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2015

Public material for CS109

2015lab1

500-ai-machine-learning-deep-learning-computer-vision-nlp-projects-with-code

500 AI Machine learning Deep learning Computer vision NLP Projects with code

agile_data_code_2

Code for Agile Data Science 2.0, O'Reilly 2017, Second Edition

aima-python

Python implementation of algorithms from Russell And Norvig's "Artificial Intelligence - A Modern Approach"

alexa-cookbook

A series of sample code projects to be used for educational purposes during Alexa hackathons and workshops, and as a reference for tutorials and blog posts.

amazon-sagemaker-examples

Example notebooks that show how to apply machine learning, deep learning and reinforcement learning in Amazon SageMaker

autogluon

AutoGluon: AutoML for Text, Image, and Tabular Data

awesome-deep-learning

A curated list of awesome Deep Learning tutorials, projects and communities.

azure-docs

Open source documentation of Microsoft Azure

bits-deeplearning

booksfordatascience

booksfordatascience-1

cadl

ARCHIVED: Contains historical course materials/Homework materials for the FREE MOOC course on "Creative Applications of Deep Learning w/ Tensorflow" #CADL

caffe

Caffe: a fast open framework for deep learning.

chatroom

React-based Chatroom Component for Rasa Stack

cs231n.github.io

Public facing notes page

data-mining-bits-pilani

Contains Lab Sheets and their Solutions done during the Course

data-science--cheat-sheet

Cheat Sheets

data-science-essentials

Course files for the Microsoft data Science Essentials Course

datasets

A collection of datasets ready to use with TensorFlow

deep-learning-illustrated

Deep Learning Illustrated (2019)

deep_learning_ai

flair

A very simple framework for state-of-the-art Natural Language Processing (NLP)

handson-ml2

A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.

hashing

In this Problem, you have to write an application in Python that keeps track of student records in a university. At a university, there is a need to store all the details of graduating students. For this exercise, let us consider that the CGPA of the student is stored against the student id. The students ID has the following format <YYYYAAADDDD> where YYYY - represents the year in which this student joined the university AAA - a three letter (alphabet) representing degree program DDDD - a four digit number representing the students roll number For instance, an ID can be of the form 2008CSE1223 corresponding to a student who joined the university in the year 2008 in the CSE department with the roll number 1223. The university offers a 4 year graduate degree program in CSE (Computer Science and Engineering), MEC (Mechanical Engineering), ECE (Electronics and Communication Engineering) and ARC (Architecture). In the year 2008 the first batch of 20 students were admitted to the university. Now in the year 2018, 200 students were admitted across all departments. Create an input file input.txt with a random list of students per year and their corresponding CGPA (maximum of 5.0 point CGPA). The university now wants to use the details of all its past students to: a. Identify and commemorate their alumni on the 10th year anniversary of the University. For this they will need to get a list of all students who scored over x CGPA. b. Extend a new course offering to selected students who have secured a CGPA between a specified range. For this they will need to get a list of all students who secured between CGPA x to CGPA y in the past five years. c. Identify the maximum and average CGPA per department. Design a hash table, which uses student Id as the key to hash elements into the hash table. Generate necessary hash table definitions needed and provide a design document (1 page) detailing clearly the design and the details of considerations while making this design and the reasons for the specific choice of hash function.

image_classifier

Identify flower species from an image

isl-linear-regression

jasonbrownleebooks

kenzotakahashi.github.io

Machine Learning in Python