# Tag: TensorFlow (141)

**Top /r/MachineLearning posts, August 2018: Everybody Dance Now; Stanford class Machine Learning cheat sheets; Academic Torrents for sharing enormous datasets**- Sep 15, 2018.

A range of interesting posts from the /r/MachineLearning Reddit group for the month of August, including: Everybody Dance Now; Stanford class Machine Learning cheat sheets; Academic Torrents; Getting Alexa to respond to sign language using TensorFlow; PyCharm IDE.**Ultimate Guide to Getting Started with TensorFlow**- Sep 6, 2018.

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.**9 Things You Should Know About TensorFlow**- Aug 22, 2018.

A summary of the key points from the Google Cloud Next in San Francisco, "What’s New with TensorFlow?", including neural networks, TensorFlow Lite, data pipelines and more.**Machine Learning with TensorFlow**- Aug 16, 2018.

In this on-demand webinar, you’ll get a general introduction to working with Tensorflow and its surrounding ecosystem, general problem classes, where you can get big acceleration, and why you should be running on a CPU.**Setting up your AI Dev Environment in 5 Minutes**- Aug 13, 2018.

Whether you're a novice data science enthusiast setting up TensorFlow for the first time, or a seasoned AI engineer working with terabytes of data, getting your libraries, packages, and frameworks installed is always a struggle. Learn how datmo, an open source python package, helps you get started in minutes.**Autoregressive Models in TensorFlow**- Aug 6, 2018.

This article investigates autoregressive models in TensorFlow, including autoregressive time series and predictions with the actual observations.**Ready your Skills for a Cloud-First World with Google**- Jul 20, 2018.

The Machine Learning with TensorFlow on Google Cloud Platform Specialization on Coursera will help you jumpstart your career, includes hands-on labs, and takes you from a strategic overview to practical skills in building real-world, accurate ML models.**Top KDnuggets tweets, Jul 11-17: Foundations of Machine Learning – A Bloomberg course; The 5 Clustering Algorithms Data Scientists Need to Know**- Jul 18, 2018.

Also: Bayesian Machine Learning, Explained; Is Google Tensorflow Object Detection API the Easiest Way to Implement Image Recognition?; Data Science of Variable Selection: A Review; 7 Steps to Understanding Deep Learning**Top KDnuggets tweets, Jul 4-10: Fantastic notes on the freely available @fastdotai machine learning course**- Jul 11, 2018.

Also: Analyze a Soccer (Football) Game Using #Tensorflow Object Detection; 18 Inspiring Women In AI, Big Data, Data Science, Machine Learning; Timsort - the fastest #sorting #algorithm you've never heard of.**Analyze a Soccer (Football) Game Using Tensorflow Object Detection and OpenCV**- Jul 10, 2018.

For the data scientist within you let's use this opportunity to do some analysis on soccer clips. With the use of deep learning and opencv we can extract interesting insights from video clips**Inside the Mind of a Neural Network with Interactive Code in Tensorflow**- Jun 29, 2018.

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.**[ebook] Apache Spark™ Under the Hood**- Jun 27, 2018.

Learn how to install and run Spark yourself; A summary of Spark core architecture and concepts; Spark powerful language APIs and how you can use them.**Top 20 Python Libraries for Data Science in 2018**- Jun 27, 2018.

Our selection actually contains more than 20 libraries, as some of them are alternatives to each other and solve the same problem. Therefore we have grouped them as it's difficult to distinguish one particular leader at the moment.**The 6 components of Open-Source Data Science/ Machine Learning Ecosystem; Did Python declare victory over R?**- Jun 6, 2018.

We find 6 tools form the modern open source Data Science / Machine Learning ecosystem; examine whether Python declared victory over R; and review which tools are most associated with Deep Learning and Big Data.**Deep Learning With Apache Spark: Part 2**- May 23, 2018.

In this article I’ll continue the discussion on Deep Learning with Apache Spark. I will focus entirely on the DL pipelines library and how to use it from scratch.**Python eats away at R: Top Software for Analytics, Data Science, Machine Learning in 2018: Trends and Analysis**- May 22, 2018.

Python continues to eat away at R, RapidMiner gains, SQL is steady, Tensorflow advances pulling along Keras, Hadoop drops, Data Science platforms consolidate, and more.**Top Stories, May 14-20: Data Science vs Machine Learning vs Data Analytics vs Business Analytics; Implement a YOLO Object Detector from Scratch in PyTorch**- May 21, 2018.

Also: An Introduction to Deep Learning for Tabular Data; 9 Must-have skills you need to become a Data Scientist, updated; GANs in TensorFlow from the Command Line: Creating Your First GitHub Project; Complete Guide to Build ConvNet HTTP-Based Application**GANs in TensorFlow from the Command Line: Creating Your First GitHub Project**- May 16, 2018.

In this article I will present the steps to create your first GitHub Project. I will use as an example Generative Adversarial Networks.**Complete Guide to Build ConvNet HTTP-Based Application using TensorFlow and Flask RESTful Python API**- May 15, 2018.

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.**How I Used CNNs and Tensorflow and Lost a Silver Medal in Kaggle Challenge**- May 8, 2018.

I joined the competition a month before it ended, eager to explore how to use Deep Natural Language Processing (NLP) techniques for this problem. Then came the deception. And I will tell you how I lost my silver medal in that competition.**Ultra-compact workstation for top deep learning frameworks**- Apr 27, 2018.

For workstation development platforms purpose-built for Tensorflow, PyTorch, Caffe2, MXNet, and other DL frameworks, the solution is BOXX. We're bringing deep learning to your deskside with the all-new APEXX W3!**Top 16 Open Source Deep Learning Libraries and Platforms**- Apr 24, 2018.

We bring to you the top 16 open source deep learning libraries and platforms. TensorFlow is out in front as the undisputed number one, with Keras and Caffe completing the top three.**Are High Level APIs Dumbing Down Machine Learning?**- Apr 16, 2018.

Libraries like Keras simplify the construction of neural networks, but are they impeding on practitioners full understanding? Or are they simply useful (and inevitable) abstractions?**Top 20 Deep Learning Papers, 2018 Edition**- Apr 3, 2018.

Deep Learning is constantly evolving at a fast pace. New techniques, tools and implementations are changing the field of Machine Learning and bringing excellent results.**A “Weird” Introduction to Deep Learning**- Mar 30, 2018.

There are amazing introductions, courses and blog posts on Deep Learning. But this is a different kind of introduction.**Using Tensorflow Object Detection to do Pixel Wise Classification**- Mar 29, 2018.

Tensorflow recently added new functionality and now we can extend the API to determine pixel by pixel location of objects of interest. So when would we need this extra granularity?**Comparing Deep Learning Frameworks: A Rosetta Stone Approach**- Mar 26, 2018.

A Rosetta Stone of deep-learning frameworks has been created to allow data-scientists to easily leverage their expertise from one framework to another.**KDnuggets™ News 18:n10, Mar 7: Functional Programming in Python; Surviving Your Data Science Interview; Easy Image Recognition with Google Tensorflow**- Mar 7, 2018.

Also: Data Science in Fashion; Time Series for Dummies - The 3 Step Process; Deep Misconceptions About Deep Learning; Data Science for Javascript Developers**Is Google Tensorflow Object Detection API the Easiest Way to Implement Image Recognition?**- Mar 1, 2018.

There are many different ways to do image recognition. Google recently released a new Tensorflow Object Detection API to give computer vision everywhere a boost.**Top 20 Python AI and Machine Learning Open Source Projects**- Feb 20, 2018.

We update the top AI and Machine Learning projects in Python. Tensorflow has moved to the first place with triple-digit growth in contributors. Scikit-learn dropped to 2nd place, but still has a very large base of contributors.**Deep Learning Development with Google Colab, TensorFlow, Keras & PyTorch**- Feb 20, 2018.

Now you can develop deep learning applications with Google Colaboratory - on the free Tesla K80 GPU - using Keras, Tensorflow and PyTorch.**Building a Toy Detector with Tensorflow Object Detection API**- Feb 13, 2018.

This project is second phase of my popular project - Is Google Tensorflow Object Detection API the easiest way to implement image recognition? Here I extend the API to train on a new object that is not part of the COCO dataset.**3 Essential Google Colaboratory Tips & Tricks**- Feb 12, 2018.

Google Colaboratory is a promising machine learning research platform. Here are 3 tips to simplify its usage and facilitate using a GPU, installing libraries, and uploading data files.**KDnuggets™ News 18:n04, Jan 24: TensorFlow vs XGBoost; Machine Learning Pipelines in Python; Semi-Supervised Machine Learning**- Jan 24, 2018.

Gradient Boosting in TensorFlow vs XGBoost; Managing Machine Learning Workflows with Scikit-learn Pipelines Part 2; Using Genetic Algorithm for Optimizing Recurrent Neural Networks; The Value of Semi-Supervised Machine Learning; Comparing Machine Learning as a Service: Amazon, Microsoft Azure, Google Cloud AI**Gradient Boosting in TensorFlow vs XGBoost**- Jan 18, 2018.

For many Kaggle-style data mining problems, XGBoost has been the go-to solution since its release in 2016. It's probably as close to an out-of-the-box machine learning algorithm as you can get today.**Top KDnuggets tweets, Jan 10-16: The Art of Learning #DataScience; Gradient Boosting in #TensorFlow vs XGBoost**- Jan 17, 2018.

Also Japanese scientists just used #AI #DeepLearning to read minds and it's amazing; Using #DeepLearning to Solve Real World Problems.**A Day in the Life of an AI Developer**- Jan 16, 2018.

This is the narrative of a typical AI Sunday, where I decided to look at building a sequence to sequence (seq2seq) model based chatbot using some already available sample code and data from the Cornell movie database.**Custom Optimizer in TensorFlow**- Jan 8, 2018.

How to customize the optimizers to speed-up and improve the process of finding a (local) minimum of the loss function using TensorFlow.**Top KDnuggets tweets, Dec 27 – Jan 02: 10 Free Must-Read Books for #MachineLearning and #DataScience**- Jan 3, 2018.

Also #TensorFlow: A proposal of good practices for files, folders and models; Creating REST API for #TensorFlow models; The Most Popular Language For #MachineLearning and #DataScience Is ...**Deep Learning Made Easy with Deep Cognition**- Dec 21, 2017.

So normally we do Deep Learning programming, and learning new APIs, some harder than others, some are really easy an expressive like Keras, but how about a visual API to create and deploy Deep Learning solutions with the click of a button? This is the promise of Deep Cognition.**Top KDnuggets tweets, Dec 13-19: The Art of Learning Data Science; Data Science, ML Main Developments, Key Trends**- Dec 20, 2017.

The Art of Learning #DataScience; How to Generate FiveThirtyEight Graphs in #Python; #TensorFlow for Short-Term Stocks Prediction; 15 Mathematics MOOCs for #DataScience.**Getting Started with TensorFlow: A Machine Learning Tutorial**- Dec 19, 2017.

A complete and rigorous introduction to Tensorflow. Code along with this tutorial to get started with hands-on examples.**TensorFlow for Short-Term Stocks Prediction**- Dec 12, 2017.

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.**Exploring Recurrent Neural Networks**- Dec 1, 2017.

We explore recurrent neural networks, starting with the basics, using a motivating weather modeling problem, and implement and train an RNN in TensorFlow.**InfoGAN - Generative Adversarial Networks Part III**- Nov 30, 2017.

In this third part of this series of posts the contributions of InfoGAN will be explored, which apply concepts from Information Theory to transform some of the noise terms into latent codes that have systematic, predictable effects on the outcome.**Understanding Deep Convolutional Neural Networks with a practical use-case in Tensorflow and Keras**- Nov 29, 2017.

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.**How To Unit Test Machine Learning Code**- Nov 28, 2017.

One of the main principles I learned during my time at Google Brain was that unit tests can make or break your algorithm and can save you weeks of debugging and training time.**Implementing Enterprise AI course using TensorFlow and Keras**- Nov 27, 2017.

The course is for developers and architects who want to transition their career to Enterprise AI, but also has strategic (non-coding) version. The course starts in Jan 2018 and will take 3 months for the content and up to 3 months for the team project.**Using TensorFlow for Predictive Analytics with Linear Regression**- Nov 21, 2017.

This post presents a powerful and simple example of how to use TensorFlow to perform a Linear Regression. check out the code for your own experiments!**Top 10 Videos on Deep Learning in Python**- Nov 17, 2017.

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!**TensorFlow: What Parameters to Optimize?**- Nov 9, 2017.

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.**Choosing an Open Source Machine Learning Library: TensorFlow, Theano, Torch, scikit-learn, Caffe**- Nov 8, 2017.

Open Source is the heart of innovation and rapid evolution of technologies, these days. Here we discuss how to choose open source machine learning tools for different use cases.**Ranking Popular Deep Learning Libraries for Data Science**- Oct 23, 2017.

We rank 23 open-source deep learning libraries that are useful for Data Science. The ranking is based on equally weighing its three components: Github and Stack Overflow activity, as well as Google search results.**TensorFlow: Building Feed-Forward Neural Networks Step-by-Step**- Oct 23, 2017.

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.**Data Science –The need for a Systems Engineering approach**- Oct 5, 2017.

We need a greater emphasis on the Systems Engineering aspects of Data Science. I am exploring these ideas as part of my course "Data Science for Internet of Things" at the University of Oxford.**Top KDnuggets tweets, Sep 27 – Oct 03: Introduction to #Blockchains & What It Means to #BigData; 7 More Steps to Mastering #MachineLearning With #Python**- Oct 4, 2017.

Also Jupyter Notebooks are Breathtakingly Featureless - Use Jupyter Lab; The 4 Types of Data #Analytics; Aspiring Data Scientists! Learn the basics with these 7 books.**Neural Networks: Innumerable Architectures, One Fundamental Idea**- Oct 4, 2017.

At the end of this post, you’ll be able to implement a neural network to identify handwritten digits using the MNIST dataset and have a rough time idea about how to build your own neural networks.**Data Science, AI & Deep Learning Conference – 16 November 2017, London**- Oct 2, 2017.

This conference brings together a range of expert practitioners to explore and discuss the new era of AI, Machine Learning and Deep Learning. Participants gain real insights on how to exploit these technological advances for themselves and their organisations in an increasingly ‘data-driven world’.**GPU-accelerated, In-database Analytics for Operationalizing AI**- Oct 2, 2017.

This blog explores how the massive parallel processing power of the GPU is able to unify the entire AI pipeline on a single platform, and how this is both necessary and sufficient for overcoming the challenges to operationalizing AI.**Tensorflow Tutorial, Part 2 – Getting Started**- Sep 28, 2017.

This tutorial will lay a solid foundation to your understanding of Tensorflow, the leading Deep Learning platform. The second part shows how to get started, install, and build a small test case.**Top KDnuggets tweets, Sep 20-26: 30 Essential #DataScience, #MachineLearning & #DeepLearning Cheat Sheets**- Sep 27, 2017.

Also: Older news, but still inspiring: #Harvard Thinks It Found the Next #Einstein; Putting #MachineLearning in Production - how to guide.**The Search for the Fastest Keras Deep Learning Backend**- Sep 26, 2017.

This is an overview of the performance comparison for the popular Deep Learning frameworks supported by Keras – TensorFlow, CNTK, MXNet and Theano.**Tensorflow Tutorial: Part 1 – Introduction**- Sep 21, 2017.

Everyone is talking about Tensorflow these days. In this multipart series, we explain Tensorflow in detail, including it’s architecture and industry applications.**Advancing Analytics, Melbourne, October 18 – Early bird extended**- Sep 13, 2017.

IAPA National Conference in Melbourne on 18 October will be a fantastic day with another five speakers just announced. Early bird rates have been extended to Sep 20 or become IAPA member and save even more.**ACM Data Science Camp 2017, Oct 14, Silicon Valley**- Sep 6, 2017.

Data Science Camp is SF Bay ACM annual event combining sessions, keynote, and optional tutorial - an excellent opportunity to learn and connect with others, at very low cost.**PyTorch or TensorFlow?**- Aug 29, 2017.

PyTorch is better for rapid prototyping in research, for hobbyists and for small scale projects. TensorFlow is better for large-scale deployments, especially when cross-platform and embedded deployment is a consideration.**Top KDnuggets tweets, Aug 09-15: #Tensorflow tutorials and best practices; Top Influencers for #DataScience**- Aug 16, 2017.

Also 37 Reasons why your #NeuralNetwork is not working; Making Predictive Model Robust: Holdout vs Cross-Validation.**Top KDnuggets tweets, Jul 26 – Aug 01: 37 Reasons why your #NeuralNetwork is not working; Machine Learning Exercises in Python**- Aug 2, 2017.

Also Hill criteria for #causality vs #correlation via #xkcd cartoons; #MachineLearning Workflows in #Python from Scratch Part 2: k-means Clustering**KDnuggets™ News 17:n26, Jul 12: Applying Deep Learning to Real-world Problems; New Poll: Will society be better from increased automation, AI?**- Jul 12, 2017.

Also Text Clustering: Get quick insights from Unstructured Data; Using the TensorFlow API: An Introductory Tutorial Series; Deep Learning Zero to One: 5 Awe-Inspiring Demos with Code for Beginners, part 2**Top /r/MachineLearning Posts, June: NumPy Gets Funding; ML Cheat Sheets For All; Hot Dog or Not?!?**- Jul 3, 2017.

NumPy receives first ever funding, thanks to Moore Foundation; Cheat Sheets for deep learning and machine learning; How HBO’s Silicon Valley built “Not Hotdog” with mobile TensorFlow & Keras; Andrej Karpathy leaves OpenAI for Tesla; Machine, a machine learning IDE**Using the TensorFlow API: An Introductory Tutorial Series**- Jun 28, 2017.

This post summarizes and links to a great multi-part tutorial series on learning the TensorFlow API for building a variety of neural networks, as well as a bonus tutorial on backpropagation from the beginning.**Deep Learning Zero to One: 5 Awe-Inspiring Demos with Code for Beginners**- Jun 26, 2017.

Here are deep learning demos and examples you can just download and run. No Math. No Theory. No Books.**The world’s first protein database for Machine Learning and AI**- Jun 22, 2017.

dSPP is the world first interactive database of proteins for AI and Machine Learning, and is fully integrated with Keras and Tensorflow. You can access the database at peptone.io/dspp**AI for fintech course – Early discounts and limited places**- Jun 20, 2017.

This new course with limited places will focus on AI design (product, development and Data) for the fintech industry and will be taught online by Ajit Jaokar and Jakob Aungiers.**Top KDnuggets tweets, Jun 07-13: Is Regression Analysis Really Machine Learning?**- Jun 14, 2017.

Machine Learning in Real Life: Tales from the Trenches; Is Regression Analysis Really Machine Learning?; Implementing Your Own k-Nearest Neighbour Algorithm Using Python; Building Simple Neural Networks - TensorFlow for Hackers.**Deep Learning: TensorFlow Programming via XML and PMML**- Jun 9, 2017.

In this approach, problem dataset and its Neural network are specified in a PMML like XML file. Then it is used to populate the TensorFlow graph, which, in turn run to get the results.**Deep Learning 101: Demystifying Tensors**- Jun 2, 2017.

Many deep-learning systems available today are based on tensor algebra, but tensor algebra isn’t tied to deep-learning. It isn’t hard to get started with tensor abuse but can be hard to stop.**New Leader, Trends, and Surprises in Analytics, Data Science, Machine Learning Software Poll**- May 22, 2017.

Python caught up with R and (barely) overtook it; Deep Learning usage surges to 32%; RapidMiner remains top general Data Science platform; Five languages of Data Science.

**MLTrain: transitioning academic theory to practice**- May 9, 2017.

Learn how to master Machine Learning by understanding the theory behind. MLTrain also teaches the concepts and helpful tricks of key systems like TensorFlow and how to code machine learning algorithms using it.**Top /r/MachineLearning Posts, April: Why Momentum Really Works; Machine Learning with Scikit-Learn & TensorFlow**- May 5, 2017.

Why Momentum Really Works; O'Reilly's Hands-On Machine Learning with Scikit-Learn and TensorFlow; Implemented BEGAN and saw a cute face at iteration 168k; Self-driving car course; Exploring the mysteries of Go; DeepMind Solves AGI**KDnuggets™ News 17:n17, May 3: Learn Machine Learning… in 10 Days?!? Gradient Descent, Simplified**- May 3, 2017.

How to Learn Machine Learning in 10 Days; Keep it simple! How to understand Gradient Descent algorithm; The Guerrilla Guide to Machine Learning with Python; What Data You Analyzed - KDnuggets Poll Results and Trends; Cartoon: Machine Learning - What They Think I Do**How Not To Program the TensorFlow Graph**- May 1, 2017.

Using TensorFlow from Python is like using Python to program another computer. Being thoughtful about the graphs you construct can help you avoid confusion and costly performance problems.**How to Build a Recurrent Neural Network in TensorFlow**- Apr 26, 2017.

This is a no-nonsense overview of implementing a recurrent neural network (RNN) in TensorFlow. Both theory and practice are covered concisely, and the end result is running TensorFlow RNN code.**Getting Started with Deep Learning**- Mar 24, 2017.

This post approaches getting started with deep learning from a framework perspective. Gain a quick overview and comparison of available tools for implementing neural networks to help choose what's right for you.**Top /r/MachineLearning Posts, February: Oxford Deep NLP Course; Data Visualization for Scikit-learn Results**- Mar 6, 2017.

Oxford Deep NLP Course; scikit-plot: Data Visualization for Scikit-learn Results; Machine Learning at Berkeley's ML Crash Course: Neural Networks; Predicting parking difficulty with machine learning; TensorFlow 1.0 Release**An Overview of Python Deep Learning Frameworks**- Feb 27, 2017.

Read this concise overview of leading Python deep learning frameworks, including Theano, Lasagne, Blocks, TensorFlow, Keras, MXNet, and PyTorch.**The Gentlest Introduction to Tensorflow – Part 4**- Feb 22, 2017.

This post is the fourth entry in a series dedicated to introducing newcomers to TensorFlow in the gentlest possible manner, and focuses on logistic regression for classifying the digits of 0-9.**The Gentlest Introduction to Tensorflow – Part 3**- Feb 21, 2017.

This post is the third entry in a series dedicated to introducing newcomers to TensorFlow in the gentlest possible manner. This entry progresses to multi-feature linear regression.**Top /r/MachineLearning Posts, January: TensorFlow Updates; AlphaGo in the Wild; Self-Driving Mario Kart**- Feb 7, 2017.

TensorFlow 1.0.0-alpha; Unknown bot repeatedly beats top Go players online - so far it's undefeated; TensorKart: self-driving MarioKart with TensorFlow; GTA V integration into Universe is now open-source; Keras will be added to core TensorFlow at Google**Top KDnuggets tweets, Jan 25-31: Python implementations of Andrew Ng #MachineLearning MOOC exercises**- Feb 1, 2017.

#Python implementations of Andrew Ng #MachineLearning MOOC exercises; This repository contains the entire #Python #DataScience Handbook; What are the best #visualizations of #MachineLearning algorithms? Learn #TensorFlow and #DeepLearning, without a PhD.**Top /r/MachineLearning Posts, 2016: Google Brain AMA; Google Machine Learning Recipes; StarCraft II AI Research Environment**- Jan 11, 2017.

Google Brain AMA; Google Machine Learning Recipes; StarCraft II AI Research Environment; Huggable Image Classifier; xkcd: Linear Regression; AlphaGO WINS!; TensorFlow Fizzbuzz**Top KDnuggets tweets, Dec 14-20: False positives versus false negatives: Best explanation ever**- Dec 21, 2016.

Also #MachineLearning, #AI experts: Main Developments 2016, Key Trends 2017; Official code repository for #MachineLearning with #TensorFlow book; Top 10 Essential Books for the #Data Enthusiast.**What we can learn from AI mistakes**- Dec 19, 2016.

Because of recent innovations and research in AI, we have seen AI performing best in some very important tasks and even worst in even simple tasks. So the question is, Why is it that AI can look so brilliant and so stupid at the same time?**Predictions for Deep Learning in 2017**- Dec 19, 2016.

The first hugely successful consumer application of deep learning will come to market, a dominant open-source deep-learning tool and library will take the developer community by storm, and more Deep Learning predictions.**New Book: TensorFlow for Machine Intelligence – KDnuggets Holiday Offer**- Dec 12, 2016.

TensorFlow for Machine Intelligence is a hands-on introduction to learning algorithms and the "TensorFlow book for humans." For a limited holiday special, KDnuggets readers get a 40% discount, available here.**Implementing a CNN for Human Activity Recognition in Tensorflow**- Nov 21, 2016.

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.**Introduction to Trainspotting: Computer Vision, Caltrain, and Predictive Analytics**- Nov 1, 2016.

We previously analyzed delays using Caltrain’s real-time API to improve arrival predictions, and we have modeled the sounds of passing trains to tell them apart. In this post we’ll start looking at the nuts and bolts of making our Caltrain work possible.**MLDB: The Machine Learning Database**- Oct 17, 2016.

MLDB is an opensource database designed for machine learning. Send it commands over a RESTful API to store data, explore it using SQL, then train machine learning models and expose them as APIs.**Urban Sound Classification with Neural Networks in Tensorflow**- Sep 12, 2016.

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.**Top /r/MachineLearning Posts, August: Google Brain AMA, Image Completion with TensorFlow, Japanese Cucumber Farming**- Sep 5, 2016.

Google Brain AMA; Image Completion with Deep Learning in TensorFlow; Japanese Cucumber Farming; Andrew Ng's machine learning class in Python; Google Brain datasets for robotics research**New Book: TensorFlow for Machine Intelligence, KDnuggets Offer**- Aug 30, 2016.

TensorFlow for Machine Intelligence is a hands-on introduction to learning algorithms and the "TensorFlow book for humans." KDnuggets readers get a 25% discount, available here.**Top KDnuggets tweets, Aug 17-23: Approaching (Almost) Any #MachineLearning Problem; #Database Nirvana – can one query language rule them all?**- Aug 24, 2016.

In Search of #Database Nirvana - can one query language rule them all? Google Cloud Datalab: #Jupyter meets #TensorFlow, #cloud meets local deployment; Approaching (Almost) Any #MachineLearning Problem; The Gentlest Introduction to Tensorflow Part 1.**KDnuggets™ News 16:n31, Aug 24: 10 Algo Machine Learning Engineers Need to Know; How to Become a Data Scientist; Gentle Tensorflow**- Aug 24, 2016.

The 10 Algorithms Machine Learning Engineers Need to Know; How to Become a Data Scientist - Part 1; The Gentlest Introduction to Tensorflow - Part 1; Approaching (Almost) Any Machine Learning Problem.**The Gentlest Introduction to Tensorflow – Part 2**- Aug 19, 2016.

Check out the second and final part of this introductory tutorial to TensorFlow.**The Gentlest Introduction to Tensorflow – Part 1**- Aug 17, 2016.

In this series of articles, we present the gentlest introduction to Tensorflow that starts off by showing how to do linear regression for a single feature problem, and expand from there.**Top KDnuggets tweets, Jul 27 – Aug 2: Understanding neural networks with Google TensorFlow Playground; Getting Started with Data Science in Python**- Aug 3, 2016.

Understanding neural networks with Google TensorFlow Playground; The 100 Best-Funded #Analytics #DataScience #Startups; Great tutorial: Getting Started with #DataScience - #Python; #MachineLearning over 1M hotel reviews: interesting insights.**Deep Learning For Chatbots, Part 2 – Implementing A Retrieval-Based Model In TensorFlow**- Jul 29, 2016.

Check out part 2 of this tutorial on building chatbots with deep neural networks. This part gets practical, and using Python and TensorFlow to implement.**Top KDnuggets tweets, Jul 20-26: Math-free simple explanation: #DeepLearning Demystified; Are #Humans Becoming More Machine-Like?**- Jul 27, 2016.

Finally, a #TensorFlow book for humans; Great math-free simple intro explanation video: Deep Learning Demystified; Does #sentiment analysis work? A tidy analysis of Yelp reviews; JupyterLab: the next generation of the #Jupyter Notebook**Multi-Task Learning in Tensorflow: Part 1**- Jul 20, 2016.

A discussion and step-by-step tutorial on how to use Tensorflow graphs for multi-task learning.**Recursive (not Recurrent!) Neural Networks in TensorFlow**- Jun 30, 2016.

Learn how to implement*recursive*neural networks in TensorFlow, which can be used to learn tree-like structures, or directed acyclic graphs.**Top /r/MachineLearning Posts, May: TensorFlow Tricks; Machine Learning Tutorials; Google TPUs**- Jun 1, 2016.

May on /r/MachineLearning was all about tutorials, TensorFlow, Google hardware, Deep Learning machine installations, and some laughs.**Introduction to Recurrent Networks in TensorFlow**- May 31, 2016.

A straightforward, introductory overview of implementing Recurrent Neural Networks in TensorFlow.**The Good, Bad & Ugly of TensorFlow**- May 24, 2016.

A survey of six months of rapid evolution (+ tips/hacks and code to fix the ugly stuff) using TensorFlow. Get some great advice from the trenches.**Top KDnuggets tweets, May 11-17: Vote: What software you used for Analytics, Data Mining, Data Science projects?**- May 18, 2016.

Vote: What software you used for Analytics, Data Mining, Data Science projects? Useful #Cheatsheet: #Python, R #rstats code for #MachineLearning Algorithms; TPOT: A #Python Tool for Automating Data Science; Randomize Acceptance of Borderline Research Papers, save 25 reviewer person-years.**How to Quantize Neural Networks with TensorFlow**- May 4, 2016.

The simplest motivation for quantization is to shrink neural network representation by storing the min and max for each layer. Learn more how to perform quantization for deep neural networks.**Top KDnuggets tweets, Apr 27 – May 3: Trifecta: Python, Machine Learning, and Dueling Languages; Fun game 4 #MachineLearning newbies**- May 4, 2016.

Trifecta: #Python, #MachineLearning, + Dueling Languages; Cartoon: When #Automation Goes Too Far; #AI Speed: 2-year old #xkcd cartoon: cannot check if a photo has a bird; Removing Duplicates in #BigData.**Top /r/MachineLearning Posts, April: New Google Machine Learning Videos, Deep Learning Book, TensorFlow Playground**- May 2, 2016.

Check out the most popular topics on Reddit's Machine Learning subreddit from April, including TensorFlow, deep learning, tutorials, self-reflection, and free books.**Top 10 IPython Notebook Tutorials for Data Science and Machine Learning**- Apr 22, 2016.

A list of 10 useful Github repositories made up of IPython (Jupyter) notebooks, focused on teaching data science and machine learning. Python is the clear target here, but general principles are transferable.**Tricking Deep Learning**- Apr 8, 2016.

Deep neural networks have had remarkable success with many tasks including image recognition. Read this overview regarding deep learning trickery, and why you should be cognizant.**Top /r/MachineLearning Posts, February: AlphaGo, Distributed TensorFlow, Neural Network Image Enhancement**- Mar 2, 2016.

In February on /r/MachineLearning, we get a run-down of the AlphaGo matches, Distributed TensorFlow is released, convolutional neural nets are cleaning Star Wars images, vintage science is on parade, military machine learning is criticized, and the overwhelmed researcher is given advice.**KDnuggets™ News 16:n08, Mar 2: Citizen Data Scientist Mirage; Spark Tipping Point; 80% Machine Learning**- Mar 2, 2016.

The Mirage of a Citizen Data Scientist; Why Spark Reached the Tipping Point in 2015; The Machine Learning Problem of The Next Decade; How The Algorithm Economy And Containers Are Changing The Apps.**Distributed TensorFlow Has Arrived**- Mar 1, 2016.

Google has open sourced its distributed version of TensorFlow. Get the info on it here, and catch up on some other TensorFlow news at the same time.**Opening Up Deep Learning For Everyone**- Feb 19, 2016.

Opening deep learning up to everyone is a noble goal. But is it achievable? Should non-programmers and even non-technical people be able to implement deep neural models?**Scikit Flow: Easy Deep Learning with TensorFlow and Scikit-learn**- Feb 12, 2016.

Scikit Learn is a new easy-to-use interface for TensorFlow from Google based on the Scikit-learn fit/predict model. Does it succeed in making deep learning more accessible?**KDnuggets™ News 16:n04, Feb 3: Is Deep Learning Overhyped? Businesses Will Need 1M Data Scientists**- Feb 3, 2016.

New Poll: Deep Learning - does reality match the hype?; Is Deep Learning Overhyped?; Businesses Will Need One Million Data Scientists by 2018; KDnuggets New Responsive, Mobile-Friendly Design.**Deep Learning with Spark and TensorFlow**- Jan 28, 2016.

The integration of TensorFlow with Spark leverages the distributed framework for hyperparameter tuning and model deployment at scale. Both time savings and improved error rates are demonstrated.**Google Launches Deep Learning with TensorFlow MOOC**- Jan 26, 2016.

Google and Udacity have partnered for a new self-paced course on deep learning and TensorFlow, starting immediately.**KDnuggets™ News 16:n01, Jan 13: Detect Fake Data Scientists; Tensorflow is Terrific; More arXiv Deep Learning, explained**- Jan 12, 2016.

20 Questions to Detect Fake Data Scientists; TensorFlow is Terrific; 5 More arXiv Deep Learning Papers, Explained; What questions can data science answer?**7 Steps to Understanding Deep Learning**- Jan 11, 2016.

There are many deep learning resources freely available online, but it can be confusing knowing where to begin. Go from vague understanding of deep neural networks to knowledgeable practitioner in 7 steps!**Top 5 Deep Learning Resources, January**- Jan 7, 2016.

There is an increasing volume of deep learning research, articles, blog posts, and news constantly emerging. Our Deep Learning Reading List aims to make this information easier to digest.