[unable to retrieve full-text content]Also 37 Reasons why your #NeuralNetwork is not working; Making Predictive Model Robust: Holdout vs Cross-Validation.
Original Post: Top KDnuggets tweets, Aug 09-15: #Tensorflow tutorials and best practices; Top Influencers for #DataScience
TensorFlow
Top KDnuggets tweets, Jul 26 – Aug 01: 37 Reasons why your #NeuralNetwork is not working; Machine Learning Exercises in Python
[unable to retrieve full-text content]Also Hill criteria for #causality vs #correlation via #xkcd cartoons; #MachineLearning Workflows in #Python from Scratch Part 2: k-means Clustering
Original Post: Top KDnuggets tweets, Jul 26 – Aug 01: 37 Reasons why your #NeuralNetwork is not working; Machine Learning Exercises in Python
Building Your Own Neural Machine Translation System in TensorFlow
Posted by Thang Luong, Research Scientist, and Eugene Brevdo, Staff Software Engineer, Google Brain TeamMachine translation – the task of automatically translating between languages – is one of the most active research areas in the machine learning community. Among the many approaches to machine translation, sequence-to-sequence (“seq2seq”) models [1, 2] have recently enjoyed great success and have become the de facto standard in most commercial translation systems, such as Google Translate, thanks to its ability to use deep neural networks to capture sentence meanings. However, while there is an abundance of material on seq2seq models such as OpenNMT or tf-seq2seq, there is a lack of material that teaches people both the knowledge and the skills to easily build high-quality translation systems.Today we are happy to announce a new Neural Machine Translation (NMT) tutorial for TensorFlow that gives readers a full…
Original Post: Building Your Own Neural Machine Translation System in TensorFlow
KDnuggets™ News 17:n26, Jul 12: Applying Deep Learning to Real-world Problems; New Poll: Will society be better from increased automation, AI?
[unable to retrieve full-text content]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
Original Post: KDnuggets™ News 17:n26, Jul 12: Applying Deep Learning to Real-world Problems; New Poll: Will society be better from increased automation, AI?
Top /r/MachineLearning Posts, June: NumPy Gets Funding; ML Cheat Sheets For All; Hot Dog or Not?!?
[unable to retrieve full-text content]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
Original Post: Top /r/MachineLearning Posts, June: NumPy Gets Funding; ML Cheat Sheets For All; Hot Dog or Not?!?
Using the TensorFlow API: An Introductory Tutorial Series
[unable to retrieve full-text content]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.
Original Post: Using the TensorFlow API: An Introductory Tutorial Series
Deep Learning Zero to One: 5 Awe-Inspiring Demos with Code for Beginners
[unable to retrieve full-text content]Here are deep learning demos and examples you can just download and run. No Math. No Theory. No Books.
Original Post: Deep Learning Zero to One: 5 Awe-Inspiring Demos with Code for Beginners
The world’s first protein database for Machine Learning and AI
[unable to retrieve full-text content]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
Original Post: The world’s first protein database for Machine Learning and AI
MultiModel: Multi-Task Machine Learning Across Domains
Original Post: MultiModel: Multi-Task Machine Learning Across Domains
AI for fintech course – Early discounts and limited places
[unable to retrieve full-text content]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.
Original Post: AI for fintech course – Early discounts and limited places