Search results for Convolutional Neural Networks

Found 393 documents, 5930 searched:

• A Comprehensive Guide to Convolutional Neural Networks

  Artificial Intelligence has been witnessing monumental growth in bridging the gap between the capabilities of humans and machines. Researchers and enthusiasts alike, work on numerous aspects of the field to make amazing things happen. One of many such areas is the domain of Computer Vision.

  https://www.kdnuggets.com/2023/06/comprehensive-guide-convolutional-neural-networks.html

• Image Classification with Convolutional Neural Networks (CNNs)

  In this article, we’ll look at what Convolutional Neural Networks are and how they work.

  https://www.kdnuggets.com/2022/05/image-classification-convolutional-neural-networks-cnns.html

• Introduction to Convolutional Neural Networks

  The article focuses on explaining key components in CNN and its implementation using Keras python library.

  https://www.kdnuggets.com/2020/06/introduction-convolutional-neural-networks.html

• Convolutional Neural Networks: A Python Tutorial Using TensorFlow and Keras">Convolutional Neural Networks: A Python Tutorial Using TensorFlow and Keras

  Different neural network architectures excel in different tasks. This particular article focuses on crafting convolutional neural networks in Python using TensorFlow and Keras.

  https://www.kdnuggets.com/2019/07/convolutional-neural-networks-python-tutorial-tensorflow-keras.html

• Using Topological Data Analysis to Understand the Behavior of Convolutional Neural Networks

  Neural Networks are powerful but complex and opaque tools. Using Topological Data Analysis, we can describe the functioning and learning of a convolutional neural network in a compact and understandable way.

  https://www.kdnuggets.com/2018/06/topological-data-analysis-convolutional-neural-networks.html

• Detecting Sarcasm with Deep Convolutional Neural Networks">Detecting Sarcasm with Deep Convolutional Neural Networks

  Detection of sarcasm is important in other areas such as affective computing and sentiment analysis because such expressions can flip the polarity of a sentence.

  https://www.kdnuggets.com/2018/06/detecting-sarcasm-deep-convolutional-neural-networks.html

• Understanding Deep Convolutional Neural Networks with a practical use-case in Tensorflow and Keras">Understanding Deep Convolutional Neural Networks with a practical use-case in Tensorflow and Keras

  We show how to build a deep neural network that classifies images to many categories with an accuracy of a 90%. This was a very hard problem before the rise of deep networks and especially Convolutional Neural Networks.

  https://www.kdnuggets.com/2017/11/understanding-deep-convolutional-neural-networks-tensorflow-keras.html

• How Convolutional Neural Networks Accomplish Image Recognition?

  Image recognition is very interesting and challenging field of study. Here we explain concepts, applications and techniques of image recognition using Convolutional Neural Networks.

  https://www.kdnuggets.com/2017/08/convolutional-neural-networks-image-recognition.html

• Visualizing Convolutional Neural Networks with Open-source Picasso

  Toolkits for standard neural network visualizations exist, along with tools for monitoring the training process, but are often tied to the deep learning framework. Could a general, easy-to-setup tool for generating standard visualizations provide a sanity check on the learning process?

  https://www.kdnuggets.com/2017/08/visualizing-convolutional-neural-networks-open-source-picasso.html

• An Intuitive Explanation of Convolutional Neural Networks

  This article provides a easy to understand introduction to what convolutional neural networks are and how they work.

  https://www.kdnuggets.com/2016/11/intuitive-explanation-convolutional-neural-networks.html

• A Beginner’s Guide To Understanding Convolutional Neural Networks Part 2

  This is the second part of a thorough introductory treatment of convolutional neural networks. Have a look after reading the first part.

  https://www.kdnuggets.com/2016/09/beginners-guide-understanding-convolutional-neural-networks-part-2.html

• A Beginner’s Guide To Understanding Convolutional Neural Networks Part 1">A Beginner’s Guide To Understanding Convolutional Neural Networks Part 1

  Interested in better understanding convolutional neural networks? Check out this first part of a very comprehensive overview of the topic.

  https://www.kdnuggets.com/2016/09/beginners-guide-understanding-convolutional-neural-networks-part-1.html

• How Convolutional Neural Networks Work

  Get an overview of what is going on inside convolutional neural networks, and what it is that makes them so effective.

  https://www.kdnuggets.com/2016/08/brohrer-convolutional-neural-networks-explanation.html

• Understanding Convolutional Neural Networks for NLP

  Dive into the world of Convolution Neural Networks (CNN), learn how they work, how to apply them for NLP, and how to tune CNN hyperparameters for best performance.

  https://www.kdnuggets.com/2015/11/understanding-convolutional-neural-networks-nlp.html

• A Brief History of the Neural Networks

  From the biological neuron to LLMs: How AI became smart.

  https://www.kdnuggets.com/a-brief-history-of-the-neural-networks

• Building a Convolutional Neural Network with PyTorch

  This blog post provides a tutorial on constructing a convolutional neural network for image classification in PyTorch, leveraging convolutional and pooling layers for feature extraction as well as fully-connected layers for prediction.

  https://www.kdnuggets.com/building-a-convolutional-neural-network-with-pytorch

• Exploring Neural Networks

  Unlocking the power of AI: a suide to neural networks and their applications.

  https://www.kdnuggets.com/exploring-neural-networks

• Neural Networks and Deep Learning: A Textbook (2nd Edition)

  The second edition of the book Neural Networks and Deep Learning is now available. This book covers both classical and modern models in deep learning. The book is intended to be a textbook for universities, and it covers the theoretical and algorithmic aspects of deep learning. The second edition is significantly expanded and covers many modern topics such as graph neural networks, adversarial learning, attention mechanisms, transformers, and large language models.

  https://www.kdnuggets.com/2023/07/aggarwal-neural-networks-deep-learning-textbook-2nd-edition.html

• Building and Training Your First Neural Network with TensorFlow and Keras

  Learn how to build and train your first Image Classification model with Keras and TensorFlow using Convolutional Neural Network.

  https://www.kdnuggets.com/2023/05/building-training-first-neural-network-tensorflow-keras.html

• A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainability

  We give a taxonomy of the trustworthy GNNs in privacy, robustness, fairness, and explainability. For each aspect, we categorize existing works into various categories, give general frameworks in each category, and more.

  https://www.kdnuggets.com/2022/05/comprehensive-survey-trustworthy-graph-neural-networks-privacy-robustness-fairness-explainability.html

• Learn Deep Learning by Building 15 Neural Network Projects in 2022

  Here are 15 neural network projects you can take on in 2022 to build your skills, your know-how, and your portfolio.

  https://www.kdnuggets.com/2022/01/15-neural-network-projects-build-2022.html

• Generating Beautiful Neural Network Visualizations">Generating Beautiful Neural Network Visualizations

  If you are looking to easily generate visualizations of neural network architectures, PlotNeuralNet is a project you should check out.

  https://www.kdnuggets.com/2020/12/generating-beautiful-neural-network-visualizations.html

• Optimization Algorithms in Neural Networks">Optimization Algorithms in Neural Networks

  This article presents an overview of some of the most used optimizers while training a neural network.

  https://www.kdnuggets.com/2020/12/optimization-algorithms-neural-networks.html

• A Friendly Introduction to Graph Neural Networks

  Despite being what can be a confusing topic, graph neural networks can be distilled into just a handful of simple concepts. Read on to find out more.

  https://www.kdnuggets.com/2020/11/friendly-introduction-graph-neural-networks.html

• How Do Neural Networks Learn?

  With neural networks being so popular today in AI and machine learning development, they can still look like a black box in terms of how they learn to make predictions. To understand what is going on deep in these networks, we must consider how neural networks perform optimization.

  https://www.kdnuggets.com/2020/08/how-neural-networks-learn.html

• Batch Normalization in Deep Neural Networks

  Batch normalization is a technique for training very deep neural networks that normalizes the contributions to a layer for every mini batch.

  https://www.kdnuggets.com/2020/08/batch-normalization-deep-neural-networks.html

• The Unreasonable Progress of Deep Neural Networks in Natural Language Processing (NLP)

  Natural language processing has made incredible advances through advanced techniques in deep learning. Learn about these powerful models, and find how close (or far away) these approaches are to human-level understanding.

  https://www.kdnuggets.com/2020/06/unreasonable-progress-deep-neural-networks-nlp.html

• Recreating Fingerprints using Convolutional Autoencoders

  The article gets you started working with fingerprints using Deep Learning.

  https://www.kdnuggets.com/2020/03/recreating-fingerprints-using-convolutional-autoencoders.html

• Deep Neural Networks

  We examine the features and applications of a deep neural network.

  https://www.kdnuggets.com/2020/02/deep-neural-networks.html

• Semi-supervised learning with Generative Adversarial Networks

  The paper discussed in this post, Semi-supervised learning with Generative Adversarial Networks, utilizes a GAN architecture for multi-label classification.

  https://www.kdnuggets.com/2020/01/semi-supervised-learning-generative-adversarial-networks.html

• 5 Techniques to Prevent Overfitting in Neural Networks

  In this article, I will present five techniques to prevent overfitting while training neural networks.

  https://www.kdnuggets.com/2019/12/5-techniques-prevent-overfitting-neural-networks.html

• Can Neural Networks Develop Attention? Google Thinks they Can

  Google recently published some work about modeling attention mechanisms in deep neural networks.

  https://www.kdnuggets.com/2019/11/neural-networks-develop-attention-google.html

• Convolutional Neural Network for Breast Cancer Classification

  See how Deep Learning can help in solving one of the most commonly diagnosed cancer in women.

  https://www.kdnuggets.com/2019/10/convolutional-neural-network-breast-cancer-classification.html

• Introduction to Artificial Neural Networks

  In this article, we’ll try to cover everything related to Artificial Neural Networks or ANN.

  https://www.kdnuggets.com/2019/10/introduction-artificial-neural-networks.html

• Can graph machine learning identify hate speech in online social networks?

  Online hate speech is a complex subject. Follow this demonstration using state-of-the-art graph neural network models to detect hateful users based on their activities on the Twitter social network.

  https://www.kdnuggets.com/2019/09/graph-machine-learning-hate-speech-social-networks.html

• Nothing but NumPy: Understanding & Creating Neural Networks with Computational Graphs from Scratch">Nothing but NumPy: Understanding & Creating Neural Networks with Computational Graphs from Scratch

  Entirely implemented with NumPy, this extensive tutorial provides a detailed review of neural networks followed by guided code for creating one from scratch with computational graphs.

  https://www.kdnuggets.com/2019/08/numpy-neural-networks-computational-graphs.html

• Training a Neural Network to Write Like Lovecraft">Training a Neural Network to Write Like Lovecraft

  In this post, the author attempts to train a neural network to generate Lovecraft-esque prose, known to be awkward and irregular at best. Did it end in success? If not, any suggestions on how it might have? Read on to find out.

  https://www.kdnuggets.com/2019/07/training-neural-network-write-like-lovecraft.html

• Random Forests® vs Neural Networks: Which is Better, and When?">Random Forests® vs Neural Networks: Which is Better, and When?

  Random Forests and Neural Network are the two widely used machine learning algorithms. What is the difference between the two approaches? When should one use Neural Network or Random Forest?

  https://www.kdnuggets.com/2019/06/random-forest-vs-neural-network.html

• Graduating in GANs: Going From Understanding Generative Adversarial Networks to Running Your Own

  Read how generative adversarial networks (GANs) research and evaluation has developed then implement your own GAN to generate handwritten digits.

  https://www.kdnuggets.com/2019/04/graduating-gans-understanding-generative-adversarial-networks.html

• Generative Adversarial Networks – Key Milestones and State of the Art

  We provide an overview of Generative Adversarial Networks (GANs), discuss challenges in GANs learning, and examine two promising GANs: the RadialGAN, designed for numbers, and the StyleGAN, which does style transfer for images.

  https://www.kdnuggets.com/2019/04/future-generative-adversarial-networks.html

• The Rise of Generative Adversarial Networks

  A comprehensive overview of Generative Adversarial Networks, covering its birth, different architectures including DCGAN, StyleGAN and BigGAN, as well as some real-world examples.

  https://www.kdnuggets.com/2019/04/rise-generative-adversarial-networks.html

• Predict Age and Gender Using Convolutional Neural Network and OpenCV">Predict Age and Gender Using Convolutional Neural Network and OpenCV

  Age and gender estimation from a single face image are important tasks in intelligent applications. As such, let's build a simple age and gender detection model in this detailed article.

  https://www.kdnuggets.com/2019/04/predict-age-gender-using-convolutional-neural-network-opencv.html

• Checklist for Debugging Neural Networks

  Check out these tangible steps you can take to identify and fix issues with training, generalization, and optimization for machine learning models.

  https://www.kdnuggets.com/2019/03/checklist-debugging-neural-networks.html

• Artificial Neural Network Implementation using NumPy and Image Classification">Artificial Neural Network Implementation using NumPy and Image Classification

  This tutorial builds artificial neural network in Python using NumPy from scratch in order to do an image classification application for the Fruits360 dataset

  https://www.kdnuggets.com/2019/02/artificial-neural-network-implementation-using-numpy-and-image-classification.html

• A comprehensive survey on graph neural networks

  This article summarizes a paper which presents us with a broad sweep of the graph neural network landscape. It’s a survey paper, so you’ll find details on the key approaches and representative papers, as well as information on commonly used datasets and benchmark performance on them.

  https://www.kdnuggets.com/2019/02/comprehensive-survey-graph-neural-networks.html

• Generative Adversarial Networks – Paper Reading Road Map

  To help the others who want to learn more about the technical sides of GANs, I wanted to share some papers I have read in the order that I read them.

  https://www.kdnuggets.com/2018/10/generative-adversarial-networks-paper-reading-road-map.html

• Sequence Modeling with Neural Networks – Part I

  In the context of this post, we will focus on modeling sequences as a well-known data structure and will study its specific learning framework.

  https://www.kdnuggets.com/2018/10/sequence-modeling-neural-networks-part-1.html

• Neural Networks and Deep Learning: A Textbook">Neural Networks and Deep Learning: A Textbook

  This book covers both classical and modern models in deep learning. The book is intended to be a textbook for universities, and it covers the theoretical and algorithmic aspects of deep learning.

  https://www.kdnuggets.com/2018/09/aggarwal-neural-networks-textbook.html

• Don’t Use Dropout in Convolutional Networks

  If you are wondering how to implement dropout, here is your answer - including an explanation on when to use dropout, an implementation example with Keras, batch normalization, and more.

  https://www.kdnuggets.com/2018/09/dropout-convolutional-networks.html

• Beginners Ask “How Many Hidden Layers/Neurons to Use in Artificial Neural Networks?”">Beginners Ask “How Many Hidden Layers/Neurons to Use in Artificial Neural Networks?”

  By the end of this article, you could at least get the idea of how these questions are answered and be able to test yourself based on simple examples.

  https://www.kdnuggets.com/2018/07/beginners-ask-how-many-hidden-layers-neurons-neural-networks.html

• Inside the Mind of a Neural Network with Interactive Code in Tensorflow

  Understand the inner workings of neural network models as this post covers three related topics: histogram of weights, visualizing the activation of neurons, and interior / integral gradients.

  https://www.kdnuggets.com/2018/06/inside-mind-neural-network-interactive-code-tensorflow.html

• Batch Normalization in Neural Networks

  This article explains batch normalization in a simple way. I wrote this article after what I learned from Fast.ai and deeplearning.ai.

  https://www.kdnuggets.com/2018/06/batch-normalization-neural-networks.html

• On the contribution of neural networks and word embeddings in Natural Language Processing

  In this post I will try to explain, in a very simplified way, how to apply neural networks and integrate word embeddings in text-based applications, and some of the main implicit benefits of using neural networks and word embeddings in NLP.

  https://www.kdnuggets.com/2018/05/contribution-neural-networks-word-embeddings-natural-language-processing.html

• Improving the Performance of a Neural Network

  There are many techniques available that could help us achieve that. Follow along to get to know them and to build your own accurate neural network.

  https://www.kdnuggets.com/2018/05/improving-performance-neural-network.html

• Building Convolutional Neural Network using NumPy from Scratch">Building Convolutional Neural Network using NumPy from Scratch

  In this article, CNN is created using only NumPy library. Just three layers are created which are convolution (conv for short), ReLU, and max pooling.

  https://www.kdnuggets.com/2018/04/building-convolutional-neural-network-numpy-scratch.html

• Derivation of Convolutional Neural Network from Fully Connected Network Step-By-Step

  What are the advantages of ConvNets over FC networks in image analysis? How is ConvNet derived from FC networks? Where the term convolution in CNNs came from? These questions are to be answered in this article.

  https://www.kdnuggets.com/2018/04/derivation-convolutional-neural-network-fully-connected-step-by-step.html

• The 8 Neural Network Architectures Machine Learning Researchers Need to Learn">The 8 Neural Network Architectures Machine Learning Researchers Need to Learn

  In this blog post, I want to share the 8 neural network architectures from the course that I believe any machine learning researchers should be familiar with to advance their work.

  https://www.kdnuggets.com/2018/02/8-neural-network-architectures-machine-learning-researchers-need-learn.html

• Building an Audio Classifier using Deep Neural Networks

  Using a deep convolutional neural network architecture to classify audio and how to effectively use transfer learning and data-augmentation to improve model accuracy using small datasets.

  https://www.kdnuggets.com/2017/12/audio-classifier-deep-neural-networks.html

• Estimating an Optimal Learning Rate For a Deep Neural Network

  This post describes a simple and powerful way to find a reasonable learning rate for your neural network.

  https://www.kdnuggets.com/2017/11/estimating-optimal-learning-rate-deep-neural-network.html

• 7 Types of Artificial Neural Networks for Natural Language Processing">7 Types of Artificial Neural Networks for Natural Language Processing

  What is an artificial neural network? How does it work? What types of artificial neural networks exist? How are different types of artificial neural networks used in natural language processing? We will discuss all these questions in the following article.

  https://www.kdnuggets.com/2017/10/7-types-artificial-neural-networks-natural-language-processing.html

• A Guide For Time Series Prediction Using Recurrent Neural Networks (LSTMs)

  Looking at the strengths of a neural network, especially a recurrent neural network, I came up with the idea of predicting the exchange rate between the USD and the INR.

  https://www.kdnuggets.com/2017/10/guide-time-series-prediction-recurrent-neural-networks-lstms.html

• Keras Tutorial: Recognizing Tic-Tac-Toe Winners with Neural Networks

  In this tutorial, we will build a neural network with Keras to determine whether or not tic-tac-toe games have been won by player X for given endgame board configurations. Introductory neural network concerns are covered.

  https://www.kdnuggets.com/2017/09/neural-networks-tic-tac-toe-keras.html

• Neural Network Foundations, Explained: Activation Function

  This is a very basic overview of activation functions in neural networks, intended to provide a very high level overview which can be read in a couple of minutes. This won't make you an expert, but it will give you a starting point toward actual understanding.

  https://www.kdnuggets.com/2017/09/neural-network-foundations-explained-activation-function.html

• Mind Reading: Using Artificial Neural Nets to Predict Viewed Image Categories From EEG Readings

  This post outlines the approach taken at a recent deep learning hackathon, hosted by YCombinator-backed startup DeepGram. The dataset: EEG readings from a Stanford research project that predicted which category of images their test subjects were viewing using linear discriminant analysis.

  https://www.kdnuggets.com/2017/08/mind-reading-using-artificial-neural-nets.html

• Building, Training, and Improving on Existing Recurrent Neural Networks

  In this post, we’ll provide a short tutorial for training a RNN for speech recognition, including code snippets throughout.

  https://www.kdnuggets.com/2017/05/building-training-improving-existing-recurrent-neural-networks.html

• Artificial Neural Networks (ANN) Introduction, Part 1

  This intro to ANNs will look at how we can train an algorithm to recognize images of handwritten digits. We will be using the images from the famous MNIST (Mixed National Institute of Standards and Technology) database.

  https://www.kdnuggets.com/2016/12/artificial-neural-networks-intro-part-1.html

• A Quick Introduction to Neural Networks

  This article provides a beginner level introduction to multilayer perceptron and backpropagation.

  https://www.kdnuggets.com/2016/11/quick-introduction-neural-networks.html

• Artificial Intelligence, Deep Learning, and Neural Networks, Explained">Artificial Intelligence, Deep Learning, and Neural Networks, Explained

  This article is meant to explain the concepts of AI, deep learning, and neural networks at a level that can be understood by most non-practitioners, and can also serve as a reference or review for technical folks as well.

  https://www.kdnuggets.com/2016/10/artificial-intelligence-deep-learning-neural-networks-explained.html

• Deep Residual Networks for Image Classification with Python + NumPy

  This post outlines the results of an innovative Deep Residual Network implementation for Image Classification using Python and NumPy.

  https://www.kdnuggets.com/2016/07/deep-residual-neworks-image-classification-python-numpy.html

• Must Know Tips for Deep Learning Neural Networks

  Deep learning is white hot research topic. Add some solid deep learning neural network tips and tricks from a PhD researcher.

  https://www.kdnuggets.com/2016/03/must-know-tips-deep-learning-part-1.html

• Recurrent Neural Networks Tutorial, Introduction

  Recurrent Neural Networks (RNNs) are popular models that have shown great promise in NLP and many other Machine Learning tasks. Here is a much-needed guide to key RNN models and a few brilliant research papers.

  https://www.kdnuggets.com/2015/10/recurrent-neural-networks-tutorial.html

• Top /r/MachineLearning Posts, September: Implement a neural network from scratch in C++

  Neural network in C++ for beginners, Chinese character handwriting recognition beats humans, a handy machine learning algorithm cheat sheet, neural nets versus functional programming, and a neural nets paper repository.

  https://www.kdnuggets.com/2015/10/top-reddit-machine-learning-september.html

• Data Science 101: Preventing Overfitting in Neural Networks

  Overfitting is a major problem for Predictive Analytics and especially for Neural Networks. Here is an overview of key methods to avoid overfitting, including regularization (L2 and L1), Max norm constraints and Dropout.

  https://www.kdnuggets.com/2015/04/preventing-overfitting-neural-networks.html

• Geoff Hinton AMA: Neural Networks, the Brain, and Machine Learning

  In a wide-ranging Q&A, Geoff Hinton addresses the future of deep learning, its biological inspirations, and his research philosophy.

  https://www.kdnuggets.com/2014/12/geoff-hinton-ama-neural-networks-brain-machine-learning.html

• Diving into the Pool: Unraveling the Magic of CNN Pooling Layers

  A Beginner's Guide to Max, Average, and Global Pooling in Convolutional Neural Networks.

  https://www.kdnuggets.com/diving-into-the-pool-unraveling-the-magic-of-cnn-pooling-layers

• KDnuggets News, May 11: SQL Notes for Professionals; How To Structure a Data Science Project

  SQL Notes for Professionals: The Free eBook Review; How To Structure a Data Science Project: A Step-by-Step Guide; Everything You Need to Know About Tensors; Free University Data Science Resources; Image Classification with Convolutional Neural Networks (CNNs)

  https://www.kdnuggets.com/2022/n19.html

• Geometric foundations of Deep Learning">Geometric foundations of Deep Learning

  Geometric Deep Learning is an attempt for geometric unification of a broad class of machine learning problems from the perspectives of symmetry and invariance. These principles not only underlie the breakthrough performance of convolutional neural networks and the recent success of graph neural networks but also provide a principled way to construct new types of problem-specific inductive biases.

  https://www.kdnuggets.com/2021/07/geometric-foundations-deep-learning.html

• Interactive Machine Learning Experiments

  Dive into experimenting with machine learning techniques using this open-source collection of interactive demos built on multilayer perceptrons, convolutional neural networks, and recurrent neural networks. Each package consists of ready-to-try web browser interfaces and fully-developed notebooks for you to fine tune the training for better performance.

  https://www.kdnuggets.com/2020/05/interactive-machine-learning-experiments.html

• GANs Need Some Attention, Too

  Self-Attention Generative Adversarial Networks (SAGAN; Zhang et al., 2018) are convolutional neural networks that use the self-attention paradigm to capture long-range spatial relationships in existing images to better synthesize new images.

  https://www.kdnuggets.com/2019/03/gans-need-some-attention-too.html

• Overview and benchmark of traditional and deep learning models in text classification

  In this post, traditional and deep learning models in text classification will be thoroughly investigated, including a discussion into both Recurrent and Convolutional neural networks.

  https://www.kdnuggets.com/2018/07/overview-benchmark-deep-learning-models-text-classification.html

• TensorFlow for Short-Term Stocks Prediction

  In this post you will see an application of Convolutional Neural Networks to stock market prediction, using a combination of stock prices with sentiment analysis.

  https://www.kdnuggets.com/2017/12/tensorflow-short-term-stocks-prediction.html

• KDnuggets™ News 16:n32, Sep 7: Cartoon: Data Scientist was sexiest job until…; Up to Speed on Deep Learning

  Cartoon: Data Scientist - the sexiest job of the 21st century until...; Up to Speed on Deep Learning: July Update; How Convolutional Neural Networks Work; Learning from Imbalanced Classes; What is the Role of the Activation Function in a Neural Network?

  https://www.kdnuggets.com/2016/n32.html

• Facebook Open Sources deep-learning modules for Torch

  We review Facebook recently released Torch module for Deep Learning, which helps researchers train large scale convolutional neural networks for image recognition, natural language processing and other AI applications.

  https://www.kdnuggets.com/2015/02/facebook-open-source-deep-learning-torch.html

• The Ultimate Roadmap to Becoming Specialised in The Tech Industry

  There is more than one route that you can take to be a competitive tech professional.

  https://www.kdnuggets.com/the-ultimate-roadmap-to-becoming-specialised-in-the-tech-industry

• Unlock the Secrets of LLMs in 60-Minute with Andrej Karpathy

  Karpathy's talk provides a comprehensive yet accessible introduction to large language models, explaining their capabilities, future potential, and associated security risks in an engaging manner.

  https://www.kdnuggets.com/unlock-the-secrets-of-llms-in-a-60-minute-with-andrej-karpathy

• Free Harvard Course: Introduction to AI with Python

  Looking for a great course to learn Artificial Intelligence with Python? Check out this free course from Harvard University.

  https://www.kdnuggets.com/free-harvard-course-introduction-to-ai-with-python

• 5 Super Cheat Sheets to Master Data Science

  The collection of super cheat sheets covers basic concepts of data science, probability & statistics, SQL, machine learning, and deep learning.

  https://www.kdnuggets.com/5-super-cheat-sheets-to-master-data-science

• Top 10 Kaggle Machine Learning Projects to Become Data Scientist in 2024

  Master Data Science with Top 10 Kaggle ML Projects to become a Data Scientist.

  https://www.kdnuggets.com/top-10-kaggle-machine-learning-projects-to-become-data-scientist-in-2024

• Gradient Descent: The Mountain Trekker’s Guide to Optimization with Mathematics

  Gradient descent is an optimization technique used to minimise errors in machine learning models. By iteratively adjusting parameters in the steepest direction of decrease, it seeks the lowest error value.

  https://www.kdnuggets.com/gradient-descent-the-mountain-trekker-guide-to-optimization-with-mathematics

• 5 Free Books to Master Data Science

  Want to break into data science? Check this list of free books for learning Python, statistics, linear algebra, machine learning and deep learning.

  https://www.kdnuggets.com/5-free-books-to-master-data-science

• 30 Years of Data Science: A Review From a Data Science Practitioner

  A review from a data science practitioner.

  https://www.kdnuggets.com/30-years-of-data-science-a-review-from-a-data-science-practitioner

• From Zero to Hero: Create Your First ML Model with PyTorch

  Learn the PyTorch basics by building a classification model from scratch.

  https://www.kdnuggets.com/from-zero-to-hero-create-your-first-ml-model-with-pytorch

• The Best Courses for AI from Universities with YouTube Playlists

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  https://www.kdnuggets.com/2023/08/best-courses-ai-universities-youtube-playlists.html

• Exploring the Latest Trends in AI/DL: From Metaverse to Quantum Computing

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  https://www.kdnuggets.com/2023/07/exploring-latest-trends-aidl-metaverse-quantum-computing.html

• KDnuggets News, June 28: 10 ChatGPT Plugins for Data Science Cheat Sheet • The ChatGPT Plugin That Automates Data Analysis

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  https://www.kdnuggets.com/2023/n23.html

• Calculate Computational Efficiency of Deep Learning Models with FLOPs and MACs

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  https://www.kdnuggets.com/2023/06/calculate-computational-efficiency-deep-learning-models-flops-macs.html

• Advanced Feature Selection Techniques for Machine Learning Models

  Mastering Feature Selection: An Exploration of Advanced Techniques for Supervised and Unsupervised Machine Learning Models.

  https://www.kdnuggets.com/2023/06/advanced-feature-selection-techniques-machine-learning-models.html

• Free TensorFlow 2.0 Complete Course

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  https://www.kdnuggets.com/2023/02/free-tensorflow-20-complete-course.html

• 10 Free Machine Learning Courses from Top Universities

  Learn the basics of machine learning, including classification, SVM, decision tree learning, neural networks, convolutional, neural networks, boosting, and K nearest neighbors.

  https://www.kdnuggets.com/2023/02/10-free-machine-learning-courses-top-universities.html

• 7 Best Libraries for Machine Learning Explained

  Learn about machine learning libraries for building and deploying machine learning models.

  https://www.kdnuggets.com/2023/01/7-best-libraries-machine-learning-explained.html

• 5 Free Data Science Books You Must Read in 2023

  Get your hands on these gems to learn Python, data analytics, machine learning, and deep learning.

  https://www.kdnuggets.com/2023/01/5-free-data-science-books-must-read-2023.html

• 7 Super Cheat Sheets You Need To Ace Machine Learning Interview

  Revise the concepts of machine learning algorithms, frameworks, and methodologies to ace the technical interview round.

  https://www.kdnuggets.com/2022/12/7-super-cheat-sheets-need-ace-machine-learning-interview.html

• Tuning Adam Optimizer Parameters in PyTorch

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  https://www.kdnuggets.com/2022/12/tuning-adam-optimizer-parameters-pytorch.html

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  https://www.kdnuggets.com/2022/12/getting-started-pytorch-lightning.html

• Research Papers for NLP Beginners

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  https://www.kdnuggets.com/2022/11/research-papers-nlp-beginners.html

• The ABCs of NLP, From A to Z

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  https://www.kdnuggets.com/2022/10/abcs-nlp-a-to-z.html

• Deep Learning Key Terms, Explained

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  https://www.kdnuggets.com/2016/10/deep-learning-key-terms-explained.html

• KDnuggets News, May 25: The 6 Python Machine Learning Tools Every Data Scientist Should Know About; The Complete Collection of Data Science Books

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  https://www.kdnuggets.com/2022/n21.html

• HuggingFace Has Launched a Free Deep Reinforcement Learning Course

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  https://www.kdnuggets.com/2022/05/huggingface-launched-free-deep-reinforcement-learning-course.html

• A Brief Introduction to Papers With Code

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  https://www.kdnuggets.com/2022/04/brief-introduction-papers-code.html

• Uncertainty Quantification in Artificial Intelligence-based Systems

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  https://www.kdnuggets.com/2022/04/uncertainty-quantification-artificial-intelligencebased-systems.html

• Classifying Long Text Documents Using BERT

  Transformer based language models such as BERT are really good at understanding the semantic context because they were designed specifically for that purpose. BERT outperforms all NLP baselines, but as we say in the scientific community, “no free lunch”. How can we use BERT to classify long text documents?

  https://www.kdnuggets.com/2022/02/classifying-long-text-documents-bert.html

• 6 Predictive Models Every Beginner Data Scientist Should Master">6 Predictive Models Every Beginner Data Scientist Should Master

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  https://www.kdnuggets.com/2021/12/6-predictive-models-every-beginner-data-scientist-master.html

• Main 2021 Developments and Key 2022 Trends in AI, Data Science, Machine Learning Technology

  Our panel of leading experts reviews 2021 main developments and examines the key trends in AI, Data Science, Machine Learning, and Deep Learning Technology.

  https://www.kdnuggets.com/2021/12/trends-ai-data-science-ml-technology.html

• 10 AI Project Ideas in Computer Vision

  The field of computer vision has seen the development of very powerful applications leveraging machine learning. These projects will introduce you to these techniques and guide you to more advanced practice to gain a deeper appreciation for the sophistication now available.

  https://www.kdnuggets.com/2021/11/10-ai-project-ideas-computer-vision.html

• Machine Learning Model Development and Model Operations: Principles and Practices">Machine Learning Model Development and Model Operations: Principles and Practices

  The ML model management and the delivery of highly performing model is as important as the initial build of the model by choosing right dataset. The concepts around model retraining, model versioning, model deployment and model monitoring are the basis for machine learning operations (MLOps) that helps the data science teams deliver highly performing models.

  https://www.kdnuggets.com/2021/10/machine-learning-model-development-operations-principles-practice.html

• How to Create an Interactive Dashboard in Three Steps with KNIME Analytics Platform

  In this blog post I will show you how to build a simple, but useful and good-looking dashboard to present your data - in three simple steps!

  https://www.kdnuggets.com/2021/10/interactive-dashboard-three-steps-knime-analytics-platform.html

• Computer Vision in Agriculture

  Deep learning isn’t just for placing ads or identifying cats anymore. Instead, a slew of young startups have started to incorporate the advances in computer vision made possible through larger and larger neural networks to real working robots in the fields.

  https://www.kdnuggets.com/2021/09/computer-vision-agriculture.html

• Path to Full Stack Data Science">Path to Full Stack Data Science

  Start your journey toward mastering all aspects of the field of Data Science with this focused list of in-depth self-learning resources. Curated with the beginner in mind, these recommendations will help you learn efficiently, and can also offer existing professionals useful highlights for review or help filling in any gaps in skills.

  https://www.kdnuggets.com/2021/09/path-full-stack-data-science.html

• 8 Deep Learning Project Ideas for Beginners">8 Deep Learning Project Ideas for Beginners

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  https://www.kdnuggets.com/2021/09/8-deep-learning-project-ideas-beginners.html

• Machine Learning Skills – Update Yours This Summer

  The process of mastering new knowledge often requires multiple passes to ensure the information is deeply understood. If you already began your journey into machine learning and data science, then you are likely ready for a refresher on topics you previously covered. This eight-week self-learning path will help you recapture the foundations and prepare you for future success in applying these skills.

  https://www.kdnuggets.com/2021/07/update-your-machine-learning-skills.html

• High-Performance Deep Learning: How to train smaller, faster, and better models – Part 5

  Training efficient deep learning models with any software tool is nothing without an infrastructure of robust and performant compute power. Here, current software and hardware ecosystems are reviewed that you might consider in your development when the highest performance possible is needed.

  https://www.kdnuggets.com/2021/07/high-performance-deep-learning-part5.html

• 7 Open Source Libraries for Deep Learning Graphs

  In this article we’ll go through 7 up-and-coming open source libraries for graph deep learning, ranked in order of increasing popularity.

  https://www.kdnuggets.com/2021/07/7-open-source-libraries-deep-learning-graphs.html

• High-Performance Deep Learning: How to train smaller, faster, and better models – Part 3

  Now that you are ready to efficiently build advanced deep learning models with the right software and hardware tools, the techniques involved in implementing such efforts must be explored to improve model quality and obtain the performance that your organization desires.

  https://www.kdnuggets.com/2021/07/high-performance-deep-learning-part3.html

• Computational Complexity of Deep Learning: Solution Approaches

  Why has deep learning been so successful? What is the fundamental reason that deep learning can learn from big data? Why cannot traditional ML learn from the large data sets that are now available for different tasks as efficiently as deep learning can?

  https://www.kdnuggets.com/2021/06/computational-complexity-deep-learning-solution-approaches.html

• High Performance Deep Learning, Part 1

  Advancing deep learning techniques continue to demonstrate incredible potential to deliver exciting new AI-enhanced software and systems. But, training the most powerful models is expensive--financially, computationally, and environmentally. Increasing the efficiency of such models will have profound impacts in many ways, so developing future models with this intension in mind will only help to further expand the reach, applicability, and value of what deep learning has to offer.

  https://www.kdnuggets.com/2021/06/efficiency-deep-learning-part1.html

• A checklist to track your Data Science progress">A checklist to track your Data Science progress

  Whether you are just starting out in data science or already a gainfully-employed professional, always learning more to advance through state-of-the-art techniques is part of the adventure. But, it can be challenging to track of your progress and keep an eye on what's next. Follow this checklist to help you scale your expertise from entry-level to advanced.

  https://www.kdnuggets.com/2021/05/checklist-data-science-progress.html

• The Three Edge Case Culprits: Bias, Variance, and Unpredictability

  Edge cases occur for three basic reasons: Bias – the ML system is too ‘simple’; Variance – the ML system is too ‘inexperienced’; Unpredictability – the ML system operates in an environment full of surprises. How do we recognize these edge cases situations, and what can we do about them?

  https://www.kdnuggets.com/2021/04/imerit2-bias-variance-unpredictability.html

• DeepMind’s AlphaFold & the Protein Folding Problem

  Recently, DeepMind's AlphaFold made impressive headway in the protein structure prediction problem. Read this for an overview and explanation.

  https://www.kdnuggets.com/2021/03/deepmind-alphafold-protein-folding-problem.html

• Deep learning doesn’t need to be a black box">Deep learning doesn’t need to be a black box

  The cultural perception of AI is often suspect because of the described challenges in knowing why a deep neural network makes its predictions. So, researchers try to crack open this "black box" after a network is trained to correlate results with inputs. But, what if the goal of explainability could be designed into the network's architecture -- before the model is trained and without reducing its predictive power? Maybe the box could stay open from the beginning.

  https://www.kdnuggets.com/2021/02/deep-learning-not-black-box.html