12:13
Forcing a Neural Network to imagine what a rabbit looks like
29:32
I get confused trying to learn Gaussian Processes | Learn with me!
03:17
What is pooling? | CNN's #3
03:27
What is a receptive field? | CNNs #2
04:33
How Convolutional Neural Networks Work | CNN's #1
02:07
Boosting in Machine Learning | Ensembles #3
02:36
Bagging... works how? | Ensembles #2: Intuition of Bootstrap Aggregating in Machine Learning
02:53
Intuition: Ensemble Learning | Ensembles in Machine Learning #1
01:56
Maximum Likelihood Estimation (MLE): The Intuition
02:11
What is log likelihood and why do we use it?
02:22
Intuition of Likelihood in Machine Learning
01:55
Recurrent Neural Network Implementation Details
01:32
What is an RNN?
01:42
What is overfitting?
02:13
Intuition: Training Set vs. Test Set vs. Validation Set
01:04
Terms: Independent and Identically Distributed (IID)
01:25
What is independence (probability)?
01:50
Intuition Behind Linear Regression
01:01
Classification Vs. Regression in one minute.
02:46
Loss functions: What are they?
03:29
What is a Confusion Matrix? How does it work? Why do we care? (Fixing last week's mistake!)
01:33
Intuition: How does the Heaviside Activation Function work?
02:39
Terms: Nonlinearity
02:10
Terms: Activation Functions
01:39
Neural Network Caveats
01:36
What is the difference between a Deep Neural Network and an Artificial Neural Network?
02:09
Intuition: Artificial Neural Networks
01:14
Intuition: Perceptrons and Artificial Neural Networks
01:34
Types of Machine Learning
00:57
Terms: Training vs. Evaluation vs. Prediction
03:46
What is Machine Learning?
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Explaining the intuition behind machine learning concepts and algorithms.
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