Pinned Repositories
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
chatroom
React-based Chatroom Component for Rasa Stack
Python_Course
Python Course
stat453-deep-learning-ss20
STAT 453: Intro to Deep Learning @ UW-Madison (Spring 2020)
chittella369's Repositories
chittella369/ml-workshop-2-of-4
Intermediate Machine Learning with Scikit-learn, 4h interactive workshop
chittella369/ml-workshop-1-of-4
Introduction to Machine learning with Python, 4h interactive workshop
chittella369/MIT-DataScience
Course Material from MIT DataScience
chittella369/stanford-cs-221-artificial-intelligence
VIP cheatsheets for Stanford's CS 221 Artificial Intelligence
chittella369/stanford-cs-229-machine-learning
VIP cheatsheets for Stanford's CS 229 Machine Learning
chittella369/stanford-cs-230-deep-learning
VIP cheatsheets for Stanford's CS 230 Deep Learning
chittella369/BooksForDataScience
chittella369/tensorflow-safari-course
Exercises and solutions to accompany my Safari course introducing TensorFlow.
chittella369/tf2_course
Notebooks for my "Deep Learning with TensorFlow 2 and Keras" course
chittella369/system-design-primer
Learn how to design large-scale systems. Prep for the system design interview. Includes Anki flashcards.
chittella369/R_TBC_Uploads
This rfepository contains the work done under R textbook companion project under FOSSEE, IIT BOMABY
chittella369/Machine-Learning-Books
A curated collection of free Machine Learning related eBooks
chittella369/PRML-Solution-Manual
My Own Solution Manual of PRML
chittella369/Python_Course
Python Course
chittella369/pytext
A natural language modeling framework based on PyTorch
chittella369/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.
chittella369/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.
chittella369/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.
chittella369/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers
aka "Bayesian Methods for Hackers": An introduction to Bayesian methods + probabilistic programming with a computation/understanding-first, mathematics-second point of view. All in pure Python ;)
chittella369/Agile_Data_Code_2
Code for Agile Data Science 2.0, O'Reilly 2017, Second Edition
chittella369/Machine_Learning-_Resources
Machine Learning Resources by Jason Brownlee
chittella369/ISL-linear-regression
chittella369/weblogic
chittella369/CADL
ARCHIVED: Contains historical course materials/Homework materials for the FREE MOOC course on "Creative Applications of Deep Learning w/ Tensorflow" #CADL
chittella369/machine-learning
Content for Udacity's Machine Learning curriculum
chittella369/Machine-Learning-Links-And-Lessons-Learned
List of all the lessons learned, best practices, and links from my time studying machine learning
chittella369/Deep_Learning_AI
chittella369/stock-prediction
chittella369/times-series-analysis
chittella369/notes
A new beginning, following new trends, moving notes for my Students from my website to GitHub. This repository is for all those enthusiasts who wish to learn basic theories of latest technologies and trends. Lets handle and learn version control together!!!