1627237526
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
1627224778
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
1627197534
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.
#python #web-development #machine-learning
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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.
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#opencv #python #data-science #machine-learning
1627179141
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
1627118431
K-Means clusternig example with Python and Scikit-learn
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 clustering is where the machine is allowed to decide how many clusters to create based on its own algorithms.
After downloading the required modules, run the file. You can play on it as much as you want.
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
All of these Codes belong to Sentdex. Thanks Senddex.
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
1627091793
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:
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#azure #docker #machine-learning
1627084698
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.
#artificial-intelligence #deep-learning #machine-learning
1627084486
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.
#deep-learning #python #machine-learning #tensorflow #artificial-intelligence
1627084410
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.
#deep-learning #python #machine-learning #artificial-intelligence #tensorflow
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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.
#python #deep-learning #artificial-intelligence #tensorflow #machine-learning
1627084206
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.
#deep-learning #machine-learning #artificial-intelligence #python #reinforcement-learning #data-science
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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
1627052100
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.
#data-science #web-scraping #scraping #selenium #machine-learning
1627028210
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.
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.
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-smi
shows 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%.
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
Sample outputs for random digits:
Sample outputs for random cifar10 images:
Sample outputs for random digits:
Sample outputs for digits 0 to 9:
Sample outputs for random digits:
Sample outputs for random digits:
Sample outputs for digits 0 to 9:
Sample outputs for digits 0 to 9:
Sample outputs for digits 0 to 9:
Sample outputs for random cifar10 images:
Sample outputs for MNIST to SVHN:
Generated MNIST by navigating the latent space:
Mnih, Volodymyr, et al. “Human-level control through deep reinforcement learning.” Nature 518.7540 (2015): 529
DQN on Cartpole Environment:
Sutton and Barto, Reinforcement Learning: An Introduction
Policy Gradient on MountainCar Continuous Environment:
Single-Shot Detection on 3 Objects
Semantic Segmentation
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}
}
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