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Sachin Kumar

@Sachintukumar

Dec 6, 2022 • 4 tweets • 3 min read • Read on X

📝Which 𝗘𝘅𝗰𝗲𝗹 features you must know as 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝘁? 🔥

🧵👇

- 𝗣𝗶𝘃𝗼𝘁 𝘁𝗮𝗯𝗹𝗲𝘀 𝗮𝗻𝗱 𝗽𝗶𝘃𝗼𝘁 𝗰𝗵𝗮𝗿𝘁𝘀
- 𝗖𝗼𝗻𝗱𝗶𝘁𝗶𝗼𝗻𝗮𝗹 𝗳𝗼𝗿𝗺𝗮𝘁𝘁𝗶𝗻𝗴
- 𝗖𝗼𝗻𝗰𝗮𝘁𝗲𝗻𝗮𝘁𝗲
- 𝗜𝗳, 𝘀𝘂𝗺𝗶𝗳, 𝗰𝗼𝘂𝗻𝘁𝗶𝗳, 𝗮𝘃𝗲𝗿𝗮𝗴𝗲𝗶𝗳
- 𝗜𝗳 𝗲𝗿𝗿𝗼𝗿𝘀
- 𝗨𝗻𝗶𝗾𝘂𝗲
- 𝗧𝗿𝗶𝗺
- 𝗦𝗼𝗿𝘁
- 𝗖𝗼𝘂𝗻𝘁𝗯𝗹𝗮𝗻𝗸

- 𝗗𝗮𝘆𝘀 𝗮𝗻𝘀 𝗡𝗲𝘁𝘄𝗼𝗿𝗸𝗱𝗮𝘆𝘀
- 𝗥𝗮𝗻𝗸
- 𝗦𝘂𝗺𝗽𝗿𝗼𝗱𝘂𝗰𝘁
- 𝗦𝘂𝗺, 𝗖𝗼𝘂𝗻𝘁, 𝗔𝘃𝗲𝗿𝗮𝗴𝗲
- 𝗠𝗶𝗻, 𝗠𝗮𝘅, 𝗠𝗲𝗱𝗶𝗮𝗻, 𝗦𝘁𝗱𝗲𝘃
- 𝗥𝗶𝗴𝗵𝘁, 𝗟𝗲𝗳𝘁, 𝗠𝗶𝗱

📌 Follow @SachinK02316651 for more Update

#Excel #ExcelTips #exceltricks #DataScience #Data #dataprotection #SQL #Python #PowerBI

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More from @Sachintukumar

Sachin Kumar

@Sachintukumar

Jul 20

Not every deal-maker hits the target — SQL finds who actually did.

Oracle #SQL Interview Question

A Complete thread

Q) As a data analyst on @Oracle Sales Operations team, you are given a list of salespeople’s deals, & annual quota they need to hit.

Write a query that outputs each employee id & whether they hit quota or not ('yes' or 'no'). Order the results by employee id in ascending order

Definitions: - deal_size: Deals acquired by a salesperson in the year.

Each salesperson may have more than 1 deal. - quota: Total annual quota for each salesperson.

Read 8 tweets

Sachin Kumar

@Sachintukumar

May 6

Do You Really Know How #SQL Executes Your Query?

Let’s talk about actual execution order of an SQL query — it’s not order you write it in!

🧵

Even though we write SQL like this:

SELECT
FROM
WHERE
GROUP BY
HAVING
ORDER BY
LIMIT

Internally, SQL follows a different execution flow

🎯 INTERVIEW TIP:
Why can’t you use SELECT aliases in WHERE clause?
Because WHERE runs before SELECT!

Mastering this flow is crucial for writing optimized queries & cracking SQL interviews.

Read 4 tweets

Sachin Kumar

@Sachintukumar

Mar 27, 2024

📝Day 35 of #Deeplearning

✅Topic - Backpropagation in CNN

▫️ #BackPropagation is an algorithm to train neural networks. It is the method of fine-tuning weights of a neural network based on error rate obtained in previous epoch (i.e., iteration)

A Complete 🧵

✅Backpropagation is an algorithm for supervised learning of artificial neural networks using #gradientdescent

Given an artificial neural network and an error function, method calculates gradient of error function with respect to neural network's weights using chain rule

▫️ Total Trainable Parameters-

▫️ Forward Propagation-

Read 13 tweets

Sachin Kumar

@Sachintukumar

Mar 24, 2024

✅Day 32 of #Deeplearning

📝Topic - Padding & Strides

▫️ #Padding is simply a process of adding layers of zeros to our input images

▫️ #Stride describes step size of kernel when you slide a filter over an input image

A Complete 🧵

▫️ Padding is simply a process of adding layers of zeros to our input images.

The purpose of padding is to preserve original size of an image when applying a #convolutional filter & enable filter to perform full convolutions on edge pixel

▫️ So to prevent this-

We will be using padding of size 2 (i.e. original image(5) – feature map(3)).

It is also known as zero padding because we are padding it with 0

Read 13 tweets

Sachin Kumar

@Sachintukumar

Mar 20, 2024

✅Day 29 of #Deeplearning

▫️ Topic - Keras Tuner & Hyperparameter Tuning

📝#KerasTuner is a powerful library that allows you to automate hyperparameter tuning process & search for best model configuration

A Complete 🧵

A ML model has two types of parameters:

Trainable parameters - learned by algorithm during training. For instance weights of a neural network are trainable parameters

#Hyperparameters - set before launching learning process. learning rate in a dense layer are hyperparameter

✅Defining the Model

Define a simple deep learning model that consists of three hidden layers & an output layer with a #softmax activation function

use Adam optimizer and categorical cross-entropy loss function

Read 12 tweets

Sachin Kumar

@Sachintukumar

Feb 17, 2024

✅Day6⃣ of #Deeplearning🧬

▫️ Topic - Multilayer Perceptrons

📝#Multilayerperceptron is type of feedforward neural network consisting of fully connected neurons with nonlinear kind of activation function

It is widely used to distinguish data that is not linearly separable

✅Some of its key concepts

Input layer
Hidden layer
Output layer
Weights
Bias Neurons

✅How Stochastic #GradientDescent (SGD) Work -

Initialization
Iterative Optimization
Direction of Descent
Learning Rate
Convergence

Read 10 tweets

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