Data Science & Machine Learning

11 Jul 2023 16:40

Statistical Mechanics of Neural Networks ( Haiping Huang ). Springer 2021

Data Science & Machine Learning

04 Jul 2023 16:03

Overview of Machine Learning

Data Science & Machine Learning

01 Jul 2023 15:56

Python Notes 👇
/channel/pythondevelopersindia/576

Data Science & Machine Learning

28 Jun 2023 04:18

Data Science With Python Workflow Cheat Sheet

Creator: business Science
Stars ⭐️: 75
Forked By: 38

https://github.com/business-science/cheatsheets/blob/master/Data_Science_With_Python_Workflow.pdf

Data Science & Machine Learning

26 Jun 2023 03:54

Swipe👉 SQL❤️ zero⭕ to Hero😎.pdf

Data Science & Machine Learning

23 Jun 2023 16:17

8 AI Tools Just for Fun:

1. Tattoo Artist
https://tattoosai.com

2. Talk to Books
https://books.google.com/talktobooks/

3. Vintage Headshots
https://myheritage.com/ai-time-machine

4. Hello to Past
https://hellohistory.ai

5. Fake yourself
https://fakeyou.com

6. Unreal Meal
https://unrealmeal.ai

7. Reface AI
https://hey.reface.ai

8. Voice Changer
https://voicemod.net

Data Science & Machine Learning

20 Jun 2023 11:08

Professional Automated Trading
Eugene A. Durenard, 2013

Data Science & Machine Learning

18 Jun 2023 09:52

Type: Fresher Job
Company: BYJU'S
Role: Business Development Associate
Pay: INR 8 LPA

Apply now: https://bit.ly/3PieQC8

Data Science & Machine Learning

18 Jun 2023 08:01

Arduino V Machine Learning
Steven F. Barrett, 2023

Data Science & Machine Learning

15 Jun 2023 15:07

Machine Learning in Production
Suhas Pote, 2023

Data Science & Machine Learning

14 Jun 2023 08:37

ChatGPT for Data Science Interview
KDnuggets, 2023

Data Science & Machine Learning

07 Jun 2023 13:23

Unlocking the Power of Generative AI Models
Henner Gimpel, 2023

Data Science & Machine Learning

03 Jun 2023 22:08

Deep Learning Crash Course for Beginners with Python
AI Publishing, 2021

Data Science & Machine Learning

01 Jun 2023 07:29

Industry Data Science vs Academia Data Science

Comparing Data Science in academia and Data Science in industry is like comparing tennis with table tennis: they sound similar but in the end, they are completely different!

5 big differences between Data Science in academia and in industry 👇:

1️⃣ Model vs Data: Academia focuses on models, industry focuses on data. In academia, it’s all about trying to find the best model architecture to optimise a defined metric. In industry, loading and processing the data accounts for around 80% of the job.

2️⃣ Novelty vs Efficiency: The end goal of academia is often to publish a paper and to do so, you will need to find and implement a novel approach. Industry is all about efficiency: reusing existing models as much as possible and applying them to your use case.

3️⃣ Complex vs Simple: More often than not, academia requires complex solutions. I know that this isn’t always the case but unfortunately, complex papers get a higher chance of being accepted at top conferences. In industry, it’s all about simplicity: trying to find the simplest solution that solves a specific problem.

4️⃣ Theory vs Engineering: To succeed in academia, you need to have strong theoretical and maths skills. To succeed in industry, you need to develop strong engineering skills. It is great to be able to train a model in a notebook but if you cannot deploy your model in production, it will be completely useless.

5️⃣ Knowledge impact vs $ impact: In academia, it’s all about creating new work and expanding human knowledge. In industry, it is all about using data to drive value and increase revenue.

Data Science & Machine Learning

29 May 2023 16:12

Introduction to Artificial Intelligence and Expert Systems
Dan W. Patterson, 1990

Data Science & Machine Learning

08 Jul 2023 10:00

Netflix ML Architecture

Data Science & Machine Learning

02 Jul 2023 08:22

🖥 Free Courses on Large Language Models

▪ChatGPT Prompt Engineering for Developers

▪LangChain for LLM Application Development

▪Building Systems with the ChatGPT API

▪Google Cloud Generative AI Learning Path

▪Introduction to Large Language Models with Google Cloud

▪LLM University

▪Full Stack LLM Bootcamp

Data Science & Machine Learning

30 Jun 2023 08:48

1. Can you explain how the memory cell in an LSTM is implemented computationally?

The memory cell in an LSTM is implemented as a forget gate, an input gate, and an output gate. The forget gate controls how much information from the previous cell state is forgotten. The input gate controls how much new information from the current input is allowed into the cell state. The output gate controls how much information from the cell state is allowed to pass out to the next cell state.


2. What is CTE in SQL?

A CTE (Common Table Expression) is a one-time result set that only exists for the duration of the query. It allows us to refer to data within a single SELECT, INSERT, UPDATE, DELETE, CREATE VIEW, or MERGE statement's execution scope. It is temporary because its result cannot be stored anywhere and will be lost as soon as a query's execution is completed.


3. List the advantages NumPy Arrays have over Python lists?

Python’s lists, even though hugely efficient containers capable of a number of functions, have several limitations when compared to NumPy arrays. It is not possible to perform vectorised operations which includes element-wise addition and multiplication. They also require that Python store the type information of every element since they support objects of different types. This means a type dispatching code must be executed each time an operation on an element is done.

4. What’s the F1 score? How would you use it?

The F1 score is a measure of a model’s performance. It is a weighted average of the precision and recall of a model, with results tending to 1 being the best, and those tending to 0 being the worst.

5. Name an example where ensemble techniques might be useful?

Ensemble techniques use a combination of learning algorithms to optimize better predictive performance. They typically reduce overfitting in models and make the model more robust (unlikely to be influenced by small changes in the training data). You could list some examples of ensemble methods (bagging, boosting, the “bucket of models” method) and demonstrate how they could increase predictive power.

Data Science & Machine Learning

27 Jun 2023 07:30

Planet Spark is hiring Business Development Associate
👉 Salary: 6.5 LPA (Expected)
👉 Experience: Freshers
👉 Location: Gurgaon / Gurugram
👉 Perks: 5 days a week, Informal dress code, Free snacks and beverages, Cab/Transportation facility, Health Insurance
📌 Apply Link: https://bit.ly/3NtOfzt

Data Science & Machine Learning

23 Jun 2023 18:10

800 Data Science Questions with Answers

Data Science & Machine Learning

21 Jun 2023 17:46

Data Engineering Project workflow

Data Science & Machine Learning

18 Jun 2023 15:15

How does a model work on a stakeholder point of view

Data Science & Machine Learning

18 Jun 2023 08:01

Deep Learning on Microcontrollers
Atul Krishna Gupta, 2023

Data Science & Machine Learning

17 Jun 2023 13:40

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Data Science & Machine Learning

15 Jun 2023 05:45

Here are some incredible platforms where you can download datasets for your project:


Our World in Data https://ourworldindata.org/

World Health Organization (https://www.who.int/data/gho

Statcounter (https://gs.statcounter.com/

Food and Agriculture Organization of the UN (FAO) (https://www.fao.org/home/en

World Bank (https://data.worldbank.org/)

Data Science & Machine Learning

07 Jun 2023 13:23

Introduction to Programming Using Python
Daniel Liang, 2012

Data Science & Machine Learning

03 Jun 2023 22:08

Python Machine Learning Workbook for Beginners
AI Publishing, 2020

Data Science & Machine Learning

01 Jun 2023 18:06

"Approaching (Almost) Any Machine Learning Problem" book.

Data Science & Machine Learning

31 May 2023 20:25

A Handbook of Statistical Analyses Using Stata
Sophia Rabe-Hesketh, 2007

Data Science & Machine Learning

29 May 2023 16:12

Open Source Projects - Beyond Code
John Mertic, 2023