Intro to machine learning Intro to deep Learning Neural network architecture Training neural networks Loss and learning rate Section 2: Data topics for deep learning Data sets for deep learning Predicting with neural networks Over and under fitting data Supervised, unsupervised, and semi...
The next step is to scale the data. We notice that the range for grades is 1.0-4.0, whereas the range for test scores is roughly 200-800, which is much larger. This means our data is skewed, and that makes it hard for a neural network to handle. Let's fit our two features into ...
Intro to Machine Learning with TensorFlow Nanodegree Program: https://www.udacity.com/course/intro-to-machine-learning-with-tensorflow-nanodegree--nd230 - jv-k/IntroductionToMachineLearningWithTensorFlow
Machine Learning:Has a formal definition.Tom Mitchelldefines Machine Learning as:“The field of machine learning is concerned with the question of how to construct computer programs that automatically improve with experience.” This definition is expressed as: “A computer program is said to learn fr...
How to use the common tools that Python threading provides This article assumes you’ve got the Python basics down pat and that you’re using at least version 3.6 to run the examples. If you need a refresher, you can start with the Python Learning Paths and get up to speed. If you’...
EE-UY / CS-UY 4563: Introduction to Machine Learning (Undergraduate) EL-GY 6123: Introduction to Machine Learning (Graduate)Anyone is free to use and copy this material (at their own risk!). But, please cite the material if you use the material in your own class....
Classic Machine Learning with Python Learn More Unlocking Knowledge with RAG Models: Build a Retrieval-Augmented Generation System Learn More AI in Computer Vision: From Fundamentals to Advanced Solutions Learn More How to Handle Error Messages Errors are something that you will inevitably run in...
This book provides many of the best explanations of data science concepts I’ve encountered. Introduces the most useful starter machine learning models—does a good job explaining how to choose the best model and what “the best” means. Great overview of all the big data technologies with ...
Section 3: Code project - Implement Q-learning with pure Python to play a game Environment set up and intro to OpenAI Gym Write Q-learning algorithm and train agent to play game Watch trained agent play game Part 2: Deep Reinforcement Learning Section 1: Deep Q-networks (DQNs) ...
The notebooks are a modular introduction to machine learning in python using scikit-learn with examples and tips. The material is in jupyter notebook format and was designed to be compatible with Python >= 2.6 or >= 3.3. To use these notebooks interatively (intended use), you will need ...