Preface
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This is a list of resources curated from the Slack channel for Udacity's first phase of AI Track scholarship challenge
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As the Slack channel will have multiple conversations going on. I thought best to consolidate all the resources into one repository for anyone to access any time without having to go through all the conversations.
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All are welcome to open a Pull Request or raise an issue, with the content you would like to share
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Text tutorials, video resources, youtube playlists, self-paced learning courses, ebooks... I hope you get the point.
All and any form of content can be shared here.
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Contributors
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Harvard CS231n: Convolutional Neural Networks for Visual Recognition This course is a deep dive into details of the deep learning architectures. Students will learn to implement, train and debug their own neural networks. The final assignment will involve training a multi-million parameter convolutional neural network and applying it on the largest image classification dataset (ImageNet). Much of the background and materials of this course will be drawn from the ImageNet Challenge.
- Video Lectures by Andrej Karpathy, instructor CS231n.
- Notes to accompany the Stanford CS class CS231n: Convolutional Neural Networks for Visual Recognition
- Python Numpy Tutorial
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Practical Deep Learning for Coders Step by step introduction to Deep learning stuff!
- Pre-Requisite: You’ve been coding for at least a year, and also that (if you haven’t used Python before) you’ll be putting in the extra time to learn whatever Python you need as you go. (For learning Python, we have a list of python learning resources available.)
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Elements of AI The Elements of AI is a series of free online courses created by Reaktor and the University of Helsinki. The courses combine theory with practical exercises and can be completed at your own pace.
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Deep Lizard Tutorials on multiple topics like,
- Machine Learning and Deep Learning Fundamentals
- Neural Network Programming - Deep Learning with PyTorch
- Data Science - Learn to code for beginners
- and many more
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Youtube: Machine Learning Recipes with Josh Gordon (offered on Google Developers Youtube Channel)
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Youtube: Linear Algebra for Beginners | Linear algebra for machine learning
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Coursera: ML with Python This course dives into the basics of machine learning
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Coursera: Introduction to Deep Learning & Neural Networks with Keras This course will introduce you to the field of deep learning
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Coursera: Machine Learning by Andrew NG. This is a course for you if are new to this world!
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Coursera: Deep Learning by Andrew NG. A 5-course specialization focusing on various concepts of deep learning with in-depth and simple explanation.
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Coursera: Neural Networks and Deep Learning by deeplearning.ai
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MIT 6.S191 Introduction to Deep Learning MIT's introductory course on deep learning methods with applications to computer vision, natural language processing, biology, and more
- Full Stack Python Build, Deploy and Operate Python Applications
- Scipy Lecture Notes One document to learn numerics, science, and data with Python
- NumPy User Guide This guide is intended as an introductory overview of NumPy and explains how to install and make use of the most important features of NumPy.
- Udacity: Introduction to Python Programming
- Youtube: 3Blue1Brown by Grant Sanderson, is combination of math and entertainment. The goal is for explanations to be driven by animations and for difficult problems to be made simple with changes in perspective
- Khan Academy great resource to learn all the math from the ground up
- MIT 18.06: Linear Algebra by Prof. Gilbert Strang
- This course covers matrix theory and linear algebra, emphasizing topics useful in other disciplines such as physics, economics and social sciences, natural sciences, and engineering.
- MIT 18.02: Multivariable Calculus by Prof. Denis Auroux
- This course covers differential, integral and vector calculus for functions of more than one variable.
- Computing for Data Analysis A hands-on introduction to basic programming principles and practice relevant to modern data analysis, data mining, and machine learning.
- The Deep Learning textbook by Ian Goodfellow and Yoshua Bengio and Aaron Courville
- Machine Learning Mastery! by Jason Brownlee
- Python Data Science Handbook by Jake VanderPlas
- Quantitative Economics with Python by by Thomas J. Sargent and John Stachurski. This website presents a set of lectures on quantitative economic modeling in Python
- The Hundred-page Machine Learning Book by Andriy Burkov
- Deep Learning with Pytorch by Eli Stevens and Luca Antiga offered by PyTorch
- Neural Networks and Deep Learning by Michael Nielsen
- Mathematics for Machine Learning by by Marc Peter Deisenroth, A Aldo Faisal, and Cheng Soon Ong
- Python Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow, 2nd Edition by Sebastian Raschka and Vahid Mirjalili
- Make Your Own Neural Network by Tariq Rashid
- Papers With Code highlights trending ML research and the code to implement it.
- Google Colab Colaboratory is a free Jupyter notebook environment provided by Google. If your local workstation cannot take the workload, Google Colab is the platform to use.
- Andrej Karpathy , director of artificial intelligence and Autopilot Vision at Tesla.
- Christopher Olah
- Clearing the Confusion: AI vs Machine Learning vs Deep Learning Differences
- Keras or PyTorch as your first deep learning framework
- Keras vs TensorFlow vs PyTorch : Comparison of the Deep Learning Frameworks
- Keras vs Pytorch for Deep Learning
- Machine Learning is Fun! by Adam Geitgey, The world’s easiest introduction to Machine Learning
- Perceptrons, Logical Functions, and the XOR problem by Francesco Cicala
- Perceptron Learning Algorithm A Graphical Explanation Of Why It Works by Akshay L Chandra
- Google Colab Free GPU Tutorial by fuat
- The best Data Science courses on the internet, ranked by your reviews by David Venturi
- 5 things you should do to get selected for the 2nd phase of your Google-Udacity Scholarship by George Szabo
- How to create custom Datasets and DataLoaders with Pytorch by Prince Canuma
- Understanding and implementing Neural Network with SoftMax in Python from scratch
- Understand and Implement the Backpropagation Algorithm From Scratch In Python
- Implement Neural Network using PyTorch
- Neural Representation of AND, OR, NOT, XOR and XNOR Logic Gates (Perceptron Algorithm) by Stanley Obumneme Dukor
- Awesome-pytorch-list
- Deep Learning Papers Reading Roadmap Deep Learning papers reading roadmap for anyone who are eager to learn this amazing tech!
- Awesome - Most Cited Deep Learning Papers
- Papers We Love