Search results for "gradient descent"

293 documents found out of 7176 total.

• Deep Learning Breakthrough: a sub-linear deep learning algorithm that does not need a GPU?

  Deep Learning sits at the forefront of many important advances underway in machine learning. With backpropagation being a primary training method, its computational inefficiencies require sophisticated hardware, such as GPUs. Learn about this recent breakthrough algorithmic advancement with improvements to the backpropgation calculations on a CPU that outperforms large neural network training with a GPU.

  https://www.kdnuggets.com/2020/03/deep-learning-breakthrough-sub-linear-algorithm-no-gpu.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

• Time Series Classification Synthetic vs Real Financial Time Series">Time Series Classification Synthetic vs Real Financial Time Series

  This article discusses distinguishing between real financial time series and synthetic time series using XGBoost.

  https://www.kdnuggets.com/2020/03/time-series-classification-synthetic-real-financial-time-series.html

• Few-Shot Image Classification with Meta-Learning

  Here is how you can teach your model to learn quickly from a few examples.

  https://www.kdnuggets.com/2020/03/few-shot-image-classification-meta-learning.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

• Data Science Curriculum for self-study

  Are you asking the question, "how do I become a Data Scientist?" This list recommends the best essential topics to gain an introductory understanding for getting started in the field. After learning these basics, keep in mind that doing real data science projects through internships or competitions is crucial to acquiring the core skills necessary for the job.

  https://www.kdnuggets.com/2020/02/data-science-curriculum-self-study.html

• Audio Data Analysis Using Deep Learning with Python (Part 2)

  This is a followup to the first article in this series. Once you are comfortable with the concepts explained in that article, you can come back and continue with this.

  https://www.kdnuggets.com/2020/02/audio-data-analysis-deep-learning-python-part-2.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

• 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

• Microsoft Introduces Project Petridish to Find the Best Neural Network for Your Problem">Microsoft Introduces Project Petridish to Find the Best Neural Network for Your Problem

  The new algorithm takes a novel approach to neural architecture search.

  https://www.kdnuggets.com/2020/01/microsoft-introduces-project-petridish-best-neural-network.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

• The ravages of concept drift in stream learning applications and how to deal with it

  Stream data processing has gained progressive momentum with the arriving of new stream applications and big data scenarios. These streams of data evolve generally over time and may be occasionally affected by a change (concept drift). How to handle this change by using detection and adaptation mechanisms is crucial in many real-world systems.

  https://www.kdnuggets.com/2019/12/ravages-concept-drift-stream-learning-applications.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

• 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

• 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 Bayes’ Theorem is Applied in Machine Learning

  Learn how Bayes Theorem is in Machine Learning for classification and regression!

  https://www.kdnuggets.com/2019/10/bayes-theorem-applied-machine-learning.html

• Beyond Word Embedding: Key Ideas in Document Embedding

  This literature review on document embedding techniques thoroughly covers the many ways practitioners develop rich vector representations of text -- from single sentences to entire books.

  https://www.kdnuggets.com/2019/10/beyond-word-embedding-document-embedding.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

• Multi-Task Learning – ERNIE 2.0: State-of-the-Art NLP Architecture Intuitively Explained

  The tech giant Baidu unveiled its state-of-the-art NLP architecture ERNIE 2.0 earlier this year, which scored significantly higher than XLNet and BERT on all tasks in the GLUE benchmark. This major breakthrough in NLP takes advantage of a new innovation called “Continual Incremental Multi-Task Learning”.

  https://www.kdnuggets.com/2019/10/multi-task-learning-ernie-sota-nlp-architecture.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

• Reddit Post Classification

  This article covers the implementation of a data scraping and natural language processing project which had two parts: scrape as many posts from Reddit’s API as allowed &then use classification models to predict the origin of the posts.

  https://www.kdnuggets.com/2019/09/reddit-post-classification.html

• My journey path from a Software Engineer to BI Specialist to a Data Scientist">My journey path from a Software Engineer to BI Specialist to a Data Scientist

  The career path of the Data Scientist remains a hot target for many with its continuing high demand. Becoming one requires developing a broad set of skills including statistics, programming, and even business acumen. Learn more about one person's experience making this journey, and discover the many resources available to help you find your way into a world of data science.

  https://www.kdnuggets.com/2019/09/journey-software-engineer-bi-data-scientist.html

• There is No Free Lunch in Data Science">There is No Free Lunch in Data Science

  There is no such thing as a free lunch in life or data science. Here, we'll explore some science philosophy and discuss the No Free Lunch theorems to find out what they mean for the field of data science.

  https://www.kdnuggets.com/2019/09/no-free-lunch-data-science.html

• Train sklearn 100x Faster">Train sklearn 100x Faster

  As compute gets cheaper and time to market for machine learning solutions becomes more critical, we’ve explored options for speeding up model training. One of those solutions is to combine elements from Spark and scikit-learn into our own hybrid solution.

  https://www.kdnuggets.com/2019/09/train-sklearn-100x-faster.html

• I wasn’t getting hired as a Data Scientist. So I sought data on who is.">I wasn’t getting hired as a Data Scientist. So I sought data on who is.

  Instead of focusing on skills thought to be required of data scientists, we can look at what they have actually done before.

  https://www.kdnuggets.com/2019/09/getting-hired-data-scientist-sought-data.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

• What is Poisson Distribution?

  An solid overview of the Poisson distribution, starting from why it is needed, how it stacks up to binomial distribution, deriving its formula mathematically, and more.

  https://www.kdnuggets.com/2019/08/poisson-distribution.html

• A 2019 Guide to Semantic Segmentation

  Semantic segmentation refers to the process of linking each pixel in an image to a class label. These labels could include a person, car, flower, piece of furniture, etc., just to mention a few. We’ll now look at a number of research papers on covering state-of-the-art approaches to building semantic segmentation models.

  https://www.kdnuggets.com/2019/08/2019-guide-semantic-segmentation.html

• 9 Tips For Training Lightning-Fast Neural Networks In Pytorch

  Who is this guide for? Anyone working on non-trivial deep learning models in Pytorch such as industrial researchers, Ph.D. students, academics, etc. The models we're talking about here might be taking you multiple days to train or even weeks or months.

  https://www.kdnuggets.com/2019/08/9-tips-training-lightning-fast-neural-networks-pytorch.html

• Lagrange multipliers with visualizations and code

  In this story, we’re going to take an aerial tour of optimization with Lagrange multipliers. When do we need them? Whenever we have an optimization problem with constraints.

  https://www.kdnuggets.com/2019/08/lagrange-multipliers-visualizations-code.html

• Pytorch Cheat Sheet for Beginners and Udacity Deep Learning Nanodegree

  This cheatsheet should be easier to digest than the official documentation and should be a transitional tool to get students and beginners to get started reading documentations soon.

  https://www.kdnuggets.com/2019/08/pytorch-cheat-sheet-beginners.html

• How to Make Stunning 3D Plots for Better Storytelling

  3D Plots built in the right way for the right purpose are always stunning. In this article, we’ll see how to make stunning 3D plots with R using ggplot2 and rayshader.

  https://www.kdnuggets.com/2019/07/stunning-3d-plots-better-storytelling.html

• 10 New Things I Learnt from fast.ai Course V3

  Fastai offers some really good courses in machine learning and deep learning for programmers. I recently took their "Practical Deep Learning for Coders" course and found it really interesting. Here are my learnings from the course.

  https://www.kdnuggets.com/2019/06/things-learnt-fastai-course.html

• Evolving Deep Neural Networks

  This article reviews how evolutionary algorithms have been proposed and tested as a competitive alternative to address a number of issues related to neural network design.

  https://www.kdnuggets.com/2019/06/evolving-deep-neural-networks.html

• Overview of Different Approaches to Deploying Machine Learning Models in Production

  Learn the different methods for putting machine learning models into production, and to determine which method is best for which use case.

  https://www.kdnuggets.com/2019/06/approaches-deploying-machine-learning-production.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

• Boost Your Image Classification Model

  Check out this collection of tricks to improve the accuracy of your classifier.

  https://www.kdnuggets.com/2019/05/boost-your-image-classification-model.html

• XGBoost Algorithm: Long May She Reign

  In recent years, XGBoost algorithm has gained enormous popularity in academic as well as business world. We outline some of the reasons behind this incredible success.

  https://www.kdnuggets.com/2019/05/xgboost-algorithm.html

• How Optimization Works

  Optimization problems are naturally described in terms of costs - money, time, resources - rather than benefits. In math it's convenient to make all your problems look the same before you work out a solution, so that you can just solve it the one time.

  https://www.kdnuggets.com/2019/04/how-optimization-works.html

• How can quantum computing be useful for Machine Learning

  We investigate where quantum computing and machine learning could intersect, providing plenty of use cases, examples and technical analysis.

  https://www.kdnuggets.com/2019/04/quantum-computing-machine-learning.html

• Advice for New Data Scientists">Advice for New Data Scientists

  We provide advice for junior data scientists as they begin their career, with tips and commentary from a tech lead at Airbnb.

  https://www.kdnuggets.com/2019/04/advice-new-data-scientists.html

• Another 10 Free Must-See Courses for Machine Learning and Data Science">Another 10 Free Must-See Courses for Machine Learning and Data Science

  Check out another follow-up collection of free machine learning and data science courses to give you some spring study ideas.

  https://www.kdnuggets.com/2019/04/another-10-free-must-see-courses-machine-learning-data-science.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

• Another 10 Free Must-Read Books for Machine Learning and Data Science">Another 10 Free Must-Read Books for Machine Learning and Data Science

  Here's a third set of 10 free books for machine learning and data science. Have a look to see if something catches your eye, and don't forget to check the previous installments for reading material while you're here.

  https://www.kdnuggets.com/2019/03/another-10-free-must-read-books-for-machine-learning-and-data-science.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

• 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

• Data Scientist’s Dilemma: The Cold Start Problem – Ten Machine Learning Examples

  We present an array of examples showcasing the cold-start problems in data science where the algorithms and techniques of machine learning produce the good judgment in model progression toward the optimal solution.

  https://www.kdnuggets.com/2019/01/data-scientist-dilemma-cold-start-machine-learning.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

• 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

• A comprehensive list of Machine Learning Resources: Open Courses, Textbooks, Tutorials, Cheat Sheets and more

  A thorough collection of useful resources covering statistics, classic machine learning, deep learning, probability, reinforcement learning, and more.

  https://www.kdnuggets.com/2018/12/finlayson-machine-learning-resources.html

• Common mistakes when carrying out machine learning and data science">Common mistakes when carrying out machine learning and data science

  We examine typical mistakes in Data Science process, including wrong data visualization, incorrect processing of missing values, wrong transformation of categorical variables, and more. Learn what to avoid!

  https://www.kdnuggets.com/2018/12/common-mistakes-data-science.html

• Deep Learning Performance Cheat Sheet

  We outline a variety of simple and complex tricks that can help you boost your deep learning models accuracy, from basic optimization, to open source labeling software.

  https://www.kdnuggets.com/2018/11/deep-learning-performance-cheat-sheet.html

• Introduction to PyTorch for Deep Learning

  In this tutorial, you’ll get an introduction to deep learning using the PyTorch framework, and by its conclusion, you’ll be comfortable applying it to your deep learning models.

  https://www.kdnuggets.com/2018/11/introduction-pytorch-deep-learning.html

• Mastering the Learning Rate to Speed Up Deep Learning

  Figuring out the optimal set of hyperparameters can be one of the most time consuming portions of creating a machine learning model, and that’s particularly true in deep learning.

  https://www.kdnuggets.com/2018/11/mastering-learning-rate-speed-up-deep-learning.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

• Notes on Feature Preprocessing: The What, the Why, and the How

  This article covers a few important points related to the preprocessing of numeric data, focusing on the scaling of feature values, and the broad question of dealing with outliers.

  https://www.kdnuggets.com/2018/10/notes-feature-preprocessing-what-why-how.html

• The Intuitions Behind Bayesian Optimization with Gaussian Processes

  Bayesian Optimization adds a Bayesian methodology to the iterative optimizer paradigm by incorporating a prior model on the space of possible target functions. This article introduces the basic concepts and intuitions behind Bayesian Optimization with Gaussian Processes.

  https://www.kdnuggets.com/2018/10/intuitions-behind-bayesian-optimization-gaussian-processes.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

• 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

• 6 Steps To Write Any Machine Learning Algorithm From Scratch: Perceptron Case Study">6 Steps To Write Any Machine Learning Algorithm From Scratch: Perceptron Case Study

  Writing a machine learning algorithm from scratch is an extremely rewarding learning experience. We highlight 6 steps in this process.

  https://www.kdnuggets.com/2018/09/6-steps-write-machine-learning-algorithm.html

• An Introduction to t-SNE with Python Example

  In this post we’ll give an introduction to the exploratory and visualization t-SNE algorithm. t-SNE is a powerful dimension reduction and visualization technique used on high dimensional data.

  https://www.kdnuggets.com/2018/08/introduction-t-sne-python.html

• Unveiling Mathematics Behind XGBoost">Unveiling Mathematics Behind XGBoost

  Follow me till the end, and I assure you will atleast get a sense of what is happening underneath the revolutionary machine learning model.

  https://www.kdnuggets.com/2018/08/unveiling-mathematics-behind-xgboost.html

• Autoregressive Models in TensorFlow

  This article investigates autoregressive models in TensorFlow, including autoregressive time series and predictions with the actual observations.

  https://www.kdnuggets.com/2018/08/autoregressive-models-tensorflow.html

• Only Numpy: Implementing GANs and Adam Optimizer using Numpy">Only Numpy: Implementing GANs and Adam Optimizer using Numpy

  This post is an implementation of GANs and the Adam optimizer using only Python and Numpy, with minimal focus on the underlying maths involved.

  https://www.kdnuggets.com/2018/08/only-numpy-implementing-gans-adam-optimizer.html

• Selecting the Best Machine Learning Algorithm for Your Regression Problem

  This post should then serve as a great aid in selecting the best ML algorithm for you regression problem!

  https://www.kdnuggets.com/2018/08/selecting-best-machine-learning-algorithm-regression-problem.html

• 9 Reasons why your machine learning project will fail

  This article explains in detail some of the issues that you may face during your machine learning project.

  https://www.kdnuggets.com/2018/07/why-machine-learning-project-fail.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

• Taming LSTMs: Variable-sized mini-batches and why PyTorch is good for your health

  After reading this, you’ll be back to fantasies of you + PyTorch eloping into the sunset while your Recurrent Networks achieve new accuracies you’ve only read about on Arxiv.

  https://www.kdnuggets.com/2018/06/taming-lstms-variable-sized-mini-batches-pytorch.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

• Complete Guide to Build ConvNet HTTP-Based Application using TensorFlow and Flask RESTful Python API">Complete Guide to Build ConvNet HTTP-Based Application using TensorFlow and Flask RESTful Python API

  In this tutorial, a CNN is to be built, and trained and tested against the CIFAR10 dataset. To make the model remotely accessible, a Flask Web application is created using Python to receive an uploaded image and return its classification label using HTTP.

  https://www.kdnuggets.com/2018/05/complete-guide-convnet-tensorflow-flask-restful-python-api.html

• Implementing Deep Learning Methods and Feature Engineering for Text Data: The GloVe Model

  The GloVe model stands for Global Vectors which is an unsupervised learning model which can be used to obtain dense word vectors similar to Word2Vec.

  https://www.kdnuggets.com/2018/04/implementing-deep-learning-methods-feature-engineering-text-data-glove.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

• Ten Machine Learning Algorithms You Should Know to Become a Data Scientist">Ten Machine Learning Algorithms You Should Know to Become a Data Scientist

  It's important for data scientists to have a broad range of knowledge, keeping themselves updated with the latest trends. With that being said, we take a look at the top 10 machine learning algorithms every data scientist should know.

  https://www.kdnuggets.com/2018/04/10-machine-learning-algorithms-data-scientist.html

• Getting Started with PyTorch Part 1: Understanding How Automatic Differentiation Works

  PyTorch has emerged as a major contender in the race to be the king of deep learning frameworks. What makes it really luring is it’s dynamic computation graph paradigm.

  https://www.kdnuggets.com/2018/04/getting-started-pytorch-understanding-automatic-differentiation.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

• 5 Things You Need To Know About Data Science

  Here are 5 useful things to know about Data Science, including its relationship to BI, Data Mining, Predictive Analytics, and Machine Learning; Data Scientist job prospects; where to learn Data Science; and which algorithms/methods are used by Data Scientists

  https://www.kdnuggets.com/2018/02/5-things-about-data-science.html

• The AGI/Deep Learning Connection

  Also, deep learning would definitely prove to be an essential component to create truly intelligent machines but probably not enough alone.

  https://www.kdnuggets.com/2018/02/agi-deep-learning-connection.html

• Understanding Learning Rates and How It Improves Performance in Deep Learning

  Furthermore, the learning rate affects how quickly our model can converge to a local minima (aka arrive at the best accuracy). Thus getting it right from the get go would mean lesser time for us to train the model.

  https://www.kdnuggets.com/2018/02/understanding-learning-rates-improves-performance-deep-learning.html

• Deep Learning in H2O using R

  This article is about implementing Deep Learning (DL) using the H2O package in R. We start with a background on DL, followed by some features of H2O's DL framework, followed by an implementation using R.

  https://www.kdnuggets.com/2018/01/deep-learning-h2o-using-r.html

• Custom Optimizer in TensorFlow

  How to customize the optimizers to speed-up and improve the process of finding a (local) minimum of the loss function using TensorFlow.

  https://www.kdnuggets.com/2018/01/custom-optimizer-tensorflow.html

• Top Stories of 2017: 10 Free Must-Read Books for Machine Learning and Data Science; Python overtakes R, becomes the leader in Data Science, Machine Learning platforms

  Also Top 10 Machine Learning Algorithms for Beginners; 30 Essential Data Science, Machine Learning & Deep Learning Cheat Sheets.

  https://www.kdnuggets.com/2017/12/top-stories-2017.html

• Getting Started with TensorFlow: A Machine Learning Tutorial

  A complete and rigorous introduction to Tensorflow. Code along with this tutorial to get started with hands-on examples.

  https://www.kdnuggets.com/2017/12/getting-started-tensorflow.html

• The 10 Deep Learning Methods AI Practitioners Need to Apply

  Deep learning emerged from that decade’s explosive computational growth as a serious contender in the field, winning many important machine learning competitions. The interest has not cooled as of 2017; today, we see deep learning mentioned in every corner of machine learning.

  https://www.kdnuggets.com/2017/12/10-deep-learning-methods-ai-practitioners-need-apply.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

• Understanding Objective Functions in Neural Networks

  This blog post is targeted towards people who have experience with machine learning, and want to get a better intuition on the different objective functions used to train neural networks.

  https://www.kdnuggets.com/2017/11/understanding-objective-functions-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

• Top 10 Videos on Deep Learning in Python">Top 10 Videos on Deep Learning in Python

  Playlists, individual tutorials (not part of a playlist) and online courses on Deep Learning (DL) in Python using the Keras, Theano, TensorFlow and PyTorch libraries. Assumes no prior knowledge. These videos cover all skill levels and time constraints!

  https://www.kdnuggets.com/2017/11/top-10-videos-deep-learning-python.html

• Machine Learning Algorithms: Which One to Choose for Your Problem">Machine Learning Algorithms: Which One to Choose for Your Problem

  This article will try to explain basic concepts and give some intuition of using different kinds of machine learning algorithms in different tasks. At the end of the article, you’ll find the structured overview of the main features of described algorithms.

  https://www.kdnuggets.com/2017/11/machine-learning-algorithms-choose-your-problem.html

• TensorFlow: What Parameters to Optimize?

  Learning TensorFlow Core API, which is the lowest level API in TensorFlow, is a very good step for starting learning TensorFlow because it let you understand the kernel of the library. Here is a very simple example of TensorFlow Core API in which we create and train a linear regression model.

  https://www.kdnuggets.com/2017/11/tensorflow-parameters-optimize.html

• Want to know how Deep Learning works? Here’s a quick guide for everyone">Want to know how Deep Learning works? Here’s a quick guide for everyone

  Once you’ve read this article, you will understand the basics of AI and ML. More importantly, you will understand how Deep Learning, the most popular type of ML, works.

  https://www.kdnuggets.com/2017/11/deep-learning-works-quick-guide-everyone.html

• Neural Networks, Step 1: Where to Begin with Neural Nets & Deep Learning

  This is a short post for beginners learning neural networks, covering several essential neural networks concepts.

  https://www.kdnuggets.com/2017/10/neural-networks-step-1.html

• XGBoost: A Concise Technical Overview">XGBoost: A Concise Technical Overview

  Interested in learning the concepts behind XGBoost, rather than just using it as a black box? Or, are you looking for a concise introduction to XGBoost? Then, this article is for you. Includes a Python implementation and links to other basic Python and R codes as well.

  https://www.kdnuggets.com/2017/10/xgboost-concise-technical-overview.html

• TensorFlow: Building Feed-Forward Neural Networks Step-by-Step">TensorFlow: Building Feed-Forward Neural Networks Step-by-Step

  This article will take you through all steps required to build a simple feed-forward neural network in TensorFlow by explaining each step in details.

  https://www.kdnuggets.com/2017/10/tensorflow-building-feed-forward-neural-networks-step-by-step.html

• How LinkedIn Makes Personalized Recommendations via Photon-ML Machine Learning tool">How LinkedIn Makes Personalized Recommendations via Photon-ML Machine Learning tool

  In this article we focus on the personalization aspect of model building and explain the modeling principle as well as how to implement Photon-ML so that it can scale to hundreds of millions of users.

  https://www.kdnuggets.com/2017/10/linkedin-personalized-recommendations-photon-ml.html

• How I started with learning AI in the last 2 months">How I started with learning AI in the last 2 months

  The relevance of a full stack developer will not be enough in the changing scenario of things. In the next two years, full stack will not be full stack without AI skills.

  https://www.kdnuggets.com/2017/10/how-started-learning-ai.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

• XGBoost, a Top Machine Learning Method on Kaggle, Explained">XGBoost, a Top Machine Learning Method on Kaggle, Explained

  Looking to boost your machine learning competitions score? Here’s a brief summary and introduction to a powerful and popular tool among Kagglers, XGBoost.

  https://www.kdnuggets.com/2017/10/xgboost-top-machine-learning-method-kaggle-explained.html

• 5 Ways to Get Started with Reinforcement Learning

  We give an accessible overview of reinforcement learning, including Deep Q Learning, and provide useful links for implementing RL.

  https://www.kdnuggets.com/2017/09/5-ways-get-started-reinforcement-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

• 42 Steps to Mastering Data Science">42 Steps to Mastering Data Science

  This post is a collection of 6 separate posts of 7 steps a piece, each for mastering and better understanding a particular data science topic, with topics ranging from data preparation, to machine learning, to SQL databases, to NoSQL and beyond.

  https://www.kdnuggets.com/2017/08/42-steps-mastering-data-science.html

• 37 Reasons why your Neural Network is not working">37 Reasons why your Neural Network is not working

  Over the course of many debugging sessions, I’ve compiled my experience along with the best ideas around in this handy list. I hope they would be useful to you.

  https://www.kdnuggets.com/2017/08/37-reasons-neural-network-not-working.html

• Deep Learning and Neural Networks Primer: Basic Concepts for Beginners

  This is a collection of introductory posts which present a basic overview of neural networks and deep learning. Start by learning some key terminology and gaining an understanding through some curated resources. Then look at summarized important research in the field before looking at a pair of concise case studies.

  https://www.kdnuggets.com/2017/08/deep-learning-neural-networks-primer-basic-concepts-beginners.html

• First Steps of Learning Deep Learning: Image Classification in Keras

  Whether you want to start learning deep learning for you career, to have a nice adventure (e.g. with detecting huggable objects) or to get insight into machines before they take over, this post is for you!

  https://www.kdnuggets.com/2017/08/first-steps-learning-deep-learning-image-classification-keras.html

• Machine Learning Exercises in Python: An Introductory Tutorial Series">Machine Learning Exercises in Python: An Introductory Tutorial Series

  This post presents a summary of a series of tutorials covering the exercises from Andrew Ng's machine learning class on Coursera. Instead of implementing the exercises in Octave, the author has opted to do so in Python, and provide commentary along the way.

  https://www.kdnuggets.com/2017/07/machine-learning-exercises-python-introductory-tutorial-series.html

• Introduction to Neural Networks, Advantages and Applications">Introduction to Neural Networks, Advantages and Applications

  Artificial Neural Network (ANN) algorithm mimic the human brain to process information. Here we explain how human brain and ANN works.

  https://www.kdnuggets.com/2017/07/introduction-neural-networks-advantages-applications.html

• Optimization in Machine Learning: Robust or global minimum?

  Here we discuss how convex problems are solved and optimised in machine learning/deep learning.

  https://www.kdnuggets.com/2017/06/robust-global-minimum.html

• Understanding Deep Learning Requires Re-thinking Generalization">Understanding Deep Learning Requires Re-thinking Generalization

  What is it that distinguishes neural networks that generalize well from those that don’t? A satisfying answer to this question would not only help to make neural networks more interpretable, but it might also lead to more principled and reliable model architecture design.

  https://www.kdnuggets.com/2017/06/understanding-deep-learning-rethinking-generalization.html

• How Feature Engineering Can Help You Do Well in a Kaggle Competition – Part I

  As I scroll through the leaderboard page, I found my name in the 19th position, which was the top 2% from nearly 1,000 competitors. Not bad for the first Kaggle competition I had decided to put a real effort in!

  https://www.kdnuggets.com/2017/06/feature-engineering-help-kaggle-competition-1.html

• Why Does Deep Learning Not Have a Local Minimum?

  "As I understand, the chance of having a derivative zero in each of the thousands of direction is low. Is there some other reason besides this?"

  https://www.kdnuggets.com/2017/06/deep-learning-local-minimum.html

• Top Stories, May 22-28: Analytics, Data Science, Machine Learning Software Poll Results; Machine Learning Crash Course

  New Leader, Trends, and Surprises in Analytics, Data Science, Machine Learning Software Poll; Machine Learning Crash Course: Part 1; Text Mining 101: Mining Information From A Resume; Data science platforms are on the rise and IBM is leading the way; An Introduction to the MXNet Python API

  https://www.kdnuggets.com/2017/05/top-news-week-0522-0528.html

• Machine Learning Crash Course: Part 1

  This post, the first in a series of ML tutorials, aims to make machine learning accessible to anyone willing to learn. We’ve designed it to give you a solid understanding of how ML algorithms work as well as provide you the knowledge to harness it in your projects.

  https://www.kdnuggets.com/2017/05/machine-learning-crash-course-part-1.html

• Using Deep Learning To Extract Knowledge From Job Descriptions">Using Deep Learning To Extract Knowledge From Job Descriptions

  We present a deep learning approach to extract knowledge from a large amount of data from the recruitment space. A learning to rank approach is followed to train a convolutional neural network to generate job title and job description embeddings.

  https://www.kdnuggets.com/2017/05/deep-learning-extract-knowledge-job-descriptions.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

• Deep Learning – Past, Present, and Future">Deep Learning – Past, Present, and Future

  There is a lot of buzz around deep learning technology. First developed in the 1940s, deep learning was meant to simulate neural networks found in brains, but in the last decade 3 key developments have unleashed its potential.

  https://www.kdnuggets.com/2017/05/deep-learning-big-deal.html

• 10 Free Must-Read Books for Machine Learning and Data Science">10 Free Must-Read Books for Machine Learning and Data Science

  Spring. Rejuvenation. Rebirth. Everything’s blooming. And, of course, people want free ebooks. With that in mind, here's a list of 10 free machine learning and data science titles to get your spring reading started right.

  https://www.kdnuggets.com/2017/04/10-free-must-read-books-machine-learning-data-science.html

• Deep Learning Research Review: Natural Language Processing">Deep Learning Research Review: Natural Language Processing

  This edition of Deep Learning Research Review explains recent research papers in Natural Language Processing (NLP). If you don't have the time to read the top papers yourself, or need an overview of NLP with Deep Learning, this post is for you.

  https://www.kdnuggets.com/2017/01/deep-learning-review-natural-language-processing.html

• The Current State of Automated Machine Learning

  What is automated machine learning (AutoML)? Why do we need it? What are some of the AutoML tools that are available? What does its future hold? Read this article for answers to these and other AutoML questions.

  https://www.kdnuggets.com/2017/01/current-state-automated-machine-learning.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

• The hard thing about deep learning">The hard thing about deep learning

  It’s easy to optimize simple neural networks, let’s say single layer perceptron. But, as network becomes deeper, the optmization problem becomes crucial. This article discusses about such optimization problems with deep neural networks.

  https://www.kdnuggets.com/2016/12/hard-thing-about-deep-learning.html

• Deep Learning Research Review: Reinforcement Learning

  This edition of Deep Learning Research Review explains recent research papers in Reinforcement Learning (RL). If you don't have the time to read the top papers yourself, or need an overview of RL in general, this post has you covered.

  https://www.kdnuggets.com/2016/11/deep-learning-research-review-reinforcement-learning.html

• Implementing a CNN for Human Activity Recognition in Tensorflow

  In this post, we will see how to employ Convolutional Neural Network (CNN) for HAR, that will learn complex features automatically from the raw accelerometer signal to differentiate between different activities of daily life.

  https://www.kdnuggets.com/2016/11/implementing-cnn-human-activity-recognition-tensorflow.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 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

• Automated Data Science & Machine Learning: An Interview with the Auto-sklearn Team">Automated Data Science & Machine Learning: An Interview with the Auto-sklearn Team

  This is an interview with the authors of the recent winning KDnuggets Automated Data Science and Machine Learning blog contest entry, which provided an overview of the Auto-sklearn project. Learn more about the authors, the project, and automated data science.

  https://www.kdnuggets.com/2016/10/interview-auto-sklearn-automated-data-science-machine-learning-team.html

• 9 Key Deep Learning Papers, Explained">9 Key Deep Learning Papers, Explained

  If you are interested in understanding the current state of deep learning, this post outlines and thoroughly summarizes 9 of the most influential contemporary papers in the field.

  https://www.kdnuggets.com/2016/09/9-key-deep-learning-papers-explained.html

• Urban Sound Classification with Neural Networks in Tensorflow

  This post discuss techniques of feature extraction from sound in Python using open source library Librosa and implements a Neural Network in Tensorflow to categories urban sounds, including car horns, children playing, dogs bark, and more.

  https://www.kdnuggets.com/2016/09/urban-sound-classification-neural-networks-tensorflow.html