With the rise of Machine Learning came the rise of developer tools and libraries. What are they good for and what are the top ones that every data scientist and ML engineer should know. This article sheds some light on those questions.

A deep learning framework is an interface, library or a tool which allows us to build deep learning models more easily and quickly, without getting into the details of underlying algorithms. They provide a clear and concise way for defining models using a collection of pre-built and optimized components.

Predicting the stock market is one of the most difficult things to do given all the variables. There are numerous factors involved – physical factors vs. psychological, rational and irrational behavior, etc. All these aspects combine to make share prices volatile and very difficult to predict accurately.

In this article, we will work with historical data about the stock prices of a publicly listed company. We will implement a mix of machine learning algorithms to predict the future stock price of this company, starting with simple algorithms like averaging and linear regression, and then move on to advanced techniques like Auto ARIMA and LSTM.

With the Keras integration, and Eager Execution enabled by default, TensorFlow 2.0 is all about ease of use, and simplicity. In this episode of Coding TensorFlow, Developer Advocate Paige Bailey (Twitter: @dynamicwebpaige) shows us the tf_upgrade_v2 tool, which helps with the 2.0 transition by converting existing TensorFlow 1.12 Python scripts to TensorFlow’s 2.0 preview.

Lex Fridman has just posted this video of the first lecture of MIT course 6.S091: Deep Reinforcement Learning, introducing the fascinating field of Deep RL.

For more lecture videos on deep learning, reinforcement learning (RL), artificial intelligence (AI & AGI), and podcast conversations, visit our website or follow TensorFlow code tutorials on our GitHub repo.