Tutorials Libraries

23 Components & Libraries

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Tutorials Libraries

Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials

A comprehensive list of Deep Learning / Artificial Intelligence and Machine Learning tutorials - rapidly expanding into areas of AI/Deep Learning / Machine Vision / NLP and industry specific areas such as Climate / Energy, Automotives, Retail, Pharma, Medicine, Healthcare, Policy, Ethics and more.

IPython Notebook(s) and other programming tools such as Torch/Lua/D lang in demonstrating deep learning functionality. Additional PyRo tutorials: IPython Notebook(s) demonstrating scikit-learn functi…

Cool Python features for machine learning that I used to be too afraid to use. Will be updated as I have more time / learn more.

A gentle guide to the Python features that I didn't know existed or was too afraid to use. This will be updated as I learn more and become less lazy. GitHub has problem rendering Jupyter notebook so …

Course demos and handouts for Talk Python's Effective PyCharm course

JetBrain's PyCharm Cheat Sheet PyCharm is the premier Python IDE (integrated development environment). You will be hard-pressed to find an editor that gives a more holistic way to build Python applic…

Code, exercises and tutorials of my personal blog ! 📝

The repository is organized the following way : For the moment, these cheat sheets are written manually. I'd like to create a visual content later that would both dive in the maths and illustrate cle…

Article back-end for hack.guides() website

We've all learned by reading great content on the web. Now is your chance to give back. Studies show the best way to learn is by teaching. In fact, writing is a great way to teach and learn at the…

🔥机器学习/深度学习/Python/算法面试/自然语言处理教程/剑指offer/machine learning/deeplearning/Python/Algorithm interview/NLP Tutorial

Hadoop Hive

Machine Learning notebooks for refreshing concepts.

Helpful jupyter noteboks that I compiled while learning Machine Learning and Deep Learning from various sources on the Internet. Linear & Multiple Regression Logistic Regression Regularization

:snake: :information_source: Create and manage data in your Flask app via a SQL database.

Connect your Flask app to a database using Flask-SQLAlchemy. Get set up locally in two steps:

Deep & Classical Reinforcement Learning + Machine Learning Examples in Python

We provide here a suite of Python examples that walk you through concepts in: Classical & Deep Reinforcement Learning Basic & Advanced Machine Learning Anson Wong

MachineLearning-DeepLearning-Code-for-my-YouTube-Channel

The full collection of all codes for my Youtube Channel segregated as per topic.

FastAPI Tutorials & Deployment Methods to Cloud and on-prem infrastructures

Numpy is a general-purpose array-processing package. It provides a high-performance multidimensional array object and tools for working with these arrays. It is the fundamental package for scientific computing with Python. Besides its obvious scientific uses, Numpy can also be used as an efficient multi-dimensional container of generic data.

It is an extension module of Python which is mostly written in "C". It provides various functions which are capable of performing the numeric computations with a high speed. NumPy provides various po…

All the code examples I use in my videos

This repository contains all the code examples that I use in my YouTube videos. You'll find the link to the code in the description of each of the videos on YouTube. If you're a regular watcher, you …

Machine Learning Tutorials in Python

Machine Learning tutorials in Python

A couple of python scripts to interpret geological structures from geophysical images using deep learning

If you use this project in your research or wish to refer to the results of the tutorials, please use the following BibTeX entry.

Learn everything web scraping with David Teather Codes on YouTube

If you don't want to deal with installing and configuring software, I've set up this repository so that a GitHub Codespace can do all of that for you. Select Code -> Codespaces Tab -> The + Ico…

Collection of lectures and lab lectures on machine learning and deep learning. Lab practices in Python and TensorFlow.

Code for Machine Learning with TensorFlow: 2nd Edition Published by Manning Publications

We welcome contributions to the TF2 port and to all of the notebooks in TF1.15.x too! Follow the URL from the startup message. Enjoy! Note that the Docker build described below automatically pulls do…

Tutorials on machine learning, artificial intelligence, data science with math explanation and reusable code (in python and R)

Moreover, you may find here some Colab notebooks without a theoretical tutorial (yet). I decided to upload the code before I finished writing the theoretical part (this would be indicated). I am co…

The overall objective of this toolkit is to provide and offer a free collection of data analysis and machine learning that is specifically suited for doing data science. Its purpose is to get you started in a matter of minutes. You can run this collections either in Jupyter notebook or python alone.

The overall objective of this toolkit is to provide and offer a free collection of data analysis and machine learning that is specifically suited for doing data science. Its purpose is to get you sta…

ROS tutorial by Purdue SMART lab: Gazebo simulation - autonomous mobile robot navigation and creating custom robots and sensor plugins

ROS tutorial by Purdue SMART lab: Husky mobile robot navigation and creating custom robots with sensor plugins in Gazebo simulation. This tutorial serves as an introduction to driving and controlling…

TensorFlow-Multiclass-Image-Classification-using-CNN-s

Balanced Multiclass Image Classification with TensorFlow on Python.

It is a ready-to-run code. No MNIST or CIFAR-10. This is a repository containing datasets of 5200 training images of 4 classes and 1267 testing images.No problematic image. Just extract files from mu…

Here, we will be showcasing our seminar series “CPP for Image Processing and Machine Learning” including presentations and code examples. There are image processing and machine learning libraries out there which use C++ as a base and have become industry standards (ITK for medical imaging, OpenCV for computer vision and machine learning, Eigen for linear algebra, Shogun for machine learning). The documentation provided with these packages, though extensive, assume a certain level of experience with C++. Our tutorials are intended for those people who have basic understanding of medical image processing and machine learning but who are just starting to get their toes wet with C++ (and possibly have prior experience with Python or MATLAB). Here we will be focusing on how someone with a good theoretical background in image processing and machine learning can quickly prototype algorithms using CPP and extend them to create meaningful software packages.

Welcome to CBICA’s C++ learning resource. Here, we will be showcasing our seminar series "CPP for Image Processing and Machine Learning" including presentations and code examples. The documentation p…