New course with Hugging Face: Quantization in Depth ๐ค
Enroll now: https://bit.ly/44nXDNa
Weโre excited to introduce Quantization in Depth, a new short course built in collaboration with Hugging Face, taught by Younes Belkada and Mark Sun, and designed to provide a deep technical understanding of quantization.
This course lets you build and customize your own linear quantizer from scratch, going beyond standard open source libraries such as PyTorch and Quanto, which were the focus of our previous course, Quantization Fundamentals.
Join in to:
- Implement and customize linear quantization from scratch, trading off between space and performance, and choosing between two "modes:" asymmetric and symmetric; and three granularities: per-tensor, per-channel, and per-group quantization.
- Measure the quantization error of each of these options as you balance the performance and space tradeoffs for each option.
- Build your own quantizer in PyTorch, to quantize any open source model's dense layers from 32 bits to 8 bits.
- Go beyond 8 bits, and pack four 2-bit weights into one 8-bit integer, and also, learn to unpack them.
Quantization in Depth gives you the foundation to study more advanced quantization methods, some of which are recommended at the end of the course.
Learn more: https://bit.ly/44nXDNa
Watch on YouTube โ
(saves to browser)
Sign in to unlock AI tutor explanation ยท โก30
Playlist
Uploads from DeepLearningAI ยท DeepLearningAI ยท 0 of 60
โ Previous
Next โ
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
Forward and Backward Propagation (C1W4L06)
DeepLearningAI
deeplearning.ai's Heroes of Deep Learning: Yuanqing Lin
DeepLearningAI
deeplearning.ai's Heroes of Deep Learning: Ruslan Salakhutdinov
DeepLearningAI
deeplearning.ai's Heroes of Deep Learning: Yoshua Bengio
DeepLearningAI
deeplearning.ai's Heroes of Deep Learning: Pieter Abbeel
DeepLearningAI
deeplearning.ai's Heroes of Deep Learning: Ian Goodfellow
DeepLearningAI
deeplearning.ai's Heroes of Deep Learning: Andrej Karpathy
DeepLearningAI
Using an Appropriate Scale (C2W3L02)
DeepLearningAI
Gradient Checking (C2W1L13)
DeepLearningAI
Gradient Checking Implementation Notes (C2W1L14)
DeepLearningAI
Learning Rate Decay (C2W2L09)
DeepLearningAI
Understanding Mini-Batch Gradient Dexcent (C2W2L02)
DeepLearningAI
Mini Batch Gradient Descent (C2W2L01)
DeepLearningAI
The Problem of Local Optima (C2W3L10)
DeepLearningAI
Exponentially Weighted Averages (C2W2L03)
DeepLearningAI
Tuning Process (C2W3L01)
DeepLearningAI
Understanding Exponentially Weighted Averages (C2W2L04)
DeepLearningAI
Bias Correction of Exponentially Weighted Averages (C2W2L05)
DeepLearningAI
Gradient Descent With Momentum (C2W2L06)
DeepLearningAI
Normalizing Activations in a Network (C2W3L04)
DeepLearningAI
Hyperparameter Tuning in Practice (C2W3L03)
DeepLearningAI
Adam Optimization Algorithm (C2W2L08)
DeepLearningAI
RMSProp (C2W2L07)
DeepLearningAI
Fitting Batch Norm Into Neural Networks (C2W3L05)
DeepLearningAI
Why Does Batch Norm Work? (C2W3L06)
DeepLearningAI
Batch Norm At Test Time (C2W3L07)
DeepLearningAI
Softmax Regression (C2W3L08)
DeepLearningAI
Deep Learning Frameworks (C2W3L10)
DeepLearningAI
Neural Network Overview (C1W3L01)
DeepLearningAI
Training Softmax Classifier (C2W3L09)
DeepLearningAI
Why Deep Representations? (C1W4L04)
DeepLearningAI
Gradient Descent For Neural Networks (C1W3L09)
DeepLearningAI
Neural Network Representations (C1W3L02)
DeepLearningAI
TensorFlow (C2W3L11)
DeepLearningAI
Activation Functions (C1W3L06)
DeepLearningAI
Explanation For Vectorized Implementation (C1W3L05)
DeepLearningAI
Getting Matrix Dimensions Right (C1W4L03)
DeepLearningAI
Understanding Dropout (C2W1L07)
DeepLearningAI
Building Blocks of a Deep Neural Network (C1W4L05)
DeepLearningAI
Why Non-linear Activation Functions (C1W3L07)
DeepLearningAI
Computing Neural Network Output (C1W3L03)
DeepLearningAI
Backpropagation Intuition (C1W3L10)
DeepLearningAI
Train/Dev/Test Sets (C2W1L01)
DeepLearningAI
Deep L-Layer Neural Network (C1W4L01)
DeepLearningAI
Random Initialization (C1W3L11)
DeepLearningAI
Other Regularization Methods (C2W1L08)
DeepLearningAI
Normalizing Inputs (C2W1L09)
DeepLearningAI
Derivatives Of Activation Functions (C1W3L08)
DeepLearningAI
Parameters vs Hyperparameters (C1W4L07)
DeepLearningAI
Vectorizing Across Multiple Examples (C1W3L04)
DeepLearningAI
What does this have to do with the brain? (C1W4L08)
DeepLearningAI
Dropout Regularization (C2W1L06)
DeepLearningAI
Vanishing/Exploding Gradients (C2W1L10)
DeepLearningAI
Basic Recipe for Machine Learning (C2W1L03)
DeepLearningAI
Bias/Variance (C2W1L02)
DeepLearningAI
Forward Propagation in a Deep Network (C1W4L02)
DeepLearningAI
Weight Initialization in a Deep Network (C2W1L11)
DeepLearningAI
Numerical Approximations of Gradients (C2W1L12)
DeepLearningAI
Regularization (C2W1L04)
DeepLearningAI
Why Regularization Reduces Overfitting (C2W1L05)
DeepLearningAI
Related AI Lessons
โก
โก
โก
โก
The Threshold Is a Business Decision, Not a Statistical One
Medium ยท Machine Learning
Can Your Stress Level Predict How Much You Sleep?
Medium ยท Machine Learning
Role of Model Architecture In Inference โ Inference Series
Medium ยท Machine Learning
Role of Model Architecture In Inference โ Inference Series
Medium ยท Deep Learning
๐
Tutor Explanation
DeepCamp AI