Machine Learning

Machine Learning

Machine learning (ML) is the scientific study of algorithms and statistical models that computer systems use to progressively improve their performance on a specific task. Machine learning algorithms build a mathematical model of sample data...

ML Research Papers From India Accepted At ICML 2021

ICML is a renowned platform for presenting and publishing cutting-edge research on all aspects of machine learning, statistics and data science. https://analyticsindiamag.com/ml-research-papers-from-india-accepted-at-icml-2021/

#icml2021 #machine-learning

ML Research Papers From India Accepted At ICML 2021
Joel Kelly

Joel Kelly

1627224778

Top 3 Free Machine Learning Courses for Beginners

In this video I talk about my favorite free Machine Learning Crash Courses.

Course 1: Introduction to Machine Learning Problem Framing! This course helps you frame machine learning (ML) problems:
https://developers.google.com/machine-learning/problem-framing

Course 2: Google Machine Learning Crash Course: A self-study guide for aspiring machine learning practitioners. It features a series of lessons with video lectures, real-world case studies, and hands-on practice exercises using TensorFlow:
https://developers.google.com/machine-learning/crash-course

Course 3: Kaggle’s Intro to Machine Learning and Intermediate Machine Learning: Learn the core ideas in machine learning, and build your first models. Learn how to handle missing values, non-numeric values, data leakage, and more:
https://www.kaggle.com/learn/intro-to-machine-learning
https://www.kaggle.com/learn/intermediate-machine-learning

#machine-learning #data-science #developer

Top 3 Free Machine Learning Courses for Beginners
Anil  Sakhiya

Anil Sakhiya

1627197534

Python Crash Course For Absolute Beginners in Hindi

Great Learning brings you an 8-hour tutorial on Python Crash Course for Absolute Beginners in Hindi. Python is one of the most famous programming languages in the world and is favored in various fields such as machine learning, data science, etc. because of its simple and flexible syntax, its ability to work with multiple paradigms, and the ease with which it combines with other software components. These factors have made Python be the most favored programming language, which is exactly why so many people want to learn it today.

  • 00:00:00 Introduction
  • 00:02:50 Installing Python
  • 00:11:07 Variables in Python
  • 00:14:27 Datatypes in Python
  • 00:18:44 Operators in Python
  • 00:28:30 Python Identifiers
  • 00:31:05 Python Strings
  • 00:46:03 Datatypes in Python
  • 01:44:24 Functions in Real Life
  • 01:59:22 OOP Structures in Python
  • 02:19:21 Inheritance in Python
  • 02:55:00 Important Programs for Beginners
  • 04:24:42 Building a Mobile App with Python
  • 04:50:46 Python Django Web Development
  • 05:18:11 Data Analysis using FIFA dataset
  • 05:39:05 Web Scraping using Python
  • 05:51:20 Numerical Computation using the NumPy library
  • 05:56:46 Pandas Library in Python
  • 06:27:41 Data Visualization using Matplotlib
  • 07:01:12 Machine Learning with Python

#python #web-development #machine-learning

Python Crash Course For Absolute Beginners in Hindi
Anil  Sakhiya

Anil Sakhiya

1627184046

Everything You Need to Know About OpenCV Python

OpenCV-Python is a Python library specially designed for solving computer vision problems. OpenCV in Python uses NumPy, another Python library, which adds support for large arrays along with a huge collection of high-level mathematical functions to operate on these arrays. Python is a widely learnt programming language, and face detection is another one of its many popular applications which a lot of people want to learn today. Great Learning brings you this tutorial on OpenCV in Python to help you understand everything you need to know about this topic and getting started on the journey to learn about it well.

  • 00:00:00 Introduction
  • 00:01:34 Agenda
  • 00:03:27 Introduction to OpenCV
  • 00:19:26 How to install OpenCV?
  • 00:24:12 Process of Computer Vision
  • 00:42:25 Hands-on: OpenCV operations
  • 00:55:14 What is face detection?
  • 01:01:52 What is face recognition?
  • 01:09:41 Applications of face recognition
  • 01:20:55 Face recognition using Deep Learning
  • 01:31:50 Hands-on: Face detection with OpenCV
  • 01:44:32 Summary

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#opencv #python #data-science #machine-learning

Everything You Need to Know About OpenCV Python
Salman  Ankit

Salman Ankit

1627179141

Deep Learning Tutorial | How to Choose an Activation Function for Deep Learning

How to Choose an Activation Function for Deep Learning
In this video, we cover the different activation functions used in neural networks to provide an output of a given node, or neuron, given its set of inputs: linear, step, sigmoid / logistic, tanh / hyperbolic tangent, ReLU, Leaky ReLU, PReLu, Maxout, and more.

👕 T-shirts for programmers: https://bit.ly/3ir3Gci
🔔 Subscribe: https://www.youtube.com/c/SundogEducation/featured

#deep-learning #machine-learning

Deep Learning Tutorial | How to Choose an Activation Function for Deep Learning

K-Means Clusternig Example with Python and Scikit-learn

Unsupervised-Machine-Learning Flat Clustering

K-Means clusternig example with Python and Scikit-learn

Flat clustering

Clustering algorithms group a set of documents into subsets or clusters . The algorithms’ goal is to create clusters that are coherent internally, but clearly different from each other. In other words, documents within a cluster should be as similar as possible; and documents in one cluster should be as dissimilar as possible from documents in other clusters.

Hierarchical

Hierarchical clustering is where the machine is allowed to decide how many clusters to create based on its own algorithms.

İmages

img

Setup

After downloading the required modules, run the file. You can play on it as much as you want.

Resources

https://pythonprogramming.net/flat-clustering-machine-learning-python-scikit-learn/
https://nlp.stanford.edu/IR-book/html/htmledition/flat-clustering-1.html
https://nlp.stanford.edu/IR-book/pdf/16flat.pdf
https://docs.scipy.org/doc/scipy/reference/generated/scipy.cluster.hierarchy.fcluster.html
https://scikit-learn.org/stable/modules/clustering.html

Who Do These Codes Belong To?

All of these Codes belong to Sentdex. Thanks Senddex.

Download Details:

Author: Peyxw
Download Link: Download The Source Code
Official Website: https://github.com/Peyxw/Unsupervised-Machine-Learning
License: MIT

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#python #scikit-learn #machine-learning

K-Means Clusternig Example with Python and Scikit-learn
Zara  Bryant

Zara Bryant

1627091793

Prebuilt Docker Images for Inference in Azure Machine Learning

Join Seth as he welcomes Shivani Santosh Sambare to talk about Prebuilt Docker Images for Inference in Azure Machine Learning (https://aka.ms/AIShow/PrebuiltDockerImages/AzureML/Doc). Stay tuned as we get back to working on the Roshambo game.

Jump to:

  • 00:00 Livestream begins
  • 02:52 Seth joins the show
  • 05:06 What are we working on today?
  • 09:00 Welcome Shivani
  • 12:50 Prebuilt Docker Images
  • 13:14 What are the challenges working in Azure ML?
  • 14:01 Solutions to Azure ML challenges = Prebuilt Docker Images for Inference
  • 16:44 Demo: Deploying PyTorch model using Azure ML
  • 21:20 Scoring script
  • 29:04 Return to Roshambo
  • 01:22:30 Cassie Breviu previews next week’s livestream: Azure Percept – AI IoT Edge Made Easy

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#azure #docker #machine-learning

Prebuilt Docker Images for Inference in Azure Machine Learning
Phil Tabor

Phil Tabor

1627084698

Should You Go to Grad School for Artificial Intelligence?

Should you go to graduate school for artificial intelligence? As a physics PhD I have some insights for you that you may not have heard elsewhere.

Graduate school is immensely rewarding, yet also incredibly difficult intellectually and emotionally. You’ll have to deal with solving novel and complex problems, as well as learning how to deal with sacrificing your social life.

Learn about how to choose your PhD committee as well as how to get things done in the face of immense pressure.

https://youtu.be/qiH6pJkspMs

#artificial-intelligence #deep-learning #machine-learning

Should You Go to Grad School for Artificial Intelligence?
Phil Tabor

Phil Tabor

1627084486

Dueling Deep Q Learning with Tensorflow 2 & Keras

Dueling Deep Q Learning is easier than ever with Tensorflow 2 and Keras. In this tutorial for deep reinforcement learning beginners we’ll code up the dueling deep q network and agent from scratch, with no prior experience needed. We’ll train an agent to land a spacecraft on the surface of the moon, using the lunar lander environment from the OpenAI Gym.

The dueling network can be applied to both regular and double q learning, as it’s just a new network architecture. It doesn’t require any change to the q learning or double q learning algorithms. We simply have to change up our feed forward to accommodate the new value and advantage streams, and combine them in a way that makes sense.

https://youtu.be/CoePrz751lg

#deep-learning #python #machine-learning #tensorflow #artificial-intelligence

Dueling Deep Q Learning with Tensorflow 2 & Keras
Phil Tabor

Phil Tabor

1627084410

Everything You Need To Master Actor Critic Methods | Tensorflow 2 Tutorial

In this brief tutorial you’re going to learn the fundamentals of deep reinforcement learning, and the basic concepts behind actor critic methods. We’ll cover the Markov decision process, the agent’s policy, reward discounting and why it’s necessary, and the actor critic algorithm. We’ll implement an actor critic algorithm using Tensorflow 2 to handle the cart pole environment from the Open AI Gym.

Actor critic methods form the basis for more advanced algorithms such as deep deterministic policy gradients, soft actor critic, and twin delayed deep deterministic policy gradients, among others.

https://youtu.be/LawaN3BdI00

#deep-learning #python #machine-learning #artificial-intelligence #tensorflow

Everything You Need To Master Actor Critic Methods | Tensorflow 2 Tutorial
Phil Tabor

Phil Tabor

1627084342

Deep Deterministic Policy Gradients (DDPG) | Tensorflow 2 Tutorial

Deep Deterministic Policy Gradients (DDPG) is an actor critic algorithm designed for use in environments with continuous action spaces. This makes it great for fields like robotics, that rely on applying continuous voltages to electric motors. You’ll get a crash course with a quick lecture, followed by a live coding tutorial.

Despite being an actor critic method, DDPG makes use of a number of innovations from deep Q learning. We’re going to make use of a replay memory for training our agent, as well as target actor and target critic networks for learning stability. One key difference is that DDPG uses a soft update rule for the target network parameters, rather than a direct hard copy of the online networks.

In this tutorial we’re going to use Tensorflow 2 to implement a deep deterministic policy gradient agent in the pendulum environment from the Open AI gym.

https://youtu.be/4jh32CvwKYw

#python #deep-learning #artificial-intelligence #tensorflow #machine-learning

Deep Deterministic Policy Gradients (DDPG) | Tensorflow 2 Tutorial
Phil Tabor

Phil Tabor

1627084206

Soft Actor Critic (SAC) in Tensorflow2

The Soft Actor Critic Algorithm is a powerful tool for solving cutting edge deep reinforcement learning problems involving continuous action space environments. It’s a variation of the actor critic method that leverages a maximum entropy framework, double Q networks, and target value networks.

The entropy is modeled by scaling the reward factor, with an inverse relationship between the reward scale and the entropy of our agent. Larger reward scaling means more deterministic behavior, and a larger reward scale means more stochastic behavior.

We’re going to implement this algorithm using the tensorflow 2 framework, and test it out on the Inverted Pendulum environment found in the PyBullet package.

https://youtu.be/YKhkTOU0l20

#deep-learning #machine-learning #artificial-intelligence #python #reinforcement-learning #data-science

Soft Actor Critic (SAC) in Tensorflow2
Liam Hurst

Liam Hurst

1627053216

How to Run Object Detection on a Drone

In this video we are going to learn how to run object detection on a drone. We will first look at object detection and then embed it to the drone. And no we not going to install a 100 packages with 50 parameter configurations. You will have your model running it 10 to 15 mins.

Code and Files:
https://www.computervision.zone/courses/drone-object-detection/

#opencv #machine-learning #data-science

How to Run Object Detection on a Drone
Anthony  Dach

Anthony Dach

1627052100

Scraping Images Using Selenium

In this small and simple use-case, we explore how to use Selenium to scrap images from Google Chrome for any keyword (or set of keywords) searched by a user.

Aim

  • Our program should take any keyword (For Example: “cat”) from the user, along with the number of images needed, and scrap that many images from Google Images on the Chrome browser.
  • The images must be stored in a folder, named after the search term, and should be numbered properly so as to make them easy to access and interpret.

Program Flow

  1. Firstly, our program should accept a search term and number of images that are to be scraped.
  2. The program should check if a folder with the same name as the search term already exists or not. If it doesn’t then it should create one.
  3. Our program should then open the Google Images page for that particular search term.
  4. It should then click on each image and extract its URL which can be used for scraping and saving the image later. It should extract as many URLs as specified.
  5. If the webpage doesn’t have that many images, it should click on the “Load More” button to go to the next page
  6. Once the list of URLs is created, the program should hit every URL, download the image there and save it in the folder created.

#data-science #web-scraping #scraping #selenium #machine-learning

Scraping Images Using Selenium

Advanced Deep Learning with TensorFlow 2 and Keras

Advanced Deep Learning with TensorFlow 2 and Keras (Updated for 2nd Edition)

This is the code repository for Advanced Deep Learning with TensoFlow 2 and Keras, published by Packt. It contains all the supporting project files necessary to work through the book from start to finish.

Please note that the code examples have been updated to support TensorFlow 2.0 Keras API only.

About the Book

Advanced Deep Learning with TensorFlow 2 and Keras, Second Edition is a completely updated edition of the bestselling guide to the advanced deep learning techniques available today. Revised for TensorFlow 2.x, this edition introduces you to the practical side of deep learning with new chapters on unsupervised learning using mutual information, object detection (SSD), and semantic segmentation (FCN and PSPNet), further allowing you to create your own cutting-edge AI projects.

Using Keras as an open-source deep learning library, the book features hands-on projects that show you how to create more effective AI with the most up-to-date techniques.

Starting with an overview of multi-layer perceptrons (MLPs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs), the book then introduces more cutting-edge techniques as you explore deep neural network architectures, including ResNet and DenseNet, and how to create autoencoders. You will then learn about GANs, and how they can unlock new levels of AI performance.

Next, you’ll discover how a variational autoencoder (VAE) is implemented, and how GANs and VAEs have the generative power to synthesize data that can be extremely convincing to humans. You’ll also learn to implement DRL such as Deep Q-Learning and Policy Gradient Methods, which are critical to many modern results in AI.

Related Products

Installation

It is recommended to run within conda enviroment. Pls download Anacoda from: Anaconda. To install anaconda:

sh <name-of-downloaded-Anaconda3-installer>

A machine with at least 1 NVIDIA GPU (1060 or better) is required. The code examples have been tested on 1060, 1080Ti, RTX 2080Ti, V100, RTX Quadro 8000 on Ubuntu 18.04 LTS. Below is a rough guide to install NVIDIA driver and CuDNN to enable GPU support.

sudo add-apt-repository ppa:graphics-drivers/ppa

sudo apt update

sudo ubuntu-drivers autoinstall

sudo reboot

nvidia-smi

At the time of writing, nvidia-smishows the NVIDIA driver version is 440.64 and CUDA version is 10.2.

We are almost there. The last set of packages must be installed as follows. Some steps might require sudo access.

conda create --name packt

conda activate packt

cd <github-dir>

git clone https://github.com/PacktPublishing/Advanced-Deep-Learning-with-Keras

cd Advanced-Deep-Learning-with-Keras

pip install -r requirements.txt

sudo apt-get install python-pydot

sudo apt-get install ffmpeg

Test if a simple model can be trained without errors:

cd chapter1-keras-quick-tour

python3 mlp-mnist-1.3.2.py

The final output shows the accuracy of the trained model on MNIST test dataset is about 98.2%.

Alternative TensorFlow Installation

If you are having problems with CUDA libraries (ie tf could not load or find libcudart.so.10.X), TensorFlow and CUDA libraries can be installed together using conda:

pip uninstall tensorflow-gpu
conda install -c anaconda tensorflow-gpu

Advanced Deep Learning with TensorFlow 2 and Keras code examples used in the book.

Chapter 1 - Introduction

  1. MLP on MNIST
  2. CNN on MNIST
  3. RNN on MNIST

Chapter 2 - Deep Networks

  1. Functional API on MNIST
  2. Y-Network on MNIST
  3. ResNet v1 and v2 on CIFAR10
  4. DenseNet on CIFAR10

Chapter 3 - AutoEncoders

  1. Denoising AutoEncoders

Sample outputs for random digits:

Random Digits

  1. Colorization AutoEncoder

Sample outputs for random cifar10 images:

Colorized Images

Chapter 4 - Generative Adversarial Network (GAN)

  1. Deep Convolutional GAN (DCGAN)

Radford, Alec, Luke Metz, and Soumith Chintala. “Unsupervised representation learning with deep convolutional generative adversarial networks.” arXiv preprint arXiv:1511.06434 (2015).

Sample outputs for random digits:

Random Digits

  1. Conditional (GAN)

Mirza, Mehdi, and Simon Osindero. “Conditional generative adversarial nets.” arXiv preprint arXiv:1411.1784 (2014).

Sample outputs for digits 0 to 9:

Zero to Nine

Chapter 5 - Improved GAN

  1. Wasserstein GAN (WGAN)

Arjovsky, Martin, Soumith Chintala, and Léon Bottou. “Wasserstein GAN.” arXiv preprint arXiv:1701.07875 (2017).

Sample outputs for random digits:

Random Digits

  1. Least Squares GAN (LSGAN)

Mao, Xudong, et al. “Least squares generative adversarial networks.” 2017 IEEE International Conference on Computer Vision (ICCV). IEEE, 2017.

Sample outputs for random digits:

Random Digits

  1. Auxiliary Classfier GAN (ACGAN)

Odena, Augustus, Christopher Olah, and Jonathon Shlens. “Conditional image synthesis with auxiliary classifier GANs. Proceedings of the 34th International Conference on Machine Learning, Sydney, Australia, PMLR 70, 2017.”

Sample outputs for digits 0 to 9:

Zero to Nine

Chapter 6 - GAN with Disentangled Latent Representations

  1. Information Maximizing GAN (InfoGAN)

Chen, Xi, et al. “Infogan: Interpretable representation learning by information maximizing generative adversarial nets.” Advances in Neural Information Processing Systems. 2016.

Sample outputs for digits 0 to 9:

Zero to Nine

  1. Stacked GAN

Huang, Xun, et al. “Stacked generative adversarial networks.” IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Vol. 2. 2017

Sample outputs for digits 0 to 9:

Zero to Nine

Chapter 7 - Cross-Domain GAN

  1. CycleGAN

Zhu, Jun-Yan, et al. “Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks.” 2017 IEEE International Conference on Computer Vision (ICCV). IEEE, 2017.

Sample outputs for random cifar10 images:

Colorized Images

Sample outputs for MNIST to SVHN:

MNIST2SVHN

Chapter 8 - Variational Autoencoders (VAE)

  1. VAE MLP MNIST
  2. VAE CNN MNIST
  3. Conditional VAE and Beta VAE

Kingma, Diederik P., and Max Welling. “Auto-encoding Variational Bayes.” arXiv preprint arXiv:1312.6114 (2013).

Sohn, Kihyuk, Honglak Lee, and Xinchen Yan. “Learning structured output representation using deep conditional generative models.” Advances in Neural Information Processing Systems. 2015.

I. Higgins, L. Matthey, A. Pal, C. Burgess, X. Glorot, M. Botvinick, S. Mohamed, and A. Lerchner. β-VAE: Learning basic visual concepts with a constrained variational framework. ICLR, 2017.

Generated MNIST by navigating the latent space:

MNIST

Chapter 9 - Deep Reinforcement Learning

  1. Q-Learning
  2. Q-Learning on Frozen Lake Environment
  3. DQN and DDQN on Cartpole Environment

Mnih, Volodymyr, et al. “Human-level control through deep reinforcement learning.” Nature 518.7540 (2015): 529

DQN on Cartpole Environment:

Cartpole

Chapter 10 - Policy Gradient Methods

  1. REINFORCE, REINFORCE with Baseline, Actor-Critic, A2C

Sutton and Barto, Reinforcement Learning: An Introduction

Mnih, Volodymyr, et al. “Asynchronous methods for deep reinforcement learning.” International conference on machine learning. 2016.

Policy Gradient on MountainCar Continuous Environment:

Car

Chapter 11 - Object Detection

  1. Single-Shot Detection

Single-Shot Detection on 3 Objects
SSD

Chapter 12 - Semantic Segmentation

  1. FCN

  2. PSPNet

Semantic Segmentation

Semantic Segmentation

Chapter 13 - Unsupervised Learning using Mutual Information

  1. Invariant Information Clustering

  2. MINE: Mutual Information Estimation

MINE
MINE

Citation

If you find this work useful, please cite:

@book{atienza2020advanced,
  title={Advanced Deep Learning with TensorFlow 2 and Keras: Apply DL, GANs, VAEs, deep RL, unsupervised learning, object detection and segmentation, and more},
  author={Atienza, Rowel},
  year={2020},
  publisher={Packt Publishing Ltd}
}

Download Details:

Author: PacktPublishing
The Demo/Documentation: View The Demo/Documentation
Download Link: Download The Source Code
Official Website: https://github.com/PacktPublishing/Advanced-Deep-Learning-with-Keras
License: MIT

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#deep-learning #tensorflow #keras #machine-learning

Advanced Deep Learning with TensorFlow 2 and Keras