Machine Learning

Reinforcement learning has gained valuable popularity with the relatively recent success of DeepMind's AlphaGo method to beat the world champion Go player. The AlphaGo method was educated in part by reinforcement learning on deep neural networks. This style of learning is a distinct feature of machine learning from the classical supervised and unsupervised paradigms. In reinforcement learning, the network responds to environmental data (called the state) using deep neural networks and influences the behavior of an agent to try to optimize a reward. This technique helps a network to learn how to play sports, such as Atari or other video games, or some other challenge that can be rewritten as a form of the game. In this tutorial, a common model of reinforcement learning, I will introduce the broad principles of Q learning, and I will demonstrate how to incorporate deep Q learning in TensorFlow. Introduction to reinforcement learning As mentioned above, reinforcement learning consis...

I enjoy reading and spending time browsing Medium and writing in it. I use it as my blog, as my go-to source for exciting news, opinions, idea center, topic reading, a lot.  But writing and sharing information and getting around in Artificial Intelligence is not necessarily very easy. Using Artificial Intelligence, Machine learning, Deep Learning, Natural Language Processing and other buzzing words effectively can be a little complex as it keeps changing itself. Fortunately, Blogs gave us a lot of information on all the aspects and Cheatsheets plays a very vital role in the field of learning and memorizing things. They have excellent tips and tricks and “how-to” articles but they are all over the place. I spent one month putting together all cheat sheets for  Artificial Intelligence, Machine learning, Deep Learning, Natural Language Processing all together to work effectively and get to know every useful information . Feel free to use it, critique it, recommend, add to it, etc...

Tensorflow is an end-to-end open-source machine learning platform for everyone. It has a comprehensive, flexible ecosystem of tools, libraries, and community resources that lets researchers push the state-of-the-art in ML, and developers easily build and deploy ML-powered applications. whereas, With over 375,000 individual users as of early 2020, Keras has strong adoption across both the industry and the research community. Together with TensorFlow 2.0, Keras has more adoption than any other deep learning solution — in every vertical. You are already constantly interacting with features built with Keras — it is in use at Netflix, Uber, Yelp, Instacart, Zocdoc, Square, and many others. It is especially popular among startups that place deep learning at the core of their products. Keras & TensorFlow 2.0 are also a favorite among researchers, coming in #1 in terms of mentions in scientific papers indexed by Google Scholar. Keras has also been adopted by researchers at la...