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StatQuest with Josh Starmer

Neural Networks / Deep Learning

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28 items
Last updated on Apr 8, 2024
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Happy Halloween (Neural Networks Are Not Scary)
0:59
The Essential Main Ideas of Neural Networks
18:54
The Chain Rule
18:24
Gradient Descent, Step-by-Step
23:54
Neural Networks Pt. 2: Backpropagation Main Ideas
17:34
Backpropagation Details Pt. 1: Optimizing 3 parameters simultaneously.
18:32
Backpropagation Details Pt. 2: Going bonkers with The Chain Rule
13:09
Neural Networks Pt. 3: ReLU In Action!!!
8:58
Neural Networks Pt. 4: Multiple Inputs and Outputs
13:50
Neural Networks Part 5: ArgMax and SoftMax
14:03
The SoftMax Derivative, Step-by-Step!!!
7:13
Neural Networks Part 6: Cross Entropy
9:31
Neural Networks Part 7: Cross Entropy Derivatives and Backpropagation
22:08
Neural Networks Part 8: Image Classification with Convolutional Neural Networks (CNNs)
15:24
Recurrent Neural Networks (RNNs), Clearly Explained!!!
16:37
Long Short-Term Memory (LSTM), Clearly Explained
20:45
Word Embedding and Word2Vec, Clearly Explained!!!
16:12
Sequence-to-Sequence (seq2seq) Encoder-Decoder Neural Networks, Clearly Explained!!!
16:50
Attention for Neural Networks, Clearly Explained!!!
15:51
Transformer Neural Networks, ChatGPT's foundation, Clearly Explained!!!
36:15
Decoder-Only Transformers, ChatGPTs specific Transformer, Clearly Explained!!!
36:45
Tensors for Neural Networks, Clearly Explained!!!
9:40
Essential Matrix Algebra for Neural Networks, Clearly Explained!!!
30:01
The matrix math behind transformer neural networks, one step at a time!!!
23:43
The StatQuest Introduction to PyTorch
23:22
Introduction to Coding Neural Networks with PyTorch and Lightning
20:43
Long Short-Term Memory with PyTorch + Lightning
33:24
Word Embedding in PyTorch + Lightning
32:02