Hi! I’m a Data Scientist with an interest in applying models to Astrophysics problems. My main projects can be found here, along with the journey to create them, documented in my Blog.

This repository is a collection of famous books that a person who has keen to learn programming, should go through in his career. - Books/Deep Learning with Python.pdf at master · codams-org/Books

Jupyter notebooks for the code samples of the book "Deep Learning with Python" - fchollet/deep-learning-with-python-notebooks

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Source code for all exercises for the book - Deep Learning with Python - jojo62000/DeepLearningWithPython_SecondEdition

This book offers a practical, hands-on exploration of deep learning.

Following is what you need for this book: This book is for data science practitioners, machine learning engineers, and those interested in deep learning who have a basic foundation in machine learning and some Python programming experience.

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Building Machine Learning Systems with Python, 2nd Edition.pdf · Collective Intelligence (1).pdf · Collective Intelligence (1).pdf · Collective Intelligence.pdf · Collective Intelligence.pdf · Computer Vision with OpenCV 3 and Qt5.pdf · Computer Vision with OpenCV 3 and Qt5.pdf · Deep Learning for Computer Vision with Python 2 Practitioner Bundle.pdf ·

(2023/10/13) Understanding Deep Learning - Simon J. D. Prince · (2023/08/15) Probabilistic Machine Learning: Advanced Topics - Kevin P. Murphy · (2023/06/22) Probabilistic Machine Learning: An Introduction - Kevin P. Murphy · (2022/01/11) Mathematics for Machine Learning - Marc Peter Deisenroth & A. Aldo Faisal & Cheng Soon Ong · (2021/08/04) An Introduction to Statistical Learning: with Applications in R - 2nd Edition - Gareth James & Daniela Witten & Trevor Hastie & Robert Tibshirani

"Implementations of deep learning concepts from Deep Learning with Python (Second Edition) by François Chollet. Includes hands-on examples, code exercises, and practical applications using TensorFlow and Keras."

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This repository accompanies Deep Learning with Python, 2nd Edition by Nikhil Ketkar and Jojo John Moolayil (Apress, 2021).

This course will teach you the foundations of machine learning and deep learning with PyTorch (a machine learning framework written in Python). The course is video based. However, the videos are based on the contents of this online book.

📙 "Think Stats: Exploratory Data Analysis in Python" by Allen B. Downey ... 📙 "Mathematics for Machine Learning" by Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong · 📙 "The Matrix Calculus You Need For Deep Learning" by Terence Parr, and Jeremy Howard

Splitting, merging, and extracting text from PDFs and Word documents · Interacting with applications through custom mouse and keyboard macros · Managing your inbox, unsubscribing from lists, and sending email or text notifications ... A companion to Automate the Boring Stuff with Python, Third Edition.

Deep Learning with Python is written for anyone who wishes to explore deep learning from scratch. This new edition adds comprehensive coverage of generative AI and modern deep learning frameworks. It is available for free online.

FOR ==> BE - 2nd Semester - Object Oriented Programming Lab - Q Batch · Week 1 - Exercises dated 21.02.2024 **New** Lab Manual - Basic Linux Operating System Commands dated 21.02.2024 **New** Lab Manual - VIM Editor Reference dated 21.02.2024 **New** FOR ==> BE - 4th Semester - Computer ...

In 2015 I designed and was the ... first deep learning class Stanford - CS 231n: Convolutional Neural Networks for Visual Recognition ❤️. The class became one of the largest at Stanford and has grown from 150 enrolled in 2015 to 330 students in 2016, and 750 students in 2017. ... I have three blogs 🤦‍♂️. This GitHub blog is my ...