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دسته بندی دوره ها

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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    تاریخ انتشار: ۱۰ مرداد ۱۴۰۳
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