This article will covers Reinforcement Learning. This technique is different than many of the other machine learning techniques we have and has many applications in training agents (an AI) to interact with environments like games. Rather than feeding our machine learning model millions of examples we let our model come up with its own examples by exploring an environemt.

The concept is simple. Humans learn by exploring and learning from mistakes and past experiences so let’s have our computer do the same.


Before we dive into explaining reinforcement learning we need to define a few key pieces of terminology.


Natural Language Processing (or NLP for short) is a discipline in computing that deals with the communication between natural (human) languages and computer languages. A common example of NLP is something like spellcheck or autocomplete. Essentially NLP is the field that focuses on how computers can understand and/or process natural/human languages.

Recurrent Neural Networks

In this tutorial we will introduce a new kind of neural network that is much more capable of processing sequential data such as text or characters called a recurrent neural network (RNN for short).

We will learn how to use a reccurent neural network to do the following:

  • Sentiment…

This tutorial demonstrates how to generate images of handwritten digits using a Deep Convolutional Generative Adversarial Network (DCGAN). The code is written using the Keras Sequential API with a tf.GradientTape training loop.

What are GANs?

Generative Adversarial Networks (GANs) are one of the most interesting ideas in computer science today. Two models are trained simultaneously by an adversarial process. A generator (“the artist”) learns to create images that look real, while a discriminator (“the art critic”) learns to tell real images apart from fakes.

During training, the generator progressively becomes better at creating images that look real, while the discriminator becomes better at…

so we will walk through how actually c code that generates donut shape works At its core, it’s a framebuffer and a Z-buffer into which I render pixels. Since it’s just rendering relatively low-resolution ASCII art, I massively cheat. All it does is plot pixels along the surface of the torus at fixed-angle increments, and does it densely enough that the final result looks solid. The “pixels” it plots are ASCII characters corresponding to the illumination value of the surface at each point: .,-~:;=!*#$@ from dimmest to brightest. No raytracing required.

So how do we do that? Well, let’s start…

In this guide we will learn how to peform image classification and object detection/recognition using convolutional neural network. with something called a computer vision

The goal of our convolutional neural networks will be to classify and detect images or specific objects from within the image. We will be using image data as our features and a label for those images as our label or output.

We already know how neural networks work so we can skip through the basics and move right into explaining the following concepts.

  • Image Data
  • Convolutional Layer
  • Pooling Layer
  • CNN Architectures

The major differences we are…

In 2005 game company open sourced the engine software for their video game quake 3 arena in that source code fans of the game discovered an algorithm that was so ingenious it quickly became famous and the only thing this algorithm does is calculate the inverse of a square root if i had to write a piece of code that would calculate the inverse of a square root this is how i would do it here i’m using the c programming language the same programming language used for quake 3 but to be fair i wouldn’t actually write the square…

In this notebook, you will learn how to create and use a neural network to classify articles of clothing. To achieve this, we will use a sub-module of TensorFlow called keras.


Before we dive in and start discussing neural networks, I’d like to give a breif introduction to keras.

From the keras official documentation ( keras is described as follows.

“Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano. It was developed with a focus on enabling fast experimentation.

Use Keras if you need a deep learning…

In introduction to TensorFlow 2.0. We will walk through the following topics within the TensorFlow module:

  • TensorFlow Install and Setup
  • Representing Tensors
  • Tensor Shape and Rank
  • Types of Tensors

If you’d like to follow along without installing TensorFlow on your machine you can use Google Collaboratory. Collaboratory is a free Jupyter notebook environment that requires no setup and runs entirely in the cloud.

Installing TensorFlow

To install TensorFlow on your local machine you can use pip.

pip install tensorflow

If you have a CUDA enabled GPU you can install the GPU version of TensorFlow. You will also need to install some other…

we all had dreams of creating A.I that can do amazing things like play games with you when we were child and now we can actually make this happen with thing called reinforcement learning assuming you don’t know machine learning or deep learning still you can make a little A.i for you self which can play learn can do mistakes and other things so the question is how ?

So you can do this by python in gym environment it will not be a very complex agent (it’s a term for our a.i in reinforcement learning)

i know what you…

Slim Shady

Just a boring guy who falls in love with machine learning

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