YOLO v3 introduction
I will make Yolo v3 easy and reusable without over-complicating things. With this tutorial, you will be able to simply implement object detection in TensorFlow 2.1
YOLO v3 mnist detect
In this tutorial, I’ll cover the Yolo v3 loss function and model training. We’ll train custom object detector on mnist dataset.
YOLO v3 custom train
In this step-by-step tutorial, I will show how to train a 7-class object detector (could use this method to get a dataset for every detector you may use).
YOLO v3 custom images
In this tutorial, I’m going to explain to you an easy way to train YOLO v3 on TensorFlow 2 to detect a custom object even if you’re a beginner or if you have no experience with coding.
YOLO v3 on Colab
In this tutorial, I will demonstrate how to use Google Colab (Google's free cloud service for AI developers) to train the Yolo v3 custom object detector with free GPU.
This is introduction tutorial to Reinforcement Learning. To understand everything from basics I will start with simple game called - CartPole.
Cartpole Double DQN
This is second reinforcement tutorial part, where we'll make our environment to use two (Double) neural networks to train our main model.
Cartpole Dueling DDQN
In this post, we’ll be covering Dueling DQN networks for reinforcement learning. This architecture is an improvement of our previous DDQN tutorial.
In this part we'll cover Epsilon Greedy method used in Deep Q Learning and we'll fix/prepare our source code for PER method.
Prioritized Experience Replay
Now we will try to change the sampling distribution by using a criterion to define the priority of each tuple of experience.
DQN PER with CNN
Now I will show you how to implement DQN with CNN. After this tutorial, you'll be able to create an agent that successfully plays almost ‘any’ game using only pixel inputs.
Pong with DQN
In this tutorial, I'll implement a Deep Neural Network for Reinforcement Learning (Deep Q Network) and we will see it learns and finally becomes good enough to beat the computer in Pong!
RL agents Beyond DQN
To wrap up deep reinforcement learning, I’ll introduce the types of agents beyond DQN’s (Value, Model, Policy optimization and Imitation Learning). We'll implement Policy Gradient!
Advanced Actor Critic (A2C)
Today, we'll study a Reinforcement Learning method which we can call a 'hybrid method': Actor Critic. This algorithm combines the value optimization and policy optimization approaches.
Asynchronous Actor Critic (A3C)
In this tutorial I will provide an implementation of Asynchronous Advantage Actor-Critic (A3C) algorithm in Tensorflow and Keras. We will use it to solve a simple challenge in Pong environment!
Policy Optimization (PPO)
In this tutorial we'll dive on the understanding of the PPO architecture and we'll implement a Proximal Policy Optimization (PPO) agent that learns to play Pong-v0.
YOLO v3 CS:GO aim bot
In this short tutorial I will show you how to set up YOLO v3 real time object detection on your web cam capture.
YOLO v3 CS:GO aim bot
This is my last tutorial with object detection in CS:GO, check out what results i got with custom YOLO v3 object detection model in this unusual aim bot.
YOLO v3 custom training
Continuing from my previous tutorial, where I showed how to prepare custom data for YOLO v3 object detection training, finally I will show you how to train that model.
YOLO v3 custom data
In this step-by-step tutorial, I will show you how to prepare data for your own custom YOLO v3 object detector.
YOLO v3 theory explained
In this tutorial I will explain you what is YOLO v3 object detection model and how it works behind the math.
Convolutional Neural Network
Convolutional Neural Network (CNN, or ConvNet) is a class of deep neural networks, most commonly applied to analyzing visual imagery, this tutorial is about them.
Convolutional Neural Network
Welcome to second tutorial. Now I will step you through a deep learning framework that will allow you to build neural networks more easily.
Build NN in Tensorflow
In this tutorial part we will build a deep neural network using tensorflow. You'll see that training model on Tensorflow is significant faster.
Build CNN in Tensorflow
In this tutorial part we will Implement helper functions that we will use when implementing a TensorFlow model and a fully functioning ConvNet.
Build CNN in Keras
We'll learn to use Keras(programming framework), written in Python and capable of running on top of several lower-level frameworks.
Build ResNet in Keras
Now we will learn how to build extremely deep convolutional networks, using Residual Networks (ResNets)
Deep neural netowrks
In last tutorial series we wrote 2 layers neural networks model, now it's time to build deep neural network, where we could have whatever count of layers we want.
Neural network (2 layers)
It's time to build neural network, with one hidden layer. You will see that there is no big difference between this model and logistic regression.
Logistic regression tutorial
In this tutorial, we will start analysing how we can predict correct cat or dog in a given picture using logistic regression as neural network.
OpenCV image stiching tutorials
In this tutorials you will learn how to make panorama and image stitching using Python and OpenCV. I'll demonstrate step by step how image stitching can be performed.
Selenium with Python
In this short tutorial I will show you how to use Selenium with Python if you want to automate human work on your browser. I will introduce you with Selenium basics.
Tensorflow CAPTCHA solver tutorial
In this tutorial we will learn how to do step by step custom object detection from images, used technique we will use to solve simple CAPTCHA images.
Tensorflow CS:GO aim bot
In this tutorial we will learn how to create our real time TensorFlow custom object detection by using it like an aim bot on CS:GO shooter game.