👉Alternative for Keras ::

⚡️TFLearn.
⚡️Knet.
⚡️Clarifai.
⚡️DeepPy.
⚡️Torch.
⚡️NVIDIA Deep Learning GPU Training System (DIGITS)
⚡️RustNN.


👨‍🎤⛄️❄️🐋💁🏻‍♂️🙅🏼‍♀️

Hello Python Developers !!!

Next week, we'll reach 300,000 projects in the pypi package index.

That's up from 200,000 on 14 Oct 2019 and 100,000 on 4 Mar 2017.

What is your prediction for when we'll reach 400,000?

Current project Count : 299,092 projects

If your machine learning model is 99% correct, something is wrong

Possible reasons:

⚡️Wrong evaluation metric
⚡️Bad validation set
⚡️Overfitting
⚡️Leakage

⚡️Accepted data as "objective" or "authoritative"

⚡️you're accidentally using 100% of the training set as your test set

⚡️You just don't understand your data.

⚡️Model is clearly memorizing data. Could be that number of features being used is more than the number of data points?

⚡️Forget that all data are shaped through human intervention at many stages

we need to understand saving trained ML model in different formats

https://developer.ibm.com/callforcode/?utm_content=000039JL&utm_term=10008917&p1=PSocial&p2=297609258&p3=142596057&dclid=CLP4z7HPh_ACFQFUKwodbu0K0w



Your solution is officially in the running for a chance to win $200,000 USD and support in setting up your team as a startup.

Source: Thomas Malone | MIT. Machine learning is changing, or will change, every industry, and leaders need to understand the basic principles, the potential, and the limitations.

I already created video for understanding on Yellobrick. Yellowbrick is Machine Learning Visualisation library and best suit for Diagnosing the ML problems in every stage. Yellowbrick is built top on Scikit-Learn and Matplotlib.

▬▬▬▬▬▬ Installation ▬▬▬▬▬▬
$ pip install yellowbrick
conda install -c districtdatalabs yellowbrick

▬▬▬▬▬▬ Significance of Yellowbrick Python library ▬▬▬▬▬▬
Tuning hyperparameters
Select features
Visualize the score of your models
Visualize Rank Algorithms

Hi All,

We have special interaction on AI, ML, DL and NLP.

Interested people join on zoom meeting at 3PM IST

Let’s talk about something interesting 🧐

Will share zoom link before 20mins

Join Zoom Meeting
https://us02web.zoom.us/j/84127482955?pwd=SFpUVEZ2U2p2dCtsRzRYOFlnWFdQZz09

Meeting ID: 841 2748 2955
Passcode: robo

at 3PM IST - in another 15 mins

Resources for NLP and DL .. this is our third edition

People who wants to join free ML sessions daily at 9:30 PM IST

https://chat.whatsapp.com/ECDLgJ50bZx7cw9IJ4GoOS


Remember who seriously wants to learn join .. this is open source platform

arxiv.org/abs/2104.10350 -- a detailed study of CO2 emission in large models from Google

A visual introduction to machine learning

http://www.r2d3.us/visual-intro-to-machine-learning-part-1/

Best view in desktop 🖥

Join in their team If you are interested for part3 launch 🚀

What salaries does a data science professional earn?
The base annual pay for a data scientist in the US is USD 117,345, with a range of USD 86,000–157,000 (source: Glassdoor).

Here is a look at estimated salaries for data scientists by experience level (source: ZipRecruiter):
👉Entry level: The annual salary is estimated at USD 69,000.
👉Mid-level: The median annual salary is USD 89,000.
👉Experienced: These are professionals with extensive experience in their data science careers. They could earn median annual salaries of USD 1,28,000.
👉Experienced at manager level: The median annual salary could be as high as USD 184,000.

Estimated annual salaries:
👉In India: INR 708,012 (approx USD 9,507, source: PayScale)
👉In the US: USD 96,106 (source: PayScale)

Tesla uses advanced AI for vision and planning, supported by efficient use of inference hardware to make Autopilot to full self-driving. A full build of Tesla's Autopilot neural networks (Deep leaning Model ) involves 48 networks that take 70,000 GPU hours to train.

AI features in Tesla.
1. AI integrated chips
2. Autopilot

⚡️⚡️⚡️Professor 👨‍🏫 at University Of Washington stated that 👇



2000s: Neural networks = Deep learning.
2010s: Machine learning = Deep learning.
2020s: Artificial intelligence = Deep learning.
2030s: Computer science = Deep learning.
2040s: Science = Deep learning.
2050s: The universe = Deep learning.


Your learning never going to be wasted .. 💪💪

IIT Madras Team Develop AI To Restore Old, Damaged Photos To New.

Researchers from IIT Madras have harnessed the power of artificial neural networks to restore CCTV images that have been degraded due to weather conditions, beyond recognition.

Paper published in IEEE.
Degradation Aware Approach to Image Restoration Using Knowledge Distillation
https://ieeexplore.ieee.org/document/9288928/authors#authors

But you know that fundamentally they are not the same. Statistics is about doing "tests" and calculating the probability of errors of the 2 types. Data science is about manipulating the data before they can be submitted to a statistical analysis.