Curated Video
Design a computer system using tree search and reinforcement learning algorithms : Creating an Agent to Solve the MAB Problem Using Python and Tensorflow
From the section: The Multi-Armed Bandit. In this section, we will learn about the basics and look at one of the most foundational concepts in Reinforcement Learning – The Multi-Armed Bandit We construct a model of a MAB environment from...
Curated Video
Describe computer programming : Common Data Types
From the section: Common Coding Concepts.This section will cover common coding concepts such as Scratch setup, bugs, pseudocode, decomposition, commenting and many more. Common Coding Concepts: Common Data Types
Curated Video
Describe computer programming : You Can Code! Part 2
From the section: You can code!. This section will help you discover some interesting facts about coding. You can code!: You Can Code! Part 2 • Get the synopsis about the Tuple • Learn about the benefits of Tuple • Learn about the...
Curated Video
Describe computer programming : You Can Code! Part 1
From the section: You can code!. This section will help you discover some interesting facts about coding. You can code!: You Can Code! Part 1 • Create a simple list with names and another with numbers • Explain the concept of indexing •...
Curated Video
Create a computer vision system using decision tree algorithms to solve a real-world problem : Introduction: What are Artificial Neural Networks and how do they learn?
From the section: Artificial Neural Networks. In this section, we’ll cover Bayes Theorem, Naive Bayes, SVM and SVC to classify data. Artificial Neural Networks: Introduction: What are Artificial Neural Networks and how do they learn?
Curated Video
Create a computer vision system using decision tree algorithms to solve a real-world problem : [Activity] Support Vector Classifiers in Action
From the section: Machine Learning: Part 2. In this section, we’ll cover Bayes Theorem, Naive Bayes, SVM and SVC to classify data. Machine Learning: Part 2: [Activity] Support Vector Classifiers in Action
APMonitor
Python 🐍 Curve Fit with Step Test Data
The Scipy curve_fit function determines two unknown coefficients (dead-time and time constant) to minimize the difference between predicted and measured response values. Pandas imports the data and the dataframe header is diplayed with...
APMonitor
Computational Tools for Engineers Course Overview
Welcome to ChE263 which teaches computer skills useful to engineers and scientists. It covers MATLAB, Python, Mathcad, computer programs for doing all types of math, both numerically and symbolically; Excel, a spreadsheet program; and...
Programming Electronics Academy
Function Example Three: Arduino Course 8.3
A walk through of a User Defined Function in programming.
Curated Video
Predictive Analytics with TensorFlow 9.1: Using BRNN for Image Classification
We will first provide some contextual information about RNNs. We will see how to implement a BRNN implementation example using the TensorFlow library. The example is using the MNIST database of handwriting. • Look at contextual...
Packt
Data transmission : Physical versus logical topology
From the section: Introduction to Computer Networks (ICND1). This Section introduces Computer Networks. This includes lectures on Physical Components, Topology, Speed etc. Introduction to Computer Networks (ICND1): Physical versus...
Curated Video
Evaluate the impact of an AI application used in the real world. (case study) : Working with X-Ray images: Case Study - Part 6
From the section: CNN-Industry Live Project: Find Medical Abnormalities and Save Life. This section includes a CNN-Industry live project on working with X-Ray images. CNN-Industry Live Project: Find Medical Abnormalities and Save Life:...
Curated Video
Evaluate the impact of an AI application used in the real world. (case study) : Working with Flower Images: Case Study - Part 1
From the section: CNN-Industry Live Project: Playing With Real World Natural Images. This section includes a live project of working with flower images. CNN-Industry Live Project: Playing with Real World Natural Images: Working with...
Curated Video
Call a function : String Manipulation Functions
From the section: Intro to PHP Programming for Web Development. In this section, we’ll learn the basics of PHP programming. Intro to PHP Programming for Web Development: String Manipulation Functions
Curated Video
Create a computer vision system using decision tree algorithms to solve a real-world problem : Support Vector Machines (SVM) and Support Vector Classifiers (SVC)
From the section: Machine Learning: Part 2. In this section, we’ll cover Bayes Theorem, Naive Bayes, SVM and SVC to classify data. Machine Learning: Part 2: Support Vector Machines (SVM) and Support Vector Classifiers (SVC)
Pitsco Education
Code Cube Lesson 2: Changing Lights
In this lesson, you will learn how to make your Code Cube display multiple images.
Curated Video
Java 11 Programming for Beginners 3.3: Inheritance — The Non-Taxable Kind
Showcase the heaviest concept in OOP by example. • Build a second bot by leveraging the first • Go through theory
Curated Video
Predictive Analytics with TensorFlow 5.1: Using K-means for Predictive Analytics
This video will have a brief introduction to the unsupervised machine learning technique. We will also look at k-means for predictive analytics. • Understand the concept of clustering • See how k-means work • Use k-means for predicting...
Curated Video
Predictive Analytics with TensorFlow 7.4: Deep Belief Networks
While weights of an MLP are initialized randomly, a DBN uses a greedy layer-by-layer pretraining algorithm to initialize the network weights through probabilistic generative models composed of a visible layer and multiple layers of...
Curated Video
Predictive Analytics with TensorFlow 7.2: Fine-tuning DNN Hyperparameters
First, we will see DNN performance analysis. Next, we will tune the DNN hyperparameters. • Do DNN performance analysis • Tune the DNN hyperparameters
Curated Video
Create a computer vision system using decision tree algorithms to solve a real-world problem : [Activity] Naive Bayes in Action
From the section: Machine Learning: Part 2. In this section, we’ll cover Bayes Theorem, Naive Bayes, SVM and SVC to classify data. Machine Learning: Part 2: [Activity] Naive Bayes in Action
Programming Electronics Academy
Variables: Arduino Course 3.3
A description of what variables are, how they are used, and a useful analogy to understanding them.
Programming Electronics Academy
Serial Communication: Arduino Course 4.9
A walk through of serial communication.
Curated Video
Learning D3.JS 5.0 2.4: Creating Circles and Ellipses
In this video, we will learn how to create circles and ellipses. • Code a circle and view it in the browser • Fix the problem with viewing only one quarter of the circle • Code an ellipse and view it correctly