #DataScience

1/ Indexing data frames
Indexing means to selecting all/particular rows and columns of data from a DataFrame. In pandas it can be done using two constructs —
.loc() : location based
It has methods like scalar label, list of labels, slice object etc
.iloc() : Interger based

2/ Slicing data frames
In order to slice by labels you can use loc() attribute of the DataFrame.

Implementation —

Read 17 tweets

Grant Hadlich

@GrantHadlich

How negative was my Twitter feed in the last few hours? In the replies are a few models that analyze the sentiment of my home timeline feed on Twitter for the last 24 hours using the Twitter API.
GitHub: github.com/ghadlich/Daily…
#NLP #Python

I analyzed the sentiment on the last 253 tweets from my home feed using a pretrained #BERT model from #huggingface. A majority (70.0%) were classified as negative.
#Python #NLP #Classification #Sentiment #GrantBot

I analyzed the sentiment on the last 253 tweets from my home feed using a pretrained #VADER model from #NLTK. A majority (56.1%) were classified as negative.
#Python #NLP #Classification #Sentiment #GrantBot

Read 7 tweets

Grant Hadlich

@GrantHadlich

I analyzed the sentiment on the last 272 tweets from my home feed using a pretrained #BERT model from #huggingface. A majority (69.9%) were classified as negative.
#Python #NLP #Classification #Sentiment #GrantBot

I analyzed the sentiment on the last 272 tweets from my home feed using a pretrained #VADER model from #NLTK. A majority (57.0%) were classified as negative.
#Python #NLP #Classification #Sentiment #GrantBot

Read 7 tweets

Grant Hadlich

@GrantHadlich

I analyzed the sentiment on the last 378 tweets from my home feed using a pretrained #BERT model from #huggingface. A majority (68.0%) were classified as negative.
#Python #NLP #Classification #Sentiment #GrantBot

I analyzed the sentiment on the last 378 tweets from my home feed using a pretrained #VADER model from #NLTK. A majority (60.3%) were classified as negative.
#Python #NLP #Classification #Sentiment #GrantBot

Read 7 tweets

Grant Hadlich

@GrantHadlich

I analyzed the sentiment on the last 476 tweets from my home feed using a pretrained #BERT model from #huggingface. A majority (70.0%) were classified as negative.
#Python #NLP #Classification #Sentiment #GrantBot

I analyzed the sentiment on the last 476 tweets from my home feed using a pretrained #VADER model from #NLTK. A majority (60.5%) were classified as negative.
#Python #NLP #Classification #Sentiment #GrantBot

Read 7 tweets

Grant Hadlich

@GrantHadlich

I analyzed the sentiment on the last 528 tweets from my home feed using a pretrained #BERT model from #huggingface. A majority (68.0%) were classified as negative.
#Python #NLP #Classification #Sentiment #GrantBot

I analyzed the sentiment on the last 528 tweets from my home feed using a pretrained #VADER model from #NLTK. A majority (58.9%) were classified as negative.
#Python #NLP #Classification #Sentiment #GrantBot

Read 7 tweets

Grant Hadlich

@GrantHadlich

I analyzed the sentiment on the last 569 tweets from my home feed using a pretrained #BERT model from #huggingface. A majority (65.4%) were classified as negative.
#Python #NLP #Classification #Sentiment #GrantBot

I analyzed the sentiment on the last 569 tweets from my home feed using a pretrained #VADER model from #NLTK. A majority (53.6%) were classified as negative.
#Python #NLP #Classification #Sentiment #GrantBot

Read 7 tweets

TheSequence

@TheSequenceAI

Hugging Face Transformers

Building transformer architecture remains an incredible challenge.

Hugging Face Transformers is one of the most popular and advanced frameworks for Transformer architectures.
⬇️1/3

Hugging Face Transformers provides

- implementations of 100s of pre-trained transformer models
- APIs to quickly download and use
- implementations for both #PyTorch and #TensorFlow
2/3

@TheSequenceAI

This is a thread from Edge#111 – our series about transformers.

Keep learning every day. Subscribe to our Twitter @TheSequenceAI.
3/3

Read 3 tweets

Grant Hadlich

@GrantHadlich

I analyzed the sentiment on the last 239 tweets from my home feed using a pretrained #BERT model from #huggingface. A majority (61.1%) were classified as negative.
#Python #NLP #Classification #Sentiment #GrantBot

I analyzed the sentiment on the last 239 tweets from my home feed using a pretrained #VADER model from #NLTK. A majority (61.5%) were classified as negative.
#Python #NLP #Classification #Sentiment #GrantBot

Read 7 tweets

Grant Hadlich

@GrantHadlich

I analyzed the sentiment on the last 288 tweets from my home feed using a pretrained #BERT model from #huggingface. A majority (65.6%) were classified as negative.
#Python #NLP #Classification #Sentiment #GrantBot

I analyzed the sentiment on the last 288 tweets from my home feed using a pretrained #VADER model from #NLTK. A majority (62.8%) were classified as negative.
#Python #NLP #Classification #Sentiment #GrantBot

Read 7 tweets

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