AI News, MachineLearning

MachineLearning

Roughly in order from easiest to hardest, those are: Books The most often recommended textbooks on general Machine Learning are (in no particular order): Note that these books delve deep into math, and might be a bit heavy for complete beginners.

If you don't care so much about derivations or how exactly the methods work but would rather just apply them, then the folllowing are good practical intros: (I've stolen most of the books in this 2nd list from /u/rvprasad's post here).

A very good list has been collected by /u/ilsunil here Programming Languages and Software In general, the most used languages in ML are probably Python, R and Matlab (with the latter losing more and more ground to the former two).

Word of caution: a lot of people in this subreddit are very critical of WEKA, so even though it's listed here, it is probably not a good tool to do anything more than just playing around a bit.

Over 150 of the Best Machine Learning, NLP, and Python Tutorials I’ve Found

While machine learning has a rich history dating back to 1959, the field is evolving at an unprecedented rate.

Typically, I’ll find an interesting tutorial or video, and that leads to three or four more tutorials or videos, and before I know it, I have 20 tabs of new material I need to go through.

(quora.com) Perceptrons (neuralnetworksanddeeplearning.com) The Perception (natureofcode.com) Single-layer Neural Networks (Perceptrons) (dcu.ie) From Perceptrons to Deep Networks (toptal.com) Introduction to linear regression analysis (duke.edu) Linear Regression (ufldl.stanford.edu) Linear Regression (readthedocs.io) Logistic Regression (readthedocs.io) Simple Linear Regression Tutorial for Machine Learning (machinelearningmastery.com) Logistic Regression Tutorial for Machine Learning (machinelearningmastery.com) Softmax Regression (ufldl.stanford.edu) Learning with gradient descent (neuralnetworksanddeeplearning.com) Gradient Descent (iamtrask.github.io) How to understand Gradient Descent algorithm (kdnuggets.com) An overview of gradient descent optimization algorithms (sebastianruder.com) Optimization: Stochastic Gradient Descent (Stanford CS231n) Generative Learning Algorithms (Stanford CS229) A

practical explanation of a Naive Bayes classifier (monkeylearn.com) An introduction to Support Vector Machines (SVM) (monkeylearn.com) Support Vector Machines (Stanford CS229) Linear classification: Support Vector Machine, Softmax (Stanford 231n) Yes you should understand backprop (medium.com/@karpathy) Can you give a visual explanation for the back propagation algorithm for neural networks?

Primer on Neural Network Models for Natural Language Processing (Yoav Goldberg) The Definitive Guide to Natural Language Processing (monkeylearn.com) Introduction to Natural Language Processing (algorithmia.com) Natural Language Processing Tutorial (vikparuchuri.com) Natural Language Processing (almost) from Scratch (arxiv.org) Deep Learning applied to NLP (arxiv.org) Deep Learning for NLP (without Magic) (Richard Socher) Understanding Convolutional Neural Networks for NLP (wildml.com) Deep Learning, NLP, and Representations (colah.github.io) Embed, encode, attend, predict: The new deep learning formula for state-of-the-art NLP models (explosion.ai) Understanding Natural Language with Deep Neural Networks Using Torch (nvidia.com) Deep Learning for NLP with Pytorch (pytorich.org) Bag of Words Meets Bags of Popcorn (kaggle.com) On word embeddings Part I, Part II, Part III (sebastianruder.com) The amazing power of word vectors (acolyer.org) word2vec Parameter Learning Explained (arxiv.org) Word2Vec Tutorial — The Skip-Gram Model, Negative Sampling (mccormickml.com) Attention and Memory in Deep Learning and NLP (wildml.com) Sequence to Sequence Models (tensorflow.org) Sequence to Sequence Learning with Neural Networks (NIPS 2014) Machine Learning is Fun Part 5: Language Translation with Deep Learning and the Magic of Sequences (medium.com/@ageitgey) How to use an Encoder-Decoder LSTM to Echo Sequences of Random Integers (machinelearningmastery.com) tf-seq2seq (google.github.io) 7

Crash Course in Python for Scientists (nbviewer.jupyter.org) PyCon scikit-learn Tutorial Index (nbviewer.jupyter.org) scikit-learn Classification Algorithms (github.com/mmmayo13) scikit-learn Tutorials (scikit-learn.org) Abridged scikit-learn Tutorials (github.com/mmmayo13) Tensorflow Tutorials (tensorflow.org) Introduction to TensorFlow — CPU vs GPU (medium.com/@erikhallstrm) TensorFlow: A primer (metaflow.fr) RNNs in Tensorflow (wildml.com) Implementing a CNN for Text Classification in TensorFlow (wildml.com) How to Run Text Summarization with TensorFlow (surmenok.com) PyTorch Tutorials (pytorch.org) A

Machine Learning for Humans🤖👶

After a couple of AI winters and periods of false hope over the past four decades, rapid advances in data storage and computer processing power have dramatically changed the game in recent years.

In 2015, Google trained a conversational agent (AI) that could not only convincingly interact with humans as a tech support helpdesk, but also discuss morality, express opinions, and answer general facts-based questions.

Many masters could not fathom how it would be possible for a machine to grasp the full nuance and complexity of this ancient Chinese war strategy game, with its 10¹⁷⁰ possible board positions (there are only 10⁸⁰atoms in the universe).

Just a few days ago (as of this writing), on August 11, 2017, OpenAI reached yet another incredible milestone by defeating the world’s top professionals in 1v1 matches of the online multiplayer game Dota 2.

Today AI is used to design evidence-based treatment plans for cancer patients, instantly analyze results from medical tests to escalate to the appropriate specialist immediately, and conduct scientific research for drug discovery.

A machine’s learning algorithm enables it to identify patterns in observed data, build models that explain the world, and predict things without having explicit pre-programmed rules and models.

The definition of an AGI is an artificial intelligence that can successfully perform any intellectual task that a human being can, including learning, planning and decision-making under uncertainty, communicating in natural language, making jokes, manipulating people, trading stocks, or… reprogramming itself.

recent report by the Future of Humanity Institute surveyed a panel of AI researchers on timelines for AGI, and found that “researchers believe there is a 50% chance of AI outperforming humans in all tasks in 45 years” (Grace et al, 2017).

We’ve personally spoken with a number of sane and reasonable AI practitioners who predict much longer timelines (the upper limit being “never”), and others whose timelines are alarmingly short — as little as a few years.

To go beyond the abstractions of a philosopher in an armchair and intelligently shape our roadmaps and policies with respect to AI, we must engage with the details of how machines see the world — what they “want”, their potential biases and failure modes, their temperamental quirks — just as we study psychology and neuroscience to understand how humans learn, decide, act, and feel.

Here are three suggestions on how to approach it, depending on your interests and how much time you have: Vishal most recently led growth at Upstart, a lending platform that utilizes machine learning to price credit, automate the borrowing process, and acquire users.

Machine Learning

Supervised learning algorithms are trained using labeled examples, such as an input where the desired output is known.

The learning algorithm receives a set of inputs along with the corresponding correct outputs, and the algorithm learns by comparing its actual output with correct outputs to find errors.

Through methods like classification, regression, prediction and gradient boosting, supervised learning uses patterns to predict the values of the label on additional unlabeled data.

Popular techniques include self-organizing maps, nearest-neighbor mapping, k-means clustering and singular value decomposition.

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