Diksha Timilsina

learner

[Diksha Timilsina @DikshaTimilsina]

Day 30
Today I revised core AI and ML concepts to strengthen my understanding.
#AI #MachineLearning
#LearningUtsav2025
#FestivalOfLearning
#LUD30

[Diksha Timilsina @DikshaTimilsina]

Day(29/30)
Today I continued learning probability for machine learning.
#AI #ML
#LearningUtsav2025
#FestivalOfLearning
#LUD29

[Diksha Timilsina @DikshaTimilsina]

Day(28/30)
Today I started learning the basics of Probability ( the foundation of Machine Learning).
#LearningUtsav2025
#FestivalOfLearning
#LUD28

[Diksha Timilsina @DikshaTimilsina]

Day(27/30)
Today I learned about Gradient Descent. It’s an algorithm that helps model reduce errors by adjusting its parameters step by step in the direction that minimizes the loss like walking downhill to reach the lowest point.
#LearningUtsav2025
#FestivalOfLearning
#LUD27

[Diksha Timilsina @DikshaTimilsina]

Day(26/30)
Today I learned about overfitting and underfitting. Overfitting is when a model learns too much from training data and fails on new data, while underfitting is when it learns too little and performs poorly everywhere.
#LearningUtsav2025
#FestivalOfLearning
#LUD26

[Diksha Timilsina @DikshaTimilsina]

Day(25/30)
Today I learned Model Training in TensorFlow .
compile() → define how to learn
fit() → train
evaluate() → test
predict() → use
save() → store
#AI #ML
#LearningUtsav2025
#FestivalOfLearning
#LUD25

[Diksha Timilsina @DikshaTimilsina]

Day(24/30)
Today I learned about models and layers in TensorFlow!
1. Layers are the building blocks of a neural network.
2. Models connect these layers to form the complete network.
#LearningUtsav2025
#FestivalOfLearning
#LUD24

[Diksha Timilsina @DikshaTimilsina]

Day(23/30)
Today I learned about metrics in TensorFlow!
Metrics help check how well your model is doing.
1. accuracy – for classification
2. MSE – for regression .
3. Precision & Recall – for tricky datasets
#LearningUtsav2025
#FestivalOfLearning
#LUD23

[Diksha Timilsina @DikshaTimilsina]

Day(22/30)
Today I learned about different types of TensorFlow Optimizers like SGD, Adam, RMSProp, and Adagrad . each with its own way of updating weights for better model performance!
#LearningUtsav2025
#FestivalOfLearning
#LUD22

[Diksha Timilsina @DikshaTimilsina]

Day(21/30)
Today I continued learning TensorFlow , explored Layers and Models!
#AI #DeepLearning
#LearningUtsav2025
#FestivalOfLearning
#LUD21

[Diksha Timilsina @DikshaTimilsina]

Day (20/30)
Today I learnedTensorFlow basics!
Explored more about tensors, datasets, and how TensorFlow handles data for training deep learning models.
#LearningUtsav2025
#FestivalOfLearning
#LUD20

[Diksha Timilsina @DikshaTimilsina]

Day(19/30)
Today I continued my digit classification project with Keras training the model to recognize handwritten numbers!
#LearningUtsav2025
#FestivalOfLearning
#LUD19

[Diksha Timilsina @DikshaTimilsina]

Day(18/30)
Today I started building a digit classification model using Keras!
Excited to dive deeper into neural networks and see how machines learn to recognize handwritten digits.
#deeplearningai
#learningutsav2025
#festivaloflearning
#LUD18

[Diksha Timilsina @DikshaTimilsina]

Day(17/30)
Today I learned about the types of Neural Networks.
1. ANN – basic prediction tasks
2. CNN – image & video processing
3. RNN – sequence & time series data
#ai #DeepLearning
#learningutsav2025
#festivaloflearning
#LUD17

[Diksha Timilsina @DikshaTimilsina]

Day(16/30)
Today I learned:
1. Activation Functions: Add non-linearity (ReLU, Sigmoid, Tanh, Softmax).
2. Loss Functions: Measure prediction error (MSE, Binary & Categorical Cross-Entropy, Hinge).
#DeepLearning
#AI
#learningutsav2025
#festivaloflearning
#LUD16

[Diksha Timilsina @DikshaTimilsina]

Day(15/30)
Today I learned about Neural Networks and Natural Language Processing. Neural Networks are like a mini brain for computers, and NLP is about teaching computers to understand human language.
#AI #datascience #ML
#learningutsav2025
#festivaloflearning
#LUD15

[Diksha Timilsina @DikshaTimilsina]

Day(14/30)
Today I learned:
Cleaned and visualized the Dragon Real Estate dataset, removed empty rows, and trained a basic machine learning model using Scikit-learn.
#LearningUtsav2025
#festivaloflearning
#LUD14

[Diksha Timilsina @DikshaTimilsina]

Day(13/30)
Today I learned Linear Regression vs KNN in Scikit-learn:
1. LR: Fits a line, predicts numbers.
2. KNN: Looks at neighbors, predicts numbers or classes.

#machinelearning
#learningutsav2025
#festivaloflearning
#LUD13

[Diksha Timilsina @DikshaTimilsina]

@MaunataP16177 Keep going

[Diksha Timilsina @DikshaTimilsina]

Day(12/30)
Today I learned about KNN, Linear Regression, and Logistic Regression and how they are used to predict values and classify data in machine learning.
#learningutsav2025
#festivaloflearning
#LUD12