Instructional Video3:37
Science Buddies

Simple Explanation of Neural Networks

K - 5th
Tracy from Science Buddies guides you through the basics of neuron networks used in machine learning. Discover how neurons and layers work together to process information and make predictions, from image recognition to language...
Instructional Video9:25
Curated Video

Deep Learning - Recurrent Neural Networks with TensorFlow - Recurrent Neural Networks (Elman Unit Part 1)

Higher Ed
In this video, we will get introduced to simple recurrent neural networks also called as Elman Unit. This clip is from the chapter "Recurrent Neural Networks (RNNs), Time Series, and Sequence Data" of the series "Deep Learning -...
Instructional Video12:05
Curated Video

Deep Learning - Recurrent Neural Networks with TensorFlow - Autoregressive Linear Model for Time Series Prediction

Higher Ed
In this video, we will dive into coding and learn about the autoregressive linear model for time series prediction. This clip is from the chapter "Recurrent Neural Networks (RNNs), Time Series, and Sequence Data" of the series "Deep...
Instructional Video10:07
Curated Video

Machine Learning: Random Forest with Python from Scratch - Accuracy and Error-2

Higher Ed
In this video, you will learn to implement the accuracy method to help determine our model's performance. This clip is from the chapter "Random Forest Step-by-Step" of the series "Machine Learning: Random Forest with Python from...
Instructional Video7:43
Curated Video

Machine Learning: Random Forest with Python from Scratch - How to Build a Tree

Higher Ed
After creating the decision node and leaf node classes, we will build our tree to add the nodes. This clip is from the chapter "Random Forest Step-by-Step" of the series "Machine Learning: Random Forest with Python from Scratch©".This...
Instructional Video4:41
Curated Video

Machine Learning: Random Forest with Python from Scratch - Leaf and Decision Node

Higher Ed
In this lesson, you will learn to create two classes, a leaf node and a decision node, with a constructor. This clip is from the chapter "Random Forest Step-by-Step" of the series "Machine Learning: Random Forest with Python from...
Instructional Video8:03
Curated Video

Machine Learning: Random Forest with Python from Scratch - How Decision Trees and Random Forest Work

Higher Ed
We will understand what a decision tree is and create a decision tree and get a prediction result from the decision tree. This clip is from the chapter "Random Forest Step-by-Step" of the series "Machine Learning: Random Forest with...
Instructional Video16:55
Curated Video

Fundamentals of Neural Networks - Lab 3 - Deep CNN

Higher Ed
This video demonstrates a deeper CNN, where you will build a much bigger number of trainable parameters. This clip is from the chapter "Convolutional Neural Networks" of the series "Fundamentals in Neural Networks".This section explains...
Instructional Video6:02
Curated Video

Fundamentals of Neural Networks - Bi-Directional RNN

Higher Ed
Bidirectional recurrent neural networks (BRNN) connect two hidden layers of opposite directions to the same output. BRNNs are especially useful when the context of the input is needed. For example, in handwriting recognition, the...
Instructional Video9:38
Curated Video

Fundamentals of Neural Networks - Gated Recurrent Unit (GRU)

Higher Ed
Gated recurrent units (GRUs) are a gating mechanism in recurrent neural networks. GRUs have been shown to exhibit better performance on certain smaller and less frequent datasets. This clip is from the chapter "Recurrent Neural Networks"...
Instructional Video9:32
Curated Video

Fundamentals of Neural Networks - Backward Propagation Through Time

Higher Ed
Backpropagation through time (BPTT) is a gradient-based technique for training certain types of recurrent neural networks. It can be used to train Elman networks. The algorithm was independently derived by numerous researchers. This clip...
Instructional Video6:56
Curated Video

Fundamentals of Neural Networks - Why Use RNN

Higher Ed
A Recurrent neural network is a type of artificial neural network commonly used in speech recognition and natural language processing. Recurrent neural networks recognize data's sequential characteristics and use patterns to predict the...
Instructional Video6:06
Curated Video

Deep Learning - Crash Course 2023 - Probability Distribution Table

Higher Ed
In this video, you will learn about the probability distribution table. This clip is from the chapter "Basic Probability" of the series "Deep Learning - Crash Course 2023".In this section, we will talk about probability.
Instructional Video4:32
Curated Video

Advanced Chatbots with Deep Learning and Python - Predictions

Higher Ed
After checking our model for accuracy, we will make predictions of our results from the model we created. We will visualize the predictions using the test data.
Instructional Video2:29
Curated Video

Recommender Systems Complete Course Beginner to Advanced - Project Amazon Product Recommendation System: Two-Tower Model

Higher Ed
This lecture elaborates on the two-tower model, which we have already seen in the previous projects and modules of the course.
Instructional Video6:50
Curated Video

Deep Learning - Artificial Neural Networks with Tensorflow - Making Predictions

Higher Ed
In this video, we will be talking about another important part of creating a model, which is making predictions. This clip is from the chapter "Machine Learning and Neurons" of the series "Deep Learning - Artificial Neural Networks with...
Instructional Video7:23
Curated Video

Deep Learning - Artificial Neural Networks with Tensorflow - Code Preparation (Regression Theory)

Higher Ed
In this video, we will take a crash course in linear regression for TensorFlow 2.0. This clip is from the chapter "Machine Learning and Neurons" of the series "Deep Learning - Artificial Neural Networks with TensorFlow".In this section,...
Instructional Video3:31
Curated Video

Data Science - Time Series Forecasting with Facebook Prophet in Python - Prophet Section Summary

Higher Ed
In this video, we will summarize what we have learnt in this section. This clip is from the chapter "Facebook Prophet" of the series "Data Science - Time Series Forecasting with Facebook Prophet in Python".In this section, we will...
Instructional Video8:41
Curated Video

Data Science - Time Series Forecasting with Facebook Prophet in Python - Prophet in Code: Fit, Forecast, Plot

Higher Ed
In this video, you will learn how to fit, forecast, and plot our data. This clip is from the chapter "Facebook Prophet" of the series "Data Science - Time Series Forecasting with Facebook Prophet in Python".In this section, we will...
Instructional Video12:53
Curated Video

Data Science - Time Series Forecasting with Facebook Prophet in Python - Prophet: Code Preparation

Higher Ed
In this video, we will go through the code structure for Prophet. This clip is from the chapter "Facebook Prophet" of the series "Data Science - Time Series Forecasting with Facebook Prophet in Python".In this section, we will explore...
Instructional Video2:27
Curated Video

Recommender Systems: An Applied Approach using Deep Learning - Two-Tower Model

Higher Ed
In this lesson, we will discuss the two-tower model, which uses user embedding and item embedding in the recommender system. This clip is from the chapter "Project Amazon Product Recommendation System" of the series "Recommender Systems:...
Instructional Video4:30
Curated Video

Chatbots for Beginners: A Complete Guide to Build Chatbots - Deep Learning-Based Chatbot Architecture and Development: Predictions

Higher Ed
After checking our model for accuracy, we will make predictions of our results from the model we created. We will visualize the predictions using the test data. This clip is from the chapter "Advanced Chatbots with Deep Learning and...
Instructional Video4:46
Curated Video

Plant Growth

3rd - 8th
Dr. Forrester explains what a plant needs to grow: water, light, air, and minerals.
Instructional Video6:03
Curated Video

Learning About Probability

K - 8th
Mr. Addit reviews terms experiment, probability, and prediction. He then conducts some probability experiments and displays his data on a tally chart and on a bar graph.