Learning Deep Learning From Perceptron to Large Language Models

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Introduction

001. Learning Deep Learning Introduction.mp4

02:53

Lesson 1 Deep Learning Introduction

001. Topics.mp4

00:20

002. 1.1 Deep Learning and Its History.mp4

05:49

003. 1.2 Prerequisites.mp4

05:29

Lesson 2 Neural Network Fundamentals I

001. Topics.mp4

01:31

002. 2.1 The Perceptron and Its Learning Algorithm.mp4

10:07

003. 2.2 Programming Example Perceptron.mp4

07:27

004. 2.3 Understanding the Bias Term.mp4

02:24

005. 2.4 Matrix and Vector Notation for Neural Networks.mp4

07:48

006. 2.5 Perceptron Limitations.mp4

09:39

007. 2.6 Solving Learning Problem with Gradient Descent.mp4

12:06

008. 2.7 Computing Gradient with the Chain Rule.mp4

15:41

009. 2.8 The Backpropagation Algorithm.mp4

08:05

010. 2.9 Programming Example Learning the XOR Function.mp4

14:34

011. 2.10 What Activation Function to Use.mp4

02:31

012. 2.11 Lesson 2 Summary.mp4

03:00

Lesson 3 Neural Network Fundamentals II

001. Topics.mp4

01:45

002. 3.1 Datasets and Generalization.mp4

08:13

003. 3.2 Multiclass Classification.mp4

05:51

004. 3.3 Programming Example Digit Classification with Python.mp4

17:09

005. 3.4 DL Frameworks.mp4

01:56

006. 3.5 Programming Example Digit Classification with TensorFlow.mp4

06:07

007. 3.6 Programming Example Digit Classification with PyTorch.mp4

11:42

008. 3.7 Avoiding Saturating Neurons and Vanishing GradientsPart I.mp4

09:15

009. 3.8 Avoiding Saturating Neurons and Vanishing GradientsPart II.mp4

12:29

010. 3.9 Variations on Gradient Descent.mp4

03:32

011. 3.10 Programming Example Improved Digit Classification with TensorFlow.mp4

02:34

012. 3.11 Programming Example Improved Digit Classification with PyTorch.mp4

05:15

013. 3.12 Problem Types, Output Units, and Loss Functions.mp4

07:26

014. 3.13 Regularization Techniques.mp4

03:37

015. 3.14 Programming Example Regression Problem with TensorFlow.mp4

07:19

016. 3.15 Programming Example Regression Problem with PyTorch.mp4

08:37

017. 3.16 Lesson 3 Summary.mp4

03:05

Lesson 4 Convolutional Neural Networks (CNN) and Image Classification

001. Topics.mp4

01:08

002. 4.1 The CIFAR-10 Dataset.mp4

04:09

003. 4.2 Convolutional Layer.mp4

09:07

004. 4.3 Building a Convolutional Neural Network.mp4

14:25

005. 4.4 Programming Example Image Classification Using CNN with TensorF.mp4

08:59

006. 4.5 Programming Example Image Classification Using CNN with PyTorch.mp4

09:17

007. 4.6 AlexNet.mp4

05:44

008. 4.7 VGGNet.mp4

05:18

009. 4.8 GoogLeNet.mp4

05:14

010. 4.9 ResNet.mp4

06:21

011. 4.10 Programming Example Using a Pretrained Network with TensorFlow.mp4

03:52

012. 4.11 Programming Example Using a Pretrained Network with PyTorch.mp4

04:51

013. 4.12 Transfer Learning.mp4

03:58

014. 4.13 Efficient CNNs.mp4

03:46

015. 4.14 Lesson 4 Summary.mp4

02:40

Lesson 5 Recurrent Neural Networks (RNN) and Time Series Prediction

001. Topics.mp4

01:13

002. 5.1 Problem Types Involving Sequential Data.mp4

07:09

003. 5.2 Recurrent Neural Networks.mp4

08:22

004. 5.3 Programming Example Forecasting Book Sales with TensorFlow.mp4

09:26

005. 5.4 Programming Example Forecasting Book Sales with PyTorch.mp4

10:31

006. 5.5 Backpropagation Through Time and Keeping Gradients Healthy.mp4

08:35

007. 5.6 Long Short-Term Memory.mp4

09:47

008. 5.7 Autoregression and Beam Search.mp4

06:43

009. 5.8 Programming Example Text Autocompletion with TensorFlow.mp4

14:10

010. 5.9 Programming Example Text Autocompletion with PyTorch.mp4

15:40

011. 5.10 Lesson 5 Summary.mp4

01:53

Lesson 6 Neural Language Models and Word Embeddings

001. Topics.mp4

01:01

002. 6.1 Language Models.mp4

13:05

003. 6.2 Word Embeddings.mp4

12:09

004. 6.3 Programming Example Language Model and Word Embeddings with TensorFlow.mp4

12:21

005. 6.4 Programming Example Language Model and Word Embeddings with PyTorch.mp4

11:22

006. 6.5 Word2vec.mp4

06:25

007. 6.6 Programming Example Using Pretrained GloVe Embeddings.mp4

06:47

008. 6.7 Handling Out-of-Vocabulary Words with Wordpieces.mp4

03:16

009. 6.8 Lesson 6 Summary.mp4

01:44

Lesson 7 EncoderDecoder Networks, Attention, Transformers, and Neural Machine Translation

001. Topics.mp4

01:02

002. 7.1 EncoderDecoder Network for Neural Machine.mp4

04:28

003. 7.2 Programming Example Neural Machine Transla.mp4

24:25

004. 7.3 Programming Example Neural Machine Transla.mp4

21:51

005. 7.4 Attention.mp4

08:40

006. 7.5 The Transformer.mp4

09:03

007. 7.6 Programming Example Machine Translation Us.mp4

07:02

008. 7.7 Programming Example Machine Translation Us.mp4

08:22

009. 7.8 Lesson 7 Summary.mp4

01:30

Lesson 8 Large Language Models

001. Topics.mp4

01:08

002. 8.1 Overview of BERT.mp4

10:00

003. 8.2 Overview of GPT.mp4

07:16

004. 8.3 From GPT to GPT4.mp4

17:19

005. 8.4 Handling Chat History.mp4

05:45

006. 8.5 Prompt Tuning.mp4

08:50

007. 8.6 Retrieving Data and Using Tools.mp4

08:24

008. 8.7 Open Datasets and Models.mp4

05:50

009. 8.8 Demo Large Language Model Prompting.mp4

06:10

010. 8.9 Lesson 8 Summary.mp4

01:30

Lesson 9 Multi-Modal Networks and Image Captioning

001. Topics.mp4

00:56

002. 9.1 Multimodal learning.mp4

08:20

003. 9.2 Programming Example Multimodal Classification with TensorFlow.mp4

07:36

004. 9.3 Programming Example Multimodal Classification with PyTorch.mp4

06:41

005. 9.4 Image Captioning with Attention.mp4

05:34

006. 9.5 Programming Example Image Captioning with TensorFlow.mp4

17:35

007. 9.6 Programming Example Image Captioning with PyTorch.mp4

18:51

008. 9.7 Multimodal Large Language Models.mp4

20:19

009. 9.8 Lesson 9 Summary.mp4

01:31

Lesson 10 Multi-Task Learning and Computer Vision Beyond Classification

001. Topics.mp4

01:05

002. 10.1 Multitask Learning.mp4

05:52

003. 10.2 Programming Example Multitask Learning with TensorFlow.mp4

04:42

004. 10.3 Programming Example Multitask Learning with PyTorch.mp4

05:39

005. 10.4 Object Detection with R-CNN.mp4

06:39

006. 10.5 Improved Object Detection with Fast and Faster R-CNN.mp4

05:30

007. 10.6 Segmentation with Deconvolution Network and U-Net.mp4

08:32

008. 10.7 Instance Segmentation with Mask R-CNN.mp4

02:34

009. 10.8 Lesson 10 Summary.mp4

01:41

Lesson 11 Applying Deep Learning

001. Topics.mp4

00:44

002. 11.1 Ethical AI and Data Ethics.mp4

19:04

003. 11.2 Process for Tuning a Network.mp4

06:18

004. 11.3 Further Studies.mp4

04:37

Summary

001. Learning Deep Learning Summary.mp4

08:23

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