Search results for activation function

Found 288 documents, 5934 searched:

• Unveiling Neural Magic: A Dive into Activation Functions

  Cracking the code of activation functions: Demystifying their purpose, selection, and timing.

  https://www.kdnuggets.com/unveiling-neural-magic-a-dive-into-activation-functions

• How Activation Functions Work in Deep Learning

  Check out a this article for a better understanding of activation functions.

  https://www.kdnuggets.com/2022/06/activation-functions-work-deep-learning.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

• What is the Role of the Activation Function in a Neural Network?

  Confused as to exactly what the activation function in a neural network does? Read this overview, and check out the handy cheat sheet at the end.

  https://www.kdnuggets.com/2016/08/role-activation-function-neural-network.html

• Multi-label NLP: An Analysis of Class Imbalance and Loss Function Approaches

  In this comprehensive article, we have demonstrated that a seemingly simple task of multi-label text classification can be challenging when traditional methods are applied. We have proposed the use of distribution-balancing loss functions to tackle the issue of class imbalance.

  https://www.kdnuggets.com/2023/03/multilabel-nlp-analysis-class-imbalance-loss-function-approaches.html

• KDnuggets News, February 15: Top Free Resources To Learn ChatGPT • 5 Pandas Plotting Functions You Might Not Know

  Top Free Resources To Learn ChatGPT • 5 Pandas Plotting Functions You Might Not Know • Python Function Arguments: A Definitive Guide • Making Intelligent Document Processing Smarter: Part 1 • Optimizing Python Code Performance: A Deep Dive into Python Profilers

  https://www.kdnuggets.com/2023/n06.html

• Activation maps for deep learning models in a few lines of code">Activation maps for deep learning models in a few lines of code

  We illustrate how to show the activation maps of various layers in a deep CNN model with just a couple of lines of code.

  https://www.kdnuggets.com/2019/10/activation-maps-deep-learning-models-lines-code.html

• A Single Function to Streamline Image Classification with Keras

  We show, step-by-step, how to construct a single, generalized, utility function to pull images automatically from a directory and train a convolutional neural net model.

  https://www.kdnuggets.com/2019/09/single-function-streamline-image-classification-keras.html

• Is ReLU After Sigmoid Bad?

  Recently [we] were analyzing how different activation functions interact among themselves, and we found that using relu after sigmoid in the last two layers worsens the performance of the model.

  https://www.kdnuggets.com/2018/03/relu-after-sigmoid-bad.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

• How to Standout and Safeguard Your Job in the Generative AI Era

  The secret recipe to excel in your career in AI.

  https://www.kdnuggets.com/how-to-standout-and-safeguard-your-job-in-the-generative-ai-era

• The Only Interview Prep Course You Need for Deep Learning

  Dive into the 50 most popular deep-learning questions to get you ready for your interview.

  https://www.kdnuggets.com/the-only-interview-prep-course-you-need-for-deep-learning

• 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

• 10 GitHub Repositories to Master Machine Learning

  The blog covers machine learning courses, bootcamps, books, tools, interview questions, cheat sheets, MLOps platforms, and more to master ML and secure your dream job.

  https://www.kdnuggets.com/10-github-repositories-to-master-machine-learning

• 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

• 7 Steps to Mastering Natural Language Processing

  Want to learn all about Natural Language Processing (NLP)? Here is a 7 step guide to help you go from the fundamentals of machine learning and Python to Transformers, recent advances in NLP, and beyond.

  https://www.kdnuggets.com/7-steps-to-mastering-natural-language-processing

• Introduction to Deep Learning Libraries: PyTorch and Lightning AI

  Simple explanation of PyTorch and Lightning AI.

  https://www.kdnuggets.com/introduction-to-deep-learning-libraries-pytorch-and-lightning-ai

• Exploring Neural Networks

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

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

• KDnuggets News, September 20: Python in Excel: This Will Change Data Science Forever • New KDnuggets Survey!

  Python in Excel: This Will Change Data Science Forever • KDnuggets Survey: Benchmark With Your Peers On Data Science Spend & Trends 2023 H2 • 5 Best AI Tools For Maximizing Productivity • And much more!

  https://www.kdnuggets.com/2023/n34.html

• Unlock the Secrets to Choosing the Perfect Machine Learning Algorithm!

  When working on a data science problem, one of the most important choices to make is selecting the appropriate machine learning algorithm.

  https://www.kdnuggets.com/2023/07/ml-algorithm-choose.html

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

  The author discusses several emerging trends in Artificial Intelligence and Deep Learning such as Metaverse and Quantum Computing.

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

• A Comparison of Machine Learning Algorithms in Python and R

  This list of the most commonly used machine learning algorithms in Python and R is intended to help novice engineers and enthusiasts get familiar with the most commonly used algorithms.

  https://www.kdnuggets.com/2023/06/machine-learning-algorithms-python-r.html

• Vanishing Gradient Problem: Causes, Consequences, and Solutions

  This blog post aims to describe the vanishing gradient problem and explain how use of the sigmoid function resulted in it.

  https://www.kdnuggets.com/2022/02/vanishing-gradient-problem.html

• Ten Years of AI in Review

  From image classification to chatbot therapy.

  https://www.kdnuggets.com/2023/06/ten-years-ai-review.html

• Deep Learning with R

  In this tutorial, learn how to perform a deep learning task in R.

  https://www.kdnuggets.com/2023/05/deep-learning-r.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

• Unveiling the Potential of CTGAN: Harnessing Generative AI for Synthetic Data

  CTGAN and other generative AI models can create synthetic tabular data for ML training, data augmentation, testing, privacy-preserving sharing, and more.

  https://www.kdnuggets.com/2023/04/unveiling-potential-ctgan-harnessing-generative-ai-synthetic-data.html

• What Is ChatGPT Doing and Why Does It Work?

  In this article, we will explain how ChatGPT works and why it is able to produce coherent and diverse conversations.

  https://www.kdnuggets.com/2023/04/chatgpt-work.html

• Top Free Data Science Online Courses for 2023

  Learn Data Science in 2023 for FREE with these online courses.

  https://www.kdnuggets.com/2023/03/top-free-data-science-online-courses-2023.html

• A Guide to Train an Image Classification Model Using Tensorflow

  Classify images at scale and with very high accuracy with the advent of machine learning and deep learning algorithms.

  https://www.kdnuggets.com/2022/12/guide-train-image-classification-model-tensorflow.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

• Getting Started with PyTorch Lightning

  Introduction to PyTorch Lightning and how it can be used for the model building process. It also provides a brief overview of the PyTorch characteristics and how they are different from TensorFlow.

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

• How LinkedIn Uses Machine Learning To Rank Your Feed

  In this post, you will learn to clarify business problems & constraints, understand problem statements, select evaluation metrics, overcome technical challenges, and design high-level systems.

  https://www.kdnuggets.com/2022/11/linkedin-uses-machine-learning-rank-feed.html

• Machine Learning Metadata Store

  In this article, we will learn about metadata stores, the need for them, their components, and metadata store management.

  https://www.kdnuggets.com/2022/08/machine-learning-metadata-store.html

• The Complete Collection of Data Science Interviews – Part 2

  The second part covers the list of Data Management, Data Engineering, Machine Learning, Deep Learning, Natural Language Processing, MLOps, Cloud Computing, and AI Manager interview questions.

  https://www.kdnuggets.com/2022/06/complete-collection-data-science-interviews-part-2.html

• Deep Learning Key Terms, Explained

  Gain a beginner's perspective on artificial neural networks and deep learning with this set of 14 straight-to-the-point related key concept definitions.

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

• KDnuggets News, June 8: 21 Cheat Sheets for Data Science Interviews; Top 18 Data Science Group on LinkedIn

  21 Cheat Sheets for Data Science Interviews; Top 18 Data Science Group on LinkedIn; A Beginner's Guide to Q Learning; 3 Ways Understanding Bayes Theorem Will Improve Your Data Science; Machine Learning Is Not Like Your Brain Part 3: Fundamental Architecture

  https://www.kdnuggets.com/2022/n23.html

• HuggingFace Has Launched a Free Deep Reinforcement Learning Course

  Hugging Face has released a free course on Deep RL. It is self-paced and shares a lot of pointers on theory, tutorials, and hands-on guides.

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

• Popular Machine Learning Algorithms

  This guide will help aspiring data scientists and machine learning engineers gain better knowledge and experience. I will list different types of machine learning algorithms, which can be used with both Python and R.

  https://www.kdnuggets.com/2022/05/popular-machine-learning-algorithms.html

• The “Hello World” of Tensorflow

  In this article, we will build a beginner-friendly machine learning model using TensorFlow.

  https://www.kdnuggets.com/2022/05/hello-world-tensorflow.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

• A Brief Introduction to Papers With Code

  One-stop shop to learn about state-of-the-art research papers with access to open-source resources including machine learning models, datasets, methods, evaluation tables, and code.

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

• Linear vs Logistic Regression: A Succinct Explanation

  Linear Regression and Logistic Regression are two well-used Machine Learning Algorithms that both branch off from Supervised Learning. Linear Regression is used to solve Regression problems whereas Logistic Regression is used to solve Classification problems. Read more here.

  https://www.kdnuggets.com/2022/03/linear-logistic-regression-succinct-explanation.html

• Essential Machine Learning Algorithms: A Beginner’s Guide

  Machine Learning as a technology, ensures that our current gadgets and their software get smarter by the day. Here are the algorithms that you ought to know about to understand Machine Learning’s varied and extensive functionalities and their effectiveness.

  https://www.kdnuggets.com/2021/05/essential-machine-learning-algorithms-beginners.html

• TensorFlow for Computer Vision – Transfer Learning Made Easy

  In this article, see how you can get above 90% accuracy on the validation set with a pretty straightforward approach. You'll also see what happens to the validation accuracy if we scale down the amount of training data by a factor of 20. Spoiler alert - it will remain unchanged.

  https://www.kdnuggets.com/2022/01/tensorflow-computer-vision-transfer-learning-made-easy.html

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

  Data Science models come with different flavors and techniques — luckily, most advanced models are based on a couple of fundamentals. Which models should you learn when you want to begin a career as Data Scientist? This post brings you 6 models that are widely used in the industry, either in standalone form or as a building block for other advanced techniques.

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

• Multivariate Time Series Analysis with an LSTM based RNN

  Check out this codeless solution using the Keras integration.

  https://www.kdnuggets.com/2021/10/multivariate-time-series-analysis-lstm-based-rnn.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

• Getting Started with PyTorch Lightning

  As a library designed for production research, PyTorch Lightning streamlines hardware support and distributed training as well, and we’ll show how easy it is to move training to a GPU toward the end.

  https://www.kdnuggets.com/2021/10/getting-started-pytorch-lightning.html

• Four Basic Steps in Data Preparation">Four Basic Steps in Data Preparation

  What we would like to do here is introduce four very basic and very general steps in data preparation for machine learning algorithms. We will describe how and why to apply such transformations within a specific example.

  https://www.kdnuggets.com/2021/10/four-basic-steps-data-preparation.html

• Introduction to AutoEncoder and Variational AutoEncoder (VAE)">Introduction to AutoEncoder and Variational AutoEncoder (VAE)

  Autoencoders and their variants are interesting and powerful artificial neural networks used in unsupervised learning scenarios. Learn how autoencoders perform in their different approaches and how to implement with Keras on the instructional data set of the MNIST digits.

  https://www.kdnuggets.com/2021/10/introduction-autoencoder-variational-autoencoder-vae.html

• Parallelizing Python Code

  This article reviews some common options for parallelizing Python code, including process-based parallelism, specialized libraries, ipython parallel, and Ray.

  https://www.kdnuggets.com/2021/10/parallelizing-python-code.html

• Teaching AI to Classify Time-series Patterns with Synthetic Data">Teaching AI to Classify Time-series Patterns with Synthetic Data

  How to build and train an AI model to identify various common anomaly patterns in time-series data.

  https://www.kdnuggets.com/2021/10/teaching-ai-classify-time-series-patterns-synthetic-data.html

• Surpassing Trillion Parameters and GPT-3 with Switch Transformers – a path to AGI?">Surpassing Trillion Parameters and GPT-3 with Switch Transformers – a path to AGI?

  Ever larger models churning on increasingly faster machines suggest a potential path toward smarter AI, such as with the massive GPT-3 language model. However, new, more lean, approaches are being conceived and explored that may rival these super-models, which could lead to a future with more efficient implementations of advanced AI-driven systems.

  https://www.kdnuggets.com/2021/10/trillion-parameters-gpt-3-switch-transformers-path-agi.html

• An Introduction to Reinforcement Learning with OpenAI Gym, RLlib, and Google Colab

  Get an Introduction to Reinforcement Learning by attempting to balance a virtual CartPole with OpenAI Gym, RLlib, and Google Colab.

  https://www.kdnuggets.com/2021/09/intro-reinforcement-learning-openai-gym-rllib-colab.html

• From Scratch: Permutation Feature Importance for ML Interpretability

  Use permutation feature importance to discover which features in your dataset are useful for prediction — implemented from scratch in Python.

  https://www.kdnuggets.com/2021/06/from-scratch-permutation-feature-importance-ml-interpretability.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

• How a Single Mistake Wasted 3 Years of My Data Science Journey

  Self-paced courses are just sleeping pills; Industry experts are the right choice.

  https://www.kdnuggets.com/2021/06/single-mistake-wasted-3-years-data-science.html

• Deep Learning Recommendation Models (DLRM): A Deep Dive

  The currency in the 21st century is no longer just data. It's the attention of people. This deep dive article presents the architecture and deployment issues experienced with the deep learning recommendation model, DLRM, which was open-sourced by Facebook in March 2019.

  https://www.kdnuggets.com/2021/04/deep-learning-recommendation-models-dlrm-deep-dive.html

• My machine learning model does not learn. What should I do?

  This article presents 7 hints on how to get out of the quicksand.

  https://www.kdnuggets.com/2021/02/machine-learning-model-not-learn.html

• Vision Transformers: Natural Language Processing (NLP) Increases Efficiency and Model Generality

  Why do we hear so little about transformer models applied to computer vision tasks? What about attention in computer vision networks?

  https://www.kdnuggets.com/2021/02/vision-transformers-nlp-efficiency-model-generality.html

• Popular Machine Learning Interview Questions, part 2

  Get ready for your next job interview requiring domain knowledge in machine learning with answers to these thirteen common questions.

  https://www.kdnuggets.com/2021/01/popular-machine-learning-interview-questions-part2.html

• Popular Machine Learning Interview Questions">Popular Machine Learning Interview Questions

  Get ready for your next job interview requiring domain knowledge in machine learning with answers to these eleven common questions.

  https://www.kdnuggets.com/2021/01/popular-machine-learning-interview-questions.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

• Implementing a Deep Learning Library from Scratch in Python">Implementing a Deep Learning Library from Scratch in Python

  A beginner’s guide to understanding the fundamental building blocks of deep learning platforms.

  https://www.kdnuggets.com/2020/09/implementing-deep-learning-library-scratch-python.html

• AI Papers to Read in 2020

  Reading suggestions to keep you up-to-date with the latest and classic breakthroughs in AI and Data Science.

  https://www.kdnuggets.com/2020/09/ai-papers-read-2020.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

• Recurrent Neural Networks (RNN): Deep Learning for Sequential Data

  Recurrent Neural Networks can be used for a number of ways such as detecting the next word/letter, forecasting financial asset prices in a temporal space, action modeling in sports, music composition, image generation, and more.

  https://www.kdnuggets.com/2020/07/rnn-deep-learning-sequential-data.html

• Getting Started with TensorFlow 2">Getting Started with TensorFlow 2

  Learn about the latest version of TensorFlow with this hands-on walk-through of implementing a classification problem with deep learning, how to plot it, and how to improve its results.

  https://www.kdnuggets.com/2020/07/getting-started-tensorflow2.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

• The Most Important Fundamentals of PyTorch you Should Know">The Most Important Fundamentals of PyTorch you Should Know

  PyTorch is a constantly developing deep learning framework with many exciting additions and features. We review its basic elements and show an example of building a simple Deep Neural Network (DNN) step-by-step.

  https://www.kdnuggets.com/2020/06/fundamentals-pytorch.html

• Deep Learning for Detecting Pneumonia from X-ray Images">Deep Learning for Detecting Pneumonia from X-ray Images

  This article covers an end to end pipeline for pneumonia detection from X-ray images.

  https://www.kdnuggets.com/2020/06/deep-learning-detecting-pneumonia-x-ray-images.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

• Deepmind’s Gaming Streak: The Rise of AI Dominance

  There is still a long way to go before machine agents match overall human gaming prowess, but Deepmind’s gaming research focus has shown a clear progression of substantial progress.

  https://www.kdnuggets.com/2020/05/deepmind-gaming-ai-dominance.html

• 3 Reasons to Use Random Forest® Over a Neural Network: Comparing Machine Learning versus Deep Learning

  Both the random forest algorithm and Neural Networks are different techniques that learn differently but can be used in similar domains. Why would you use one over the other?

  https://www.kdnuggets.com/2020/04/3-reasons-random-forest-neural-network-comparison.html

• Build an Artificial Neural Network From Scratch: Part 2

  The second article in this series focuses on building an Artificial Neural Network using the Numpy Python library.

  https://www.kdnuggets.com/2020/03/build-artificial-neural-network-scratch-part-2.html

• Generate Realistic Human Face using GAN

  This article contain a brief intro to Generative Adversarial Network(GAN) and how to build a Human Face Generator.

  https://www.kdnuggets.com/2020/03/generate-realistic-human-face-using-gan.html

• Hands on Hyperparameter Tuning with Keras Tuner

  Or how hyperparameter tuning with Keras Tuner can boost your object classification network's accuracy by 10%.

  https://www.kdnuggets.com/2020/02/hyperparameter-tuning-keras-tuner.html

• Practical Hyperparameter Optimization

  An introduction on how to fine-tune Machine and Deep Learning models using techniques such as: Random Search, Automated Hyperparameter Tuning and Artificial Neural Networks Tuning.

  https://www.kdnuggets.com/2020/02/practical-hyperparameter-optimization.html

• Intent Recognition with BERT using Keras and TensorFlow 2

  TL;DR Learn how to fine-tune the BERT model for text classification. Train and evaluate it on a small dataset for detecting seven intents. The results might surprise you!

  https://www.kdnuggets.com/2020/02/intent-recognition-bert-keras-tensorflow.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

• H2O Framework for Machine Learning

  This article is an overview of H2O, a scalable and fast open-source platform for machine learning. We will apply it to perform classification tasks.

  https://www.kdnuggets.com/2020/01/h2o-framework-machine-learning.html

• Fighting Overfitting in Deep Learning

  This post outlines an attack plan for fighting overfitting in neural networks.

  https://www.kdnuggets.com/2019/12/fighting-overfitting-deep-learning.html

• Random Forest® vs Neural Networks for Predicting Customer Churn

  Let us see how random forest competes with neural networks for solving a real world business problem.

  https://www.kdnuggets.com/2019/12/random-forest-vs-neural-networks-predicting-customer-churn.html

• Deploying a pretrained GPT-2 model on AWS

  This post attempts to summarize my recent detour into NLP, describing how I exposed a Huggingface pre-trained Language Model (LM) on an AWS-based web application.

  https://www.kdnuggets.com/2019/12/deploying-pretrained-gpt-2-model-aws.html

• Enabling the Deep Learning Revolution

  Deep learning models are revolutionizing the business and technology world with jaw-dropping performances in one application area after another. Read this post on some of the numerous composite technologies which allow deep learning its complex nonlinearity.

  https://www.kdnuggets.com/2019/12/enabling-deep-learning-revolution.html

• Deep Learning for Image Classification with Less Data

  In this blog I will be demonstrating how deep learning can be applied even if we don’t have enough data.

  https://www.kdnuggets.com/2019/11/deep-learning-image-classification-less-data.html

• Generalization in Neural Networks

  When training a neural network in deep learning, its performance on processing new data is key. Improving the model's ability to generalize relies on preventing overfitting using these important methods.

  https://www.kdnuggets.com/2019/11/generalization-neural-networks.html

• Research Guide for Depth Estimation with Deep Learning

  In this guide, we’ll look at papers aimed at solving the problems of depth estimation using deep learning.

  https://www.kdnuggets.com/2019/11/research-guide-depth-estimation-deep-learning.html

• Designing Your Neural Networks

  Check out this step-by-step walk through of some of the more confusing aspects of neural nets to guide you to making smart decisions about your neural network architecture.

  https://www.kdnuggets.com/2019/11/designing-neural-networks.html

• Build an Artificial Neural Network From Scratch: Part 1

  This article focused on building an Artificial Neural Network using the Numpy Python library.

  https://www.kdnuggets.com/2019/11/build-artificial-neural-network-scratch-part-1.html

• How to Build Your Own Logistic Regression Model in Python

  A hands on guide to Logistic Regression for aspiring data scientist and machine learning engineer.

  https://www.kdnuggets.com/2019/10/build-logistic-regression-model-python.html

• Research Guide for Transformers

  The problem with RNNs and CNNs is that they aren’t able to keep up with context and content when sentences are too long. This limitation has been solved by paying attention to the word that is currently being operated on. This guide will focus on how this problem can be addressed by Transformers with the help of deep learning.

  https://www.kdnuggets.com/2019/10/research-guide-transformers.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

• Writing Your First Neural Net in Less Than 30 Lines of Code with Keras

  Read this quick overview of neural networks and learn how to implement your first in very few lines using Keras.

  https://www.kdnuggets.com/2019/10/writing-first-neural-net-less-30-lines-code-keras.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

• A 2019 Guide for Automatic Speech Recognition

  In this article, we’ll look at a couple of papers aimed at solving the problem of automated speech recognition with machine and deep learning.

  https://www.kdnuggets.com/2019/09/2019-guide-automatic-speech-recognition.html

• A 2019 Guide to Human Pose Estimation

  Human pose estimation refers to the process of inferring poses in an image. Essentially, it entails predicting the positions of a person’s joints in an image or video. This problem is also sometimes referred to as the localization of human joints.

  https://www.kdnuggets.com/2019/08/2019-guide-human-pose-estimation.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

• Deep Learning for NLP: Creating a Chatbot with Keras!">Deep Learning for NLP: Creating a Chatbot with Keras!

  Learn how to use Keras to build a Recurrent Neural Network and create a Chatbot! Who doesn’t like a friendly-robotic personal assistant?

  https://www.kdnuggets.com/2019/08/deep-learning-nlp-creating-chatbot-keras.html

• Deep Learning for NLP: ANNs, RNNs and LSTMs explained!">Deep Learning for NLP: ANNs, RNNs and LSTMs explained!

  Learn about Artificial Neural Networks, Deep Learning, Recurrent Neural Networks and LSTMs like never before and use NLP to build a Chatbot!

  https://www.kdnuggets.com/2019/08/deep-learning-nlp-explained.html

• Understanding Tensor Processing Units

  The Tensor Processing Unit (TPU) is Google's custom tool to accelerate machine learning workloads using the TensorFlow framework. Learn more about what TPUs do and how they can work for you.

  https://www.kdnuggets.com/2019/07/understanding-tensor-processing-units.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

• Pre-training, Transformers, and Bi-directionality

  Bidirectional Encoder Representations from Transformers BERT (Devlin et al., 2018) is a language representation model that combines the power of pre-training with the bi-directionality of the Transformer’s encoder (Vaswani et al., 2017). BERT improves the state-of-the-art performance on a wide array of downstream NLP tasks with minimal additional task-specific training.

  https://www.kdnuggets.com/2019/07/pre-training-transformers-bi-directionality.html

• Building a Recommender System, Part 2

  This post explores an technique for collaborative filtering which uses latent factor models, a which naturally generalizes to deep learning approaches. Our approach will be implemented using Tensorflow and Keras.

  https://www.kdnuggets.com/2019/07/building-recommender-system-part-2.html

• How to Automate Hyperparameter Optimization

  A step-by-step guide into performing a hyperparameter optimization task on a deep learning model by employing Bayesian Optimization that uses the Gaussian Process. We used the gp_minimize package provided by the Scikit-Optimize (skopt) library to perform this task.

  https://www.kdnuggets.com/2019/06/automate-hyperparameter-optimization.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

• Understanding Backpropagation as Applied to LSTM

  Backpropagation is one of those topics that seem to confuse many once you move past feed-forward neural networks and progress to convolutional and recurrent neural networks. This article gives you and overall process to understanding back propagation by giving you the underlying principles of backpropagation.

  https://www.kdnuggets.com/2019/05/understanding-backpropagation-applied-lstm.html

• Artificial Neural Networks Optimization using Genetic Algorithm with Python">Artificial Neural Networks Optimization using Genetic Algorithm with Python

  This tutorial explains the usage of the genetic algorithm for optimizing the network weights of an Artificial Neural Network for improved performance.

  https://www.kdnuggets.com/2019/03/artificial-neural-networks-optimization-genetic-algorithm-python.html

• Breaking neural networks with adversarial attacks

  We develop an intuition behind "adversarial attacks" on deep neural networks, and understand why these attacks are so successful.

  https://www.kdnuggets.com/2019/03/breaking-neural-networks-adversarial-attacks.html

• Neural Networks with Numpy for Absolute Beginners: Introduction

  In this tutorial, you will get a brief understanding of what Neural Networks are and how they have been developed. In the end, you will gain a brief intuition as to how the network learns.

  https://www.kdnuggets.com/2019/03/neural-networks-numpy-absolute-beginners-introduction.html

• How to do Everything in Computer Vision

  The many standard tasks in computer vision all require special consideration: classification, detection, segmentation, pose estimation, enhancement and restoration, and action recognition. Let me show you how to do everything in Computer Vision with Deep Learning!

  https://www.kdnuggets.com/2019/02/everything-computer-vision.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

• Neural Networks – an Intuition

  Neural networks are one of the most powerful algorithms used in the field of machine learning and artificial intelligence. We attempt to outline its similarities with the human brain and how intuition plays a big part in this.

  https://www.kdnuggets.com/2019/02/neural-networks-intuition.html

• Building an image search service from scratch

  By the end of this post, you should be able to build a quick semantic search model from scratch, no matter the size of your dataset.

  https://www.kdnuggets.com/2019/01/building-image-search-service-from-scratch.html

• Word Embeddings & Self-Supervised Learning, Explained

  There are many algorithms to learn word embeddings. Here, we consider only one of them: word2vec, and only one version of word2vec called skip-gram, which works well in practice.

  https://www.kdnuggets.com/2019/01/burkov-self-supervised-learning-word-embeddings.html

• End To End Guide For Machine Learning Projects">End To End Guide For Machine Learning Projects

  Let’s imagine you are attempting to work on a machine learning project. This article will provide you with the step to step guide on the process that you can follow to implement a successful project.

  https://www.kdnuggets.com/2019/01/end-to-end-guide-machine-learning-project.html

• The Backpropagation Algorithm Demystified

  A crucial aspect of machine learning is its ability to recognize error margins and to interpret data more precisely as rising numbers of datasets are fed through its neural network. Commonly referred to as backpropagation, it is a process that isn’t as complex as you might think.

  https://www.kdnuggets.com/2019/01/backpropagation-algorithm-demystified.html

• How Different are Conventional Programming and Machine Learning?

  When I heard about Machine Learning I couldn't contain the amazement. I was not able to get my mind around the fact, that unlike normal software programs - which I was accustomed to - I wouldn't even have to teach a computer the "how" in detail about all the future scenarios up front.

  https://www.kdnuggets.com/2018/12/different-conventional-programming-machine-learning.html

• Deep Learning for the Masses (… and The Semantic Layer)

  Deep learning is everywhere right now, in your watch, in your television, your phone, and in someway the platform you are using to read this article. Here I’ll talk about how can you start changing your business using Deep Learning in a very simple way. But first, you need to know about the Semantic Layer.

  https://www.kdnuggets.com/2018/11/deep-learning-masses-semantic-layer.html

• Introduction to Deep Learning with Keras

  In this article, we’ll build a simple neural network using Keras. Now let’s proceed to solve a real business problem: an insurance company wants you to develop a model to help them predict which claims look fraudulent.

  https://www.kdnuggets.com/2018/10/introduction-deep-learning-keras.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

• How to Create a Simple Neural Network in Python">How to Create a Simple Neural Network in Python

  The best way to understand how neural networks work is to create one yourself. This article will demonstrate how to do just that.

  https://www.kdnuggets.com/2018/10/simple-neural-network-python.html

• 5 Reasons Why You Should Use Cross-Validation in Your Data Science Projects

  In cross-validation, we do more than one split. We can do 3, 5, 10 or any K number of splits. Those splits called Folds, and there are many strategies we can create these folds with.

  https://www.kdnuggets.com/2018/10/5-reasons-cross-validation-data-science-projects.html

• Introduction to Deep Learning

  I decided to begin to put some structure in my understanding of Neural Networks through this series of articles.

  https://www.kdnuggets.com/2018/09/introduction-deep-learning.html

• Ultimate Guide to Getting Started with TensorFlow">Ultimate Guide to Getting Started with TensorFlow

  Including video and written tutorials, beginner code examples, useful tricks, helpful communities, books, jobs and more - this is the ultimate guide to getting started with TensorFlow.

  https://www.kdnuggets.com/2018/09/ultimate-guide-tensorflow.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

• fast.ai Deep Learning Part 1 Complete Course Notes

  This posts is a collection of a set of fantastic notes on the fast.ai deep learning part 1 MOOC freely available online, as written and shared by a student. These notes are a valuable learning resource either as a supplement to the courseware or on their own.

  https://www.kdnuggets.com/2018/07/fast-ai-deep-learning-part-1-notes.html

• The Keras 4 Step Workflow">The Keras 4 Step Workflow

  In his book "Deep Learning with Python," Francois Chollet outlines a process for developing neural networks with Keras in 4 steps. Let's take a look at this process with a simple example.

  https://www.kdnuggets.com/2018/06/keras-4-step-workflow.html