**TensorFlow is dead, long live TensorFlow!**

If you’re an AI enthusiast and you didn’t see the big news this month, you might have just snoozed through an off-the-charts earthquake. Everything is about to change! It’s a radical makeover. The consequences of what just happened are going to have major ripple effects on every industry, just you wait. If you’re a TF beginner in mid-2019, you’re extra lucky because you picked the best possible time to enter AI (though you might want to start from scratch if your old tutorials have the word ‘session’ in them). In a nutshell: TensorFlow has just gone full Keras. Those of you who know those words just fell out of your chairs. Boom!

**Artificial Intelligence Beyond Deep Neural Networks**

Artificial intelligence (AI) is dominated by pattern recognition techniques. Recently, major advances have been made in the fields of image recognition, machine translation, audio processing and several others thanks to the development and refinement of deep learning. But deep learning is not the cure for every problem. In fact, it can be a disease in its own right. No biological system, even over generations of evolution, requires the same scale of training data for simple tasks that state-of-the-art machine learning algorithms require. Brains use many techniques other than derivatives in order to learn. Animals can detect novelty and remember significant events, even if they only happened once. Here are four reasons, among others, why the AI community should think beyond deep learning.

**AI/ML Lessons for Creating a Platform Strategy – Part 1**

McKinsey says platform companies will represent 30% of global business revenue by next year (2020). Here are some lessons and examples to help mature companies evaluate where they can create AI/ML-enabled platforms to remain competitive. This is a long topic so this will be Part 1 of 2.

**Video: Answering Real-World Questions with SQL Queries**

In this video tutorial, you’ll break down real-world questions to construct complex queries using CASE statements, subqueries, and common table expressions.

**Advanced Keras – Constructing Complex Custom Losses and Metrics**

In this tutorial I cover a simple trick that will allow you to construct custom loss functions in Keras which can receive arguments other than y_true and y_pred.

**Bayes vs. the Invaders! Part Two: Abnormal Distributions**

The simple linear model developed in the previous post is far from satisfying. It makes many unsupportable assumptions about the data and the form of the residual errors from the model. Most obviously, it relies on an underlying Gaussian (or normal) distribution for its understanding of the data. For our count data, some basic features of the Guassian are inappropriate.

**A Permutation Test Regression Example**

In a post last week I talked a bit about Permutation (Randomization) tests, and how they differ from the (classical parametric) testing procedure that we generally use in econometrics. I’m going to assume that you’ve read that post.

**Creating and Deploying a Python Machine Learning Service**

Imagine you’re the moderator of a message board or comment section. You don’t want to read everything your users write online, yet you want to be alerted in case a discussion turns sour or people start spewing racial slurs all over the place. So, you decide to build yourself an automated system for hate speech detection. Text classification via machine learning is an obvious choice of technology. However, turning model prototypes into working services has proven to be a widespread challenge.

**Prison Escape – Solving Prisoner’s Dilemma with Machine Learning**

In today’s article, we are going to demystify the most famous Game Theory problem – Prisoner’s Dilemma. We are going to study the problem itself, as well as the strategies that can be used to approach it. Ultimately we are going to conduct a tournament to find the most successful strategy. By the end of this article, you will be familiar with the Prisoner’s Dilemma mechanics and its implications that can be useful in many real-world situations.

**Hitchhiker’s Guide to Residual Networks (ResNet) in Keras**

Very deep neural networks are hard to train as they are more prone to vanishing or exploding gradients. To solve this problem, the activation unit from a layer could be fed directly to a deeper layer of the network, which is termed as a skip connection. This forms the basis of residual networks or ResNets. This post will introduce the basics the residual networks before implementing one in Keras.

**A Simple ML Model Base Class**

In this post I will describe a simple implementation of a base class for Machine Learning Models. This post will focus on making predictions with ML models, and integrating ML models with other software components. Training code will not be shown to keep the code simple. The code in this post will be written in Python, if you aren’t familiar with abstract base classes in Python, here is a good place to learn.

**Quantum Machine Learning: Future of AI**

Quantum computing is another innovation that has the potential to take AI to the next level. Quantum computers use the properties of quantum mechanics to process information. A traditional computer encodes information in bits, which can take a value of 0 or 1. In contrast, quantum computers encode information in qubits. Like a bit, a qubit can take on values of 0 or 1. However, a qubit is able to take on multiple states at the same time, a quantum concept called superposition. Therefore, two qubits can take on any of 4 possible states: 01, 11, 10, or 00. In general, n qubits can represent 2^n different states. This (very simplified) concept of superposition enables quantum computers to be much more powerful than traditional computers. They can represent much more information with much less computing power.

**Everything About Python – Beginner To Advanced**

This article aims to outline all of the key points of Python programming language. My target is to keep the information short, relevant and focus on the most important topics which are absolutely required to be understood. After reading this blog, you will be able to use any Python library or implement your own Python packages.

**An introduction to Q-Learning: reinforcement learning**

This article is the second part of my ‘Deep reinforcement learning’ series. The complete series shall be available both on Medium and in videos on my YouTube channel.

In the first part of the series we learnt the basics of reinforcement learning.

Q-learning is a values-based learning algorithm in reinforcement learning. In this article, we learn about Q-Learning and its details:

• What is Q-Learning ?

• Mathematics behind Q-Learning

• Implementation using python

**Introduction to Bayesian Linear Regression**

The Bayesian vs Frequentist debate is one of those academic arguments that I find more interesting to watch than engage in. Rather than enthusiastically jump in on one side, I think it’s more productive to learn both methods of statistical inference and apply them where appropriate. In that line of thinking, recently, I have been working to learn and apply Bayesian inference methods to supplement the frequentist statistics covered in my grad classes. One of my first areas of focus in applied Bayesian Inference was Bayesian Linear modeling. The most important part of the learning process might just be explaining an idea to others, and this post is my attempt to introduce the concept of Bayesian Linear Regression. We’ll do a brief review of the frequentist approach to linear regression, introduce the Bayesian interpretation, and look at some results applied to a simple dataset. I kept the code out of this article, but it can be found on GitHub in a Jupyter Notebook.

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