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. 

notebooks are available at ageron/handson-ml3 and contain more up-to-date code.

This project aims at teaching you the fundamentals of Machine Learning in python. It contains the example code and solutions to the exercises in the second edition of my O'Reilly book Hands-on Machine Learning with Scikit-Learn, Keras and TensorFlow:

Note: If you are looking for the first edition notebooks, check out ageron/handson-ml. For the third edition, check out ageron/handson-ml3.

Quick Start

Want to play with these notebooks online without having to install anything?

Use any of the following services (I recommended Colab or Kaggle, since they offer free GPUs and TPUs).

WARNING: Please be aware that these services provide temporary environments: anything you do will be deleted after a while, so make sure you download any data you care about.

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Just want to quickly look at some notebooks, without executing any code?

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• github.com's notebook viewer also works but it's not ideal: it's slower, the math equations are not always displayed correctly, and large notebooks often fail to open.

Want to run this project using a Docker image?

Read the Docker instructions.

Want to install this project on your own machine?

Start by installing Anaconda (or Miniconda), git, and if you have a TensorFlow-compatible GPU, install the GPU driver, as well as the appropriate version of CUDA and cuDNN (see TensorFlow's documentation for more details).

Next, clone this project by opening a terminal and typing the following commands (do not type the first $ signs on each line, they just indicate that these are terminal commands):

$ git clone https://github.com/ageron/handson-ml2.git
$ cd handson-ml2

Next, run the following commands:

$ conda env create -f environment.yml
$ conda activate tf2
$ python -m ipykernel install --user --name=python3

Finally, start Jupyter:

$ jupyter notebook

If you need further instructions, read the detailed installation instructions.

FAQ

Which Python version should I use?

I recommend Python 3.8. If you follow the installation instructions above, that's the version you will get. Most code will work with other versions of Python 3, but some libraries do not support Python 3.9 or 3.10 yet, which is why I recommend Python 3.8.

I'm getting an error when I call load_housing_data()

Make sure you call fetch_housing_data() before you call load_housing_data(). If you're getting an HTTP error, make sure you're running the exact same code as in the notebook (copy/paste it if needed). If the problem persists, please check your network configuration.

I'm getting an SSL error on MacOSX

You probably need to install the SSL certificates (see this StackOverflow question). If you downloaded Python from the official website, then run /Applications/Python\ 3.8/Install\ Certificates.command in a terminal (change 3.8 to whatever version you installed). If you installed Python using MacPorts, run sudo port install curl-ca-bundle in a terminal.

I've installed this project locally. How do I update it to the latest version?

See INSTALL.md

How do I update my Python libraries to the latest versions, when using Anaconda?

See INSTALL.md

from https://github.com/ageron/handson-ml2

A resource for learning about Machine learning & Deep Learning.

www.youtube.com/c/AladdinPersson

Machine Learning Collection

In this repository you will find tutorials and projects related to Machine Learning. I try to make the code as clear as possible, and the goal is be to used as a learning resource and a way to lookup problems to solve specific problems. For most I have also done video explanations on YouTube if you want a walkthrough for the code. If you got any questions or suggestions for future videos I prefer if you ask it on YouTube. This repository is contribution friendly, so if you feel you want to add something then I'd happily merge a PR 😃

Table Of Contents

• Machine Learning Algorithms
• PyTorch Tutorials
  • Basics
  • More Advanced
  • Object Detection
  • Generative Adversarial Networks
  • Architectures
  • Lightning
• TensorFlow Tutorials
  • Beginner Tutorials
  • Architectures

Machine Learning

•   Linear Regression - With Gradient Descent ✅
•   Linear Regression - With Normal Equation ✅
•   Logistic Regression
•   Naive Bayes - Gaussian Naive Bayes
•   K-nearest neighbors
•   K-means clustering
•   Support Vector Machine - Using CVXOPT
•   Neural Network
• Decision Tree

PyTorch Tutorials

If you have any specific video suggestion please make a comment on YouTube :)

Basics

•   Tensor Basics
•   Feedforward Neural Network
•   Convolutional Neural Network
•   Recurrent Neural Network
•   Bidirectional Recurrent Neural Network
•   Loading and saving model
•   Custom Dataset (Images)
•   Custom Dataset (Text)
•   Mixed Precision Training
•   Imbalanced dataset
•   Transfer Learning and finetuning
•   Data augmentation using Torchvision
•   Data augmentation using Albumentations
•   TensorBoard Example
•   Calculate Mean and STD of Images
•   Simple Progress bar
•   Deterministic Behavior
•   Learning Rate Scheduler
•   Initialization of weights

More Advanced

•   Text Generating LSTM
•   Semantic Segmentation w. U-NET
•   Image Captioning
•   Neural Style Transfer
•   Torchtext [1] Torchtext [2] Torchtext [3]
•   Seq2Seq - Sequence to Sequence (LSTM)
•   Seq2Seq + Attention - Sequence to Sequence with Attention (LSTM)
•   Seq2Seq Transformers - Sequence to Sequence with Transformers
•   Transformers from scratch - Attention Is All You Need

Object Detection

Object Detection Playlist

•   Intersection over Union
•   Non-Max Suppression
•   Mean Average Precision
•   YOLOv1 from scratch
•   YOLOv3 from scratch

Generative Adversarial Networks

GAN Playlist

•   Simple FC GAN
•   DCGAN
•   WGAN
•   WGAN-GP
•   Pix2Pix
•   CycleGAN
•   ProGAN
• SRGAN
• ESRGAN
• StyleGAN - NOTE: NOT DONE

Architectures

•   LeNet5 - CNN architecture
•   VGG - CNN architecture
•   Inception v1 - CNN architecture
•   ResNet - CNN architecture
•   EfficientNet - CNN architecture

PyTorch Lightning

•   Tutorial 1 - Introduction and starter code
•   Tutorial 2 - LightningModule
•   Tutorial 3 - Trainer
•   Tutorial 4 - Metrics
•   Tutorial 5 - DataModule
•   Tutorial 6 - Code restructure
•   Tutorial 7 - Callbacks
•   Tutorial 8 - TensorBoard logging
•   Tutorial 9 - Profiler
•   Tutorial 10 - Multi-GPU

TensorFlow Tutorials

If you have any specific video suggestion please make a comment on YouTube :)

Beginner Tutorials

•   Tutorial 1 - Installation, Video Only
•   Tutorial 2 - Tensor Basics
•   Tutorial 3 - Neural Network
•   Tutorial 4 - Convolutional Neural Network
•   Tutorial 5 - Regularization
•   Tutorial 6 - RNN, GRU, LSTM
•   Tutorial 7 - Functional API
•   Tutorial 8 - Keras Subclassing
•   Tutorial 9 - Custom Layers
•   Tutorial 10 - Saving and Loading Models
•   Tutorial 11 - Transfer Learning
•   Tutorial 12 - TensorFlow Datasets
•   Tutorial 13 - Data Augmentation
•   Tutorial 14 - Callbacks
•   Tutorial 15 - Custom model.fit
•   Tutorial 16 - Custom Loops
•   Tutorial 17 - TensorBoard
•   Tutorial 18 - Custom Dataset Images
•   Tutorial 19 - Custom Dataset Text
•   Tutorial 20 - Classifying Skin Cancer - Beginner Project Example

CNN Architectures

• LeNet
• AlexNet
• VGG
• GoogLeNet
• ResNet

from https://github.com/aladdinpersson/Machine-Learning-Collection