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Machine Learning: Random Forest with Python from Scratch - Accuracy and Error-2
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...
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Machine Learning: Random Forest with Python from Scratch - How to Build a Tree
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...
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Machine Learning: Random Forest with Python from Scratch - Leaf and Decision Node
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...
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Machine Learning: Random Forest with Python from Scratch - How Decision Trees and Random Forest Work
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...
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Fundamentals of Neural Networks - Lab 3 - Deep CNN
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...
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Fundamentals of Neural Networks - Bi-Directional RNN
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...
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Fundamentals of Neural Networks - Gated Recurrent Unit (GRU)
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"...
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Fundamentals of Neural Networks - Backward Propagation Through Time
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...
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Fundamentals of Neural Networks - Why Use RNN
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...
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Deep Learning - Crash Course 2023 - Probability Distribution Table
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.
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Advanced Chatbots with Deep Learning and Python - Predictions
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.
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Recommender Systems Complete Course Beginner to Advanced - Project Amazon Product Recommendation System: Two-Tower Model
This lecture elaborates on the two-tower model, which we have already seen in the previous projects and modules of the course.
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Deep Learning - Artificial Neural Networks with Tensorflow - Making Predictions
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...
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Deep Learning - Artificial Neural Networks with Tensorflow - Code Preparation (Regression Theory)
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,...
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Data Science - Time Series Forecasting with Facebook Prophet in Python - Prophet Section Summary
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...
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Data Science - Time Series Forecasting with Facebook Prophet in Python - Prophet in Code: Fit, Forecast, Plot
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...
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Data Science - Time Series Forecasting with Facebook Prophet in Python - Prophet: Code Preparation
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...
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Recommender Systems: An Applied Approach using Deep Learning - Two-Tower Model
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:...
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Chatbots for Beginners: A Complete Guide to Build Chatbots - Deep Learning-Based Chatbot Architecture and Development: Predictions
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...
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Plant Growth
Dr. Forrester explains what a plant needs to grow: water, light, air, and minerals.
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Learning About Probability
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.
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Review for You: Story Elements
Mr. Griot demonstrates how to use story text as clues for making predictions and identifying main idea, supporting details, and story elements.
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Macbeth 5.3 Interview: Macbeth
This video analyzes Act 5, Scene 3 of Shakespeare's "Macbeth," where the protagonist, Macbeth, confronts the looming threat of the English army and Malcolm's forces, while grappling with the prophecies that assure his invincibility....
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Climate Change Crisis: Urgent Warning and Advanced Predictive Technology
This video highlights the urgent need for action on climate change, with a warning that we may be nearing a point of no return within a decade. It discusses the advancements in climate prediction models, showcasing the collaboration...