Learning Deep Learning From Perceptron to Large Language Models

سرفصل های دوره

Introduction

001. Learning Deep Learning Introduction

Lesson 1 Deep Learning Introduction

001. Topics

002. 1.1 Deep Learning and Its History

003. 1.2 Prerequisites

Lesson 2 Neural Network Fundamentals I

001. Topics

002. 2.1 The Perceptron and Its Learning Algorithm

003. 2.2 Programming Example Perceptron

004. 2.3 Understanding the Bias Term

005. 2.4 Matrix and Vector Notation for Neural Networks

006. 2.5 Perceptron Limitations

007. 2.6 Solving Learning Problem with Gradient Descent

008. 2.7 Computing Gradient with the Chain Rule

009. 2.8 The Backpropagation Algorithm

010. 2.9 Programming Example Learning the XOR Function

011. 2.10 What Activation Function to Use

012. 2.11 Lesson 2 Summary

Lesson 3 Neural Network Fundamentals II

001. Topics

002. 3.1 Datasets and Generalization

003. 3.2 Multiclass Classification

004. 3.3 Programming Example Digit Classification with Python

005. 3.4 DL Frameworks

006. 3.5 Programming Example Digit Classification with TensorFlow

007. 3.6 Programming Example Digit Classification with PyTorch

008. 3.7 Avoiding Saturating Neurons and Vanishing GradientsPart I

009. 3.8 Avoiding Saturating Neurons and Vanishing GradientsPart II

010. 3.9 Variations on Gradient Descent

011. 3.10 Programming Example Improved Digit Classification with TensorFlow

012. 3.11 Programming Example Improved Digit Classification with PyTorch

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

014. 3.13 Regularization Techniques

015. 3.14 Programming Example Regression Problem with TensorFlow

016. 3.15 Programming Example Regression Problem with PyTorch

017. 3.16 Lesson 3 Summary

Lesson 4 Convolutional Neural Networks (CNN) and Image Classification

001. Topics

002. 4.1 The CIFAR-10 Dataset

003. 4.2 Convolutional Layer

004. 4.3 Building a Convolutional Neural Network

005. 4.4 Programming Example Image Classification Using CNN with TensorF

006. 4.5 Programming Example Image Classification Using CNN with PyTorch

007. 4.6 AlexNet

008. 4.7 VGGNet

009. 4.8 GoogLeNet

010. 4.9 ResNet

011. 4.10 Programming Example Using a Pretrained Network with TensorFlow

012. 4.11 Programming Example Using a Pretrained Network with PyTorch

013. 4.12 Transfer Learning

014. 4.13 Efficient CNNs

015. 4.14 Lesson 4 Summary

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

001. Topics

002. 5.1 Problem Types Involving Sequential Data

003. 5.2 Recurrent Neural Networks

004. 5.3 Programming Example Forecasting Book Sales with TensorFlow

005. 5.4 Programming Example Forecasting Book Sales with PyTorch

006. 5.5 Backpropagation Through Time and Keeping Gradients Healthy

007. 5.6 Long Short-Term Memory

008. 5.7 Autoregression and Beam Search

009. 5.8 Programming Example Text Autocompletion with TensorFlow

010. 5.9 Programming Example Text Autocompletion with PyTorch

011. 5.10 Lesson 5 Summary

Lesson 6 Neural Language Models and Word Embeddings

001. Topics

002. 6.1 Language Models

003. 6.2 Word Embeddings

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

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

006. 6.5 Word2vec

007. 6.6 Programming Example Using Pretrained GloVe Embeddings

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

009. 6.8 Lesson 6 Summary

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

001. Topics

002. 7.1 EncoderDecoder Network for Neural Machine

003. 7.2 Programming Example Neural Machine Transla

004. 7.3 Programming Example Neural Machine Transla

005. 7.4 Attention

006. 7.5 The Transformer

007. 7.6 Programming Example Machine Translation Us

008. 7.7 Programming Example Machine Translation Us

009. 7.8 Lesson 7 Summary

Lesson 8 Large Language Models

001. Topics

002. 8.1 Overview of BERT

003. 8.2 Overview of GPT

004. 8.3 From GPT to GPT4

005. 8.4 Handling Chat History

006. 8.5 Prompt Tuning

007. 8.6 Retrieving Data and Using Tools

008. 8.7 Open Datasets and Models

009. 8.8 Demo Large Language Model Prompting

010. 8.9 Lesson 8 Summary

Lesson 9 Multi-Modal Networks and Image Captioning

001. Topics

002. 9.1 Multimodal learning

003. 9.2 Programming Example Multimodal Classification with TensorFlow

004. 9.3 Programming Example Multimodal Classification with PyTorch

005. 9.4 Image Captioning with Attention

006. 9.5 Programming Example Image Captioning with TensorFlow

007. 9.6 Programming Example Image Captioning with PyTorch

008. 9.7 Multimodal Large Language Models

009. 9.8 Lesson 9 Summary

Lesson 10 Multi-Task Learning and Computer Vision Beyond Classification

001. Topics

002. 10.1 Multitask Learning

003. 10.2 Programming Example Multitask Learning with TensorFlow

004. 10.3 Programming Example Multitask Learning with PyTorch

005. 10.4 Object Detection with R-CNN

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

007. 10.6 Segmentation with Deconvolution Network and U-Net

008. 10.7 Instance Segmentation with Mask R-CNN

009. 10.8 Lesson 10 Summary

Lesson 11 Applying Deep Learning

001. Topics

002. 11.1 Ethical AI and Data Ethics

003. 11.2 Process for Tuning a Network

004. 11.3 Further Studies

Summary

001. Learning Deep Learning Summary

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