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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.

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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-

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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

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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

Sachin Kumar

@Sachintukumar

Feb 7, 2024

✅Day1⃣ of #DeepLearning

▫️Topic - Machine Learning (ML) vs Deep Learning (DL)

📝Deep learning is a sub-category of #Machinelearning focused on structuring a learning process for computers where they can recognize patterns & make decisions, much like humans do

A Complete 🧵

DL is essentially a type of sophisticated, multi-layered filter

input raw, unorganized data at top, & it traverses through various layers of neural network, getting refined & analyzed at each level. Eventually, what emerges at bottom is a coherent, structured piece of info a

Input layer -This input can be pixels of an image or a range of time series data

Hidden layer - Commonly known as weights, which are learned while neural network is trained

Output layer - The final layer gives you a prediction of input you fed into your network

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