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种子名称： Learning Deep Learning From Perceptron to Large Language Models
文件类型： 视频
文件数目： 110个文件
文件大小： 2.76 GB
收录时间： 2024-3-2 22:15
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最近下载： 2024-5-22 09:49

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Learning Deep Learning From Perceptron to Large Language Models.torrent

Lesson 7 Encoder–Decoder Networks, Attention, Transformers, and Neural Machine Translation/003. 7.2 Programming Example Neural Machine Translation with TensorFlow.mp4108.81MB

Lesson 1 Deep Learning Introduction/001. Topics.mp41.17MB

Lesson 1 Deep Learning Introduction/002. 1.1 Deep Learning and Its History.mp417.56MB

Lesson 1 Deep Learning Introduction/003. 1.2 Prerequisites.mp415.83MB

Lesson 2 Neural Network Fundamentals I/001. Topics.mp46.05MB

Lesson 2 Neural Network Fundamentals I/002. 2.1 The Perceptron and Its Learning Algorithm.mp429.72MB

Lesson 2 Neural Network Fundamentals I/003. 2.2 Programming Example Perceptron.mp427.52MB

Lesson 2 Neural Network Fundamentals I/004. 2.3 Understanding the Bias Term.mp46.59MB

Lesson 2 Neural Network Fundamentals I/005. 2.4 Matrix and Vector Notation for Neural Networks.mp420.72MB

Lesson 2 Neural Network Fundamentals I/006. 2.5 Perceptron Limitations.mp427.67MB

Lesson 2 Neural Network Fundamentals I/007. 2.6 Solving Learning Problem with Gradient Descent.mp435.66MB

Lesson 2 Neural Network Fundamentals I/008. 2.7 Computing Gradient with the Chain Rule.mp441.79MB

Lesson 2 Neural Network Fundamentals I/009. 2.8 The Backpropagation Algorithm.mp421.41MB

Lesson 2 Neural Network Fundamentals I/010. 2.9 Programming Example Learning the XOR Function.mp459.64MB

Lesson 2 Neural Network Fundamentals I/011. 2.10 What Activation Function to Use.mp46.65MB

Lesson 2 Neural Network Fundamentals I/012. 2.11 Lesson 2 Summary.mp48.81MB

Lesson 3 Neural Network Fundamentals II/001. Topics.mp47.04MB

Lesson 3 Neural Network Fundamentals II/002. 3.1 Datasets and Generalization.mp425.28MB

Lesson 3 Neural Network Fundamentals II/003. 3.2 Multiclass Classification.mp417.87MB

Lesson 3 Neural Network Fundamentals II/004. 3.3 Programming Example Digit Classification with Python.mp473.67MB

Lesson 3 Neural Network Fundamentals II/005. 3.4 DL Frameworks.mp45MB

Lesson 3 Neural Network Fundamentals II/006. 3.5 Programming Example Digit Classification with TensorFlow.mp425.37MB

Lesson 3 Neural Network Fundamentals II/007. 3.6 Programming Example Digit Classification with PyTorch.mp449.69MB

Lesson 3 Neural Network Fundamentals II/008. 3.7 Avoiding Saturating Neurons and Vanishing Gradients—Part I.mp426.06MB

Lesson 3 Neural Network Fundamentals II/009. 3.8 Avoiding Saturating Neurons and Vanishing Gradients—Part II.mp434.03MB

Lesson 3 Neural Network Fundamentals II/010. 3.9 Variations on Gradient Descent.mp411.74MB

Lesson 3 Neural Network Fundamentals II/011. 3.10 Programming Example Improved Digit Classification with TensorFlow.mp411.67MB

Lesson 3 Neural Network Fundamentals II/012. 3.11 Programming Example Improved Digit Classification with PyTorch.mp422.91MB

Lesson 3 Neural Network Fundamentals II/013. 3.12 Problem Types, Output Units, and Loss Functions.mp420.13MB

Lesson 3 Neural Network Fundamentals II/014. 3.13 Regularization Techniques.mp49.13MB

Lesson 3 Neural Network Fundamentals II/015. 3.14 Programming Example Regression Problem with TensorFlow.mp436.16MB

Lesson 3 Neural Network Fundamentals II/016. 3.15 Programming Example Regression Problem with PyTorch.mp445.08MB

Lesson 3 Neural Network Fundamentals II/017. 3.16 Lesson 3 Summary.mp49.52MB

Lesson 4 Convolutional Neural Networks (CNN) and Image Classification/001. Topics.mp44.77MB

Lesson 4 Convolutional Neural Networks (CNN) and Image Classification/002. 4.1 The CIFAR-10 Dataset.mp413.91MB

Lesson 4 Convolutional Neural Networks (CNN) and Image Classification/003. 4.2 Convolutional Layer.mp425.5MB

Lesson 4 Convolutional Neural Networks (CNN) and Image Classification/004. 4.3 Building a Convolutional Neural Network.mp443.65MB

Lesson 4 Convolutional Neural Networks (CNN) and Image Classification/005. 4.4 Programming Example Image Classification Using CNN with TensorFlow.mp438.52MB

Lesson 4 Convolutional Neural Networks (CNN) and Image Classification/006. 4.5 Programming Example Image Classification Using CNN with PyTorch.mp440.05MB

Lesson 4 Convolutional Neural Networks (CNN) and Image Classification/007. 4.6 AlexNet.mp415.18MB

Lesson 4 Convolutional Neural Networks (CNN) and Image Classification/008. 4.7 VGGNet.mp417.89MB

Lesson 4 Convolutional Neural Networks (CNN) and Image Classification/009. 4.8 GoogLeNet.mp416.62MB

Lesson 4 Convolutional Neural Networks (CNN) and Image Classification/010. 4.9 ResNet.mp419.35MB

Lesson 4 Convolutional Neural Networks (CNN) and Image Classification/011. 4.10 Programming Example Using a Pretrained Network with TensorFlow.mp417.04MB

Lesson 4 Convolutional Neural Networks (CNN) and Image Classification/012. 4.11 Programming Example Using a Pretrained Network with PyTorch.mp419.7MB

Lesson 4 Convolutional Neural Networks (CNN) and Image Classification/013. 4.12 Transfer Learning.mp411.6MB

Lesson 4 Convolutional Neural Networks (CNN) and Image Classification/014. 4.13 Efficient CNNs.mp411.5MB

Lesson 4 Convolutional Neural Networks (CNN) and Image Classification/015. 4.14 Lesson 4 Summary.mp47.53MB

Lesson 5 Recurrent Neural Networks (RNN) and Time Series Prediction/001. Topics.mp44.97MB

Lesson 5 Recurrent Neural Networks (RNN) and Time Series Prediction/002. 5.1 Problem Types Involving Sequential Data.mp419.68MB

Lesson 5 Recurrent Neural Networks (RNN) and Time Series Prediction/003. 5.2 Recurrent Neural Networks.mp425.12MB

Lesson 5 Recurrent Neural Networks (RNN) and Time Series Prediction/004. 5.3 Programming Example Forecasting Book Sales with TensorFlow.mp440.61MB

Lesson 5 Recurrent Neural Networks (RNN) and Time Series Prediction/005. 5.4 Programming Example Forecasting Book Sales with PyTorch.mp445.53MB

Lesson 5 Recurrent Neural Networks (RNN) and Time Series Prediction/006. 5.5 Backpropagation Through Time and Keeping Gradients Healthy.mp424.75MB

Lesson 5 Recurrent Neural Networks (RNN) and Time Series Prediction/007. 5.6 Long Short-Term Memory.mp427.94MB

Lesson 5 Recurrent Neural Networks (RNN) and Time Series Prediction/008. 5.7 Autoregression and Beam Search.mp418.49MB

Lesson 5 Recurrent Neural Networks (RNN) and Time Series Prediction/009. 5.8 Programming Example Text Autocompletion with TensorFlow.mp460.08MB

Lesson 5 Recurrent Neural Networks (RNN) and Time Series Prediction/010. 5.9 Programming Example Text Autocompletion with PyTorch.mp464.76MB

Lesson 5 Recurrent Neural Networks (RNN) and Time Series Prediction/011. 5.10 Lesson 5 Summary.mp45.69MB

Lesson 6 Neural Language Models and Word Embeddings/001. Topics.mp44.1MB

Lesson 6 Neural Language Models and Word Embeddings/002. 6.1 Language Models.mp436.29MB

Lesson 6 Neural Language Models and Word Embeddings/003. 6.2 Word Embeddings.mp435.93MB

Lesson 6 Neural Language Models and Word Embeddings/004. 6.3 Programming Example Language Model and Word Embeddings with TensorFlow.mp454.37MB

Lesson 6 Neural Language Models and Word Embeddings/005. 6.4 Programming Example Language Model and Word Embeddings with PyTorch.mp446.08MB

Lesson 6 Neural Language Models and Word Embeddings/006. 6.5 Word2vec.mp419.05MB

Lesson 6 Neural Language Models and Word Embeddings/007. 6.6 Programming Example Using Pretrained GloVe Embeddings.mp425.15MB

Lesson 6 Neural Language Models and Word Embeddings/008. 6.7 Handling Out-of-Vocabulary Words with Wordpieces.mp49.61MB

Lesson 6 Neural Language Models and Word Embeddings/009. 6.8 Lesson 6 Summary.mp44.84MB

Lesson 7 Encoder–Decoder Networks, Attention, Transformers, and Neural Machine Translation/001. Topics.mp44.13MB

Lesson 7 Encoder–Decoder Networks, Attention, Transformers, and Neural Machine Translation/002. 7.1 Encoder–Decoder Network for Neural Machine Translation.mp412.68MB

Introduction/001. Learning Deep Learning Introduction.mp411.34MB

Lesson 7 Encoder–Decoder Networks, Attention, Transformers, and Neural Machine Translation/004. 7.3 Programming Example Neural Machine Translation with PyTorch.mp499.88MB

Lesson 7 Encoder–Decoder Networks, Attention, Transformers, and Neural Machine Translation/005. 7.4 Attention.mp425.09MB

Lesson 7 Encoder–Decoder Networks, Attention, Transformers, and Neural Machine Translation/006. 7.5 The Transformer.mp426.04MB

Lesson 7 Encoder–Decoder Networks, Attention, Transformers, and Neural Machine Translation/007. 7.6 Programming Example Machine Translation Using Transformer with Te.mp436.09MB

Lesson 7 Encoder–Decoder Networks, Attention, Transformers, and Neural Machine Translation/008. 7.7 Programming Example Machine Translation Using Transformer with Py.mp440.98MB

Lesson 7 Encoder–Decoder Networks, Attention, Transformers, and Neural Machine Translation/009. 7.8 Lesson 7 Summary.mp44.58MB

Lesson 8 Large Language Models/001. Topics.mp44.22MB

Lesson 8 Large Language Models/002. 8.1 Overview of BERT.mp427.97MB

Lesson 8 Large Language Models/003. 8.2 Overview of GPT.mp420.27MB

Lesson 8 Large Language Models/004. 8.3 From GPT to GPT4.mp452.16MB

Lesson 8 Large Language Models/005. 8.4 Handling Chat History.mp416.32MB

Lesson 8 Large Language Models/006. 8.5 Prompt Tuning.mp425.07MB

Lesson 8 Large Language Models/007. 8.6 Retrieving Data and Using Tools.mp426.41MB

Lesson 8 Large Language Models/008. 8.7 Open Datasets and Models.mp415.61MB

Lesson 8 Large Language Models/009. 8.8 Demo Large Language Model Prompting.mp425.74MB

Lesson 8 Large Language Models/010. 8.9 Lesson 8 Summary.mp44.07MB

Lesson 9 Multi-Modal Networks and Image Captioning/001. Topics.mp43.83MB

Lesson 9 Multi-Modal Networks and Image Captioning/002. 9.1 Multimodal learning.mp422.61MB

Lesson 9 Multi-Modal Networks and Image Captioning/003. 9.2 Programming Example Multimodal Classification with TensorFlow.mp434.45MB

Lesson 9 Multi-Modal Networks and Image Captioning/004. 9.3 Programming Example Multimodal Classification with PyTorch.mp433.73MB

Lesson 9 Multi-Modal Networks and Image Captioning/005. 9.4 Image Captioning with Attention.mp417.89MB

Lesson 9 Multi-Modal Networks and Image Captioning/006. 9.5 Programming Example Image Captioning with TensorFlow.mp481.18MB

Lesson 9 Multi-Modal Networks and Image Captioning/007. 9.6 Programming Example Image Captioning with PyTorch.mp482.29MB

Lesson 9 Multi-Modal Networks and Image Captioning/008. 9.7 Multimodal Large Language Models.mp460.21MB

Lesson 9 Multi-Modal Networks and Image Captioning/009. 9.8 Lesson 9 Summary.mp44.23MB

Lesson 10 Multi-Task Learning and Computer Vision Beyond Classification/001. Topics.mp44.44MB

Lesson 10 Multi-Task Learning and Computer Vision Beyond Classification/002. 10.1 Multitask Learning.mp417.4MB

Lesson 10 Multi-Task Learning and Computer Vision Beyond Classification/003. 10.2 Programming Example Multitask Learning with TensorFlow.mp422.4MB

Lesson 10 Multi-Task Learning and Computer Vision Beyond Classification/004. 10.3 Programming Example Multitask Learning with PyTorch.mp430.05MB

Lesson 10 Multi-Task Learning and Computer Vision Beyond Classification/005. 10.4 Object Detection with R-CNN.mp420.46MB

Lesson 10 Multi-Task Learning and Computer Vision Beyond Classification/006. 10.5 Improved Object Detection with Fast and Faster R-CNN.mp414.62MB

Lesson 10 Multi-Task Learning and Computer Vision Beyond Classification/007. 10.6 Segmentation with Deconvolution Network and U-Net.mp424.82MB

Lesson 10 Multi-Task Learning and Computer Vision Beyond Classification/008. 10.7 Instance Segmentation with Mask R-CNN.mp49.55MB

Lesson 10 Multi-Task Learning and Computer Vision Beyond Classification/009. 10.8 Lesson 10 Summary.mp44.39MB

Lesson 11 Applying Deep Learning/001. Topics.mp42.66MB

Lesson 11 Applying Deep Learning/002. 11.1 Ethical AI and Data Ethics.mp452.92MB

Lesson 11 Applying Deep Learning/003. 11.2 Process for Tuning a Network.mp416.32MB

Lesson 11 Applying Deep Learning/004. 11.3 Further Studies.mp411.98MB

Summary/001. Learning Deep Learning Summary.mp433.36MB