| 001. Optimizing neural networks.en.srt | 1.3 KB | ||
| 001. Optimizing neural networks.mp4 | 7.4 MB | ||
| 002. Prerequisites for the course.en.srt | 4.3 KB | ||
| 002. Prerequisites for the course.mp4 | 19.7 MB | ||
| 003. Setting up exercise files.en.srt | 3.3 KB | ||
| 003. Setting up exercise files.mp4 | 20.3 MB | ||
| 004. What is deep learning.en.srt | 2.6 KB | ||
| 004. What is deep learning.mp4 | 14.4 MB | ||
| 005. Review of artificial neural networks.en.srt | 4.1 KB | ||
| 005. Review of artificial neural networks.mp4 | 21.7 MB | ||
| 006. An ANN model.en.srt | 2.4 KB | ||
| 006. An ANN model.mp4 | 13.6 MB | ||
| 007. Model optimization and tuning.en.srt | 2.4 KB | ||
| 007. Model optimization and tuning.mp4 | 13 MB | ||
| 008. The deep learning tuning process.en.srt | 5 KB | ||
| 008. The deep learning tuning process.mp4 | 25.1 MB | ||
| 009. Experiment setups for the course.en.srt | 3.2 KB | ||
| 009. Experiment setups for the course.mp4 | 17.9 MB | ||
| 010. Epoch and batch size tuning.en.srt | 3.2 KB | ||
| 010. Epoch and batch size tuning.mp4 | 16.8 MB | ||
| 011. Epoch and batch size experiment.en.srt | 4.8 KB | ||
| 011. Epoch and batch size experiment.mp4 | 26.1 MB | ||
| 012. Hidden layers tuning.en.srt | 3.2 KB | ||
| 012. Hidden layers tuning.mp4 | 16.9 MB | ||
| 013. Determining nodes in a layer.en.srt | 3.4 KB | ||
| 013. Determining nodes in a layer.mp4 | 17.2 MB | ||
| 014. Choosing activation functions.en.srt | 3.2 KB | ||
| 014. Choosing activation functions.mp4 | 17.2 MB | ||
| 015. Initializing weights.en.srt | 2.8 KB | ||
| 015. Initializing weights.mp4 | 14.9 MB | ||
| 016. Vanishing and exploding gradients.en.srt | 4 KB | ||
| 016. Vanishing and exploding gradients.mp4 | 20.5 MB | ||
| 017. Batch normalization.en.srt | 3 KB | ||
| 017. Batch normalization.mp4 | 15.7 MB | ||
| 018. Optimizers.en.srt | 2.4 KB | ||
| 018. Optimizers.mp4 | 12.2 MB | ||
| 019. Optimizer experiment.en.srt | 2.1 KB | ||
| 019. Optimizer experiment.mp4 | 10.5 MB | ||
| 020. Learning rate.en.srt | 1.9 KB | ||
| 020. Learning rate.mp4 | 10.2 MB | ||
| 021. Learning rate experiment.en.srt | 1.8 KB | ||
| 021. Learning rate experiment.mp4 | 10.5 MB | ||
| 022. Overfitting in ANNs.en.srt | 3.1 KB | ||
| 022. Overfitting in ANNs.mp4 | 15.7 MB | ||
| 023. Regularization.en.srt | 1.3 KB | ||
| 023. Regularization.mp4 | 7.3 MB | ||
| 024. Regularization experiment.en.srt | 1.3 KB | ||
| 024. Regularization experiment.mp4 | 7.3 MB | ||
| 025. Dropouts.en.srt | 1.7 KB | ||
| 025. Dropouts.mp4 | 8.5 MB | ||
| 026. Dropout experiment.en.srt | 1.4 KB | ||
| 026. Dropout experiment.mp4 | 8.3 MB | ||
| 027. Tuning exercise Problem statement.en.srt | 6.5 KB | ||
| 027. Tuning exercise Problem statement.mp4 | 33.9 MB | ||
| 028. Acquire and process data.en.srt | 1.4 KB | ||
| 028. Acquire and process data.mp4 | 7.5 MB | ||
| 029. Tuning the network.en.srt | 1.9 KB | ||
| 029. Tuning the network.mp4 | 9 MB | ||
| 030. Tuning backpropagation.en.srt | 1.3 KB | ||
| 030. Tuning backpropagation.mp4 | 7.3 MB | ||
| 031. Avoiding overfitting.en.srt | 1.3 KB | ||
| 031. Avoiding overfitting.mp4 | 7.5 MB | ||
| 032. Building the final model.en.srt | 2.1 KB | ||
| 032. Building the final model.mp4 | 11.6 MB | ||
| 033. Continuing your deep learning journey.en.srt | 1.1 KB | ||
| 033. Continuing your deep learning journey.mp4 | 6 MB | ||
| Bonus Resources.txt | 409.6 B | ||
| Common_Experiment_Functions.ipynb | 21.1 KB | ||
| Get Bonus Downloads Here.url | 204.8 B | ||
| code_02_XX Tuning the Deep Learning Network.ipynb | 346.8 KB | ||
| code_03_XX Tuning Back Propagation.ipynb | 197.8 KB | ||
| code_04_XX Overfitting Management.ipynb | 128.6 KB | ||
| code_05_XX Incident Root Cause Analysis Exercise.ipynb | 420.4 KB | ||
| iris.csv | 3.8 KB | ||
| root_cause_analysis.csv | 31.9 KB | ||
| ▲ 75 total files | |||
Deep Learning: Model Optimization and Tuning
https://TutGee.com
LinkedIn Learning
Duration: 54m 22s | .MP4 1280x720, 30 fps(r) | AAC, 48000 Hz, 2ch | 472 MB
Genre: eLearning | Language: English
Deep Learning as a technology has grown leaps and bounds in the last few years. More and more AI solutions use Deep Learning as their foundational technology. Studying this technology, however, presents several challenges. IT professionals from varying backgrounds need a simplified resource to learn the concepts and build models quickly. In this course, instructor Kumaran Ponnambalam provides a simplified path to understand various optimization and tuning options available for deep learning models and shows you how to use these options to improve models. He begins by reviewing Deep Learning, including artificial neural networks and architectures. Next, Kumaran discusses the process of hyper parameter tuning. He examines the building blocks of neural networks and the levers available to tune them. Kumaran offers recommendations and best practices. Then he concludes with an end-to-end tuning example.
| torrent name | size | uploader | age | seed | leech |
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| 283.3 MB | freecoursewb | 2 years | 0 | 1 | |
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| 449.43 MB | Nawel087 | 2 years | 9 | 0 | |
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Linkedin-Comprendre l'impact des vidéos deep fake.2021.FR.WEB-DL.720p.H264 Posted by
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