Rahul Garg

# Intel Image Classification using CNN and Resnets

The data contains images from different scenes around the world. The code tries to learn different patterns from the images by training on top of training models and then predict image category when a new/test image is passed.

The different categories of data are:  
1. buildings  
2. forest  
3. glacier  
4. mountain  
5. sea  
6. street  

intel-image-classification

05-cifar10-cnn

fashion-feedforward-minimal

04-feedforward-nn

02-insurance-linear-regression

housing-linear-minimal

03-cifar10-feedforward

02-linear-regression

01-pytorch-basics

03-logistic-regression

03-logistic-regression-live

# Exploring Pytorch Operations

Working with different Pytorch Functions and Finding their breaking points.


01-tensor-operations

## IPL Data - Expolatory Data Analysis
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To analyze IPL match stats and visualize the analysis for each understanding. Getting an idea of each team performance and contributing factors of success for each franchise.

Data Source: https://www.kaggle.com/nowke9/ipldata
1. Deliveries.csv: Contains information regarding each ball in every match.
2. Matches.csv: High Level Info For each Match
3. Players.xlsx: Player names with IPL stats.

ipl-stats-analysis

pandas-practice-assignment

# Numpy Array Operations

I have demonstrated numpy 5 common operations:
1. np.transpose
2. np.arange
3. np.matmul
4. np.add
5. np.reshape

numpy-array-operations

numpy-100-exercises

100-numpy-exercises

assignment1