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Language:
English
Total Size:
2.5 GB
Info Hash:
5A721563EDF334B9AFDEB08A4CE0C5A00D9666D0
Added By:
Added:
Nov. 13, 2025, 12:23 a.m.
Stats:
|
(Last updated: Nov. 13, 2025, 12:45 a.m.)
| File | Size |
|---|---|
| Get Bonus Downloads Here.url | 180 bytes |
| 001. Chapter 1. What is deep learning.en.srt | 2.3 KB |
| 001. Chapter 1. What is deep learning.mp4 | 5.0 MB |
| 002. Chapter 1. Artificial intelligence.en.srt | 3.8 KB |
| 002. Chapter 1. Artificial intelligence.mp4 | 7.5 MB |
| 003. Chapter 1. Machine learning.en.srt | 6.1 KB |
| 003. Chapter 1. Machine learning.mp4 | 12.6 MB |
| 004. Chapter 1. Learning rules and representations from data.en.srt | 9.6 KB |
| 004. Chapter 1. Learning rules and representations from data.mp4 | 17.0 MB |
| 005. Chapter 1. The deep in deep learning .en.srt | 4.5 KB |
| 005. Chapter 1. The deep in deep learning .mp4 | 9.8 MB |
| 006. Chapter 1. Understanding how deep learning works, in three figures.en.srt | 4.3 KB |
| 006. Chapter 1. Understanding how deep learning works, in three figures.mp4 | 6.9 MB |
| 007. Chapter 1. Understanding how deep learning works, in three figures.en.srt | 3.7 KB |
| 007. Chapter 1. Understanding how deep learning works, in three figures.mp4 | 7.9 MB |
| 008. Chapter 1. The age of generative AI.en.srt | 3.0 KB |
| 008. Chapter 1. The age of generative AI.mp4 | 4.4 MB |
| 009. Chapter 1. What deep learning has achieved so far.en.srt | 2.7 KB |
| 009. Chapter 1. What deep learning has achieved so far.mp4 | 6.5 MB |
| 010. Chapter 1. Beware of the short-term hype.en.srt | 6.6 KB |
| 010. Chapter 1. Beware of the short-term hype.mp4 | 15.1 MB |
| 011. Chapter 1. Summer can turn to winter.en.srt | 4.3 KB |
| 011. Chapter 1. Summer can turn to winter.mp4 | 11.0 MB |
| 012. Chapter 1. The promise of AI.en.srt | 4.3 KB |
| 012. Chapter 1. The promise of AI.mp4 | 8.5 MB |
| 013. Chapter 2. The mathematical building blocks of neural networks.en.srt | 14.7 KB |
| 013. Chapter 2. The mathematical building blocks of neural networks.mp4 | 22.2 MB |
| 014. Chapter 2. Data representations for neural networks.en.srt | 17.7 KB |
| 014. Chapter 2. Data representations for neural networks.mp4 | 32.6 MB |
| 015. Chapter 2. The gears of neural networks - Tensor operations.en.srt | 23.8 KB |
| 015. Chapter 2. The gears of neural networks - Tensor operations.mp4 | 30.7 MB |
| 016. Chapter 2. The engine of neural networks - Gradient-based optimization.en.srt | 35.2 KB |
| 016. Chapter 2. The engine of neural networks - Gradient-based optimization.mp4 | 60.2 MB |
| 017. Chapter 2. Looking back at our first example.en.srt | 11.4 KB |
| 017. Chapter 2. Looking back at our first example.mp4 | 19.3 MB |
| 018. Chapter 2. Summary.en.srt | 2.9 KB |
| 018. Chapter 2. Summary.mp4 | 4.5 MB |
| 019. Chapter 3. Introduction to TensorFlow, PyTorch, JAX, and Keras.en.srt | 9.4 KB |
| 019. Chapter 3. Introduction to TensorFlow, PyTorch, JAX, and Keras.mp4 | 20.0 MB |
| 020. Chapter 3. How these frameworks relate to each other.en.srt | 3.0 KB |
| 020. Chapter 3. How these frameworks relate to each other.mp4 | 5.9 MB |
| 021. Chapter 3. Introduction to TensorFlow.en.srt | 21.2 KB |
| 021. Chapter 3. Introduction to TensorFlow.mp4 | 35.5 MB |
| 022. Chapter 3. Introduction to PyTorch.en.srt | 17.9 KB |
| 022. Chapter 3. Introduction to PyTorch.mp4 | 26.9 MB |
| 023. Chapter 3. Introduction to JAX.en.srt | 17.5 KB |
| 023. Chapter 3. Introduction to JAX.mp4 | 27.5 MB |
| 024. Chapter 3. Introduction to Keras.en.srt | 28.1 KB |
| 024. Chapter 3. Introduction to Keras.mp4 | 48.3 MB |
| 025. Chapter 3. Summary.en.srt | 1.3 KB |
| 025. Chapter 3. Summary.mp4 | 4.0 MB |
| 026. Chapter 4. Classification and regression.en.srt | 28.0 KB |
| 026. Chapter 4. Classification and regression.mp4 | 47.8 MB |
| 027. Chapter 4. Classifying newswires - A multiclass classification example.en.srt | 14.4 KB |
| 027. Chapter 4. Classifying newswires - A multiclass classification example.mp4 | 23.6 MB |
| 028. Chapter 4. Predicting house prices - A regression example.en.srt | 15.5 KB |
| 028. Chapter 4. Predicting house prices - A regression example.mp4 | 25.0 MB |
| 029. Chapter 4. Summary.en.srt | 1.4 KB |
| 029. Chapter 4. Summary.mp4 | 2.1 MB |
| 030. Chapter 5. Fundamentals of machine learning.en.srt | 32.7 KB |
| 030. Chapter 5. Fundamentals of machine learning.mp4 | 51.8 MB |
| 031. Chapter 5. Evaluating machine-learning models.en.srt | 14.6 KB |
| 031. Chapter 5. Evaluating machine-learning models.mp4 | 25.3 MB |
| 032. Chapter 5. Improving model fit.en.srt | 9.5 KB |
| 032. Chapter 5. Improving model fit.mp4 | 15.7 MB |
| 033. Chapter 5. Improving generalization.en.srt | 25.0 KB |
| 033. Chapter 5. Improving generalization.mp4 | 40.4 MB |
| 034. Chapter 5. Summary.en.srt | 2.9 KB |
| 034. Chapter 5. Summary.mp4 | 6.9 MB |
| 035. Chapter 6. The universal workflow of machine learning.en.srt | 30.1 KB |
| 035. Chapter 6. The universal workflow of machine learning.mp4 | 60.2 MB |
| 036. Chapter 6. Developing a model.en.srt | 18.5 KB |
| 036. Chapter 6. Developing a model.mp4 | 31.7 MB |
| 037. Chapter 6. Deploying your model.en.srt | 21.6 KB |
| 037. Chapter 6. Deploying your model.mp4 | 37.9 MB |
| 038. Chapter 6. Summary.en.srt | 1.8 KB |
| 038. Chapter 6. Summary.mp4 | 3.9 MB |
| 039. Chapter 7. A deep dive on Keras.en.srt | 5.6 KB |
| 039. Chapter 7. A deep dive on Keras.mp4 | 11.0 MB |
| 040. Chapter 7. Different ways to build Keras models.en.srt | 20.2 KB |
| 040. Chapter 7. Different ways to build Keras models.mp4 | 32.5 MB |
| 041. Chapter 7. Using built-in training and evaluation loops.en.srt | 14.7 KB |
| 041. Chapter 7. Using built-in training and evaluation loops.mp4 | 24.6 MB |
| 042. Chapter 7. Writing your own training and evaluation loops.en.srt | 23.7 KB |
| 042. Chapter 7. Writing your own training and evaluation loops.mp4 | 38.6 MB |
| 043. Chapter 7. Summary.en.srt | 1.3 KB |
| 043. Chapter 7. Summary.mp4 | 4.0 MB |
| 044. Chapter 8. Image classification.en.srt | 27.0 KB |
| 044. Chapter 8. Image classification.mp4 | 47.7 MB |
| 045. Chapter 8. Training a ConvNet from scratch on a small dataset.en.srt | 27.4 KB |
| 045. Chapter 8. Training a ConvNet from scratch on a small dataset.mp4 | 48.3 MB |
| 046. Chapter 8. Using a pretrained model.en.srt | 23.6 KB |
| 046. Chapter 8. Using a pretrained model.mp4 | 42.4 MB |
| 047. Chapter 8. Summary.en.srt | 1.1 KB |
| 047. Chapter 8. Summary.mp4 | 2.9 MB |
| 048. Chapter 9. ConvNet architecture patterns.en.srt | 11.6 KB |
| 048. Chapter 9. ConvNet architecture patterns.mp4 | 24.1 MB |
| 049. Chapter 9. Residual connections.en.srt | 4.7 KB |
| 049. Chapter 9. Residual connections.mp4 | 8.5 MB |
| 050. Chapter 9. Batch normalization.en.srt | 7.0 KB |
| 050. Chapter 9. Batch normalization.mp4 | 12.6 MB |
| 051. Chapter 9. Depthwise separable convolutions.en.srt | 7.6 KB |
| 051. Chapter 9. Depthwise separable convolutions.mp4 | 17.3 MB |
| 052. Chapter 9. Putting it together - A mini Xception-like model.en.srt | 2.9 KB |
| 052. Chapter 9. Putting it together - A mini Xception-like model.mp4 | 5.9 MB |
| 053. Chapter 9. Beyond convolution - Vision Transformers.en.srt | 3.5 KB |
| 053. Chapter 9. Beyond convolution - Vision Transformers.mp4 | 6.1 MB |
| 054. Chapter 9. Summary.en.srt | 716 bytes |
| 054. Chapter 9. Summary.mp4 | 1.7 MB |
| 055. Chapter 10. Interpreting what ConvNets learn.en.srt | 11.0 KB |
| 055. Chapter 10. Interpreting what ConvNets learn.mp4 | 21.8 MB |
| 056. Chapter 10. Visualizing ConvNet filters.en.srt | 10.9 KB |
| 056. Chapter 10. Visualizing ConvNet filters.mp4 | 17.7 MB |
| 057. Chapter 10. Visualizing heatmaps of class activation.en.srt | 8.2 KB |
| 057. Chapter 10. Visualizing heatmaps of class activation.mp4 | 15.6 MB |
| 058. Chapter 10. Visualizing the latent space of a ConvNet.en.srt | 4.8 KB |
| 058. Chapter 10. Visualizing the latent space of a ConvNet.mp4 | 8.0 MB |
| 059. Chapter 10. Summary.en.srt | 789 bytes |
| 059. Chapter 10. Summary.mp4 | 1.6 MB |
| 060. Chapter 11. Image segmentation.en.srt | 6.4 KB |
| 060. Chapter 11. Image segmentation.mp4 | 12.2 MB |
| 061. Chapter 11. Training a segmentation model from scratch.en.srt | 10.3 KB |
| 061. Chapter 11. Training a segmentation model from scratch.mp4 | 23.8 MB |
| 062. Chapter 11. Using a pretrained segmentation model.en.srt | 13.8 KB |
| 062. Chapter 11. Using a pretrained segmentation model.mp4 | 20.8 MB |
| 063. Chapter 11. Summary.en.srt | 856 bytes |
| 063. Chapter 11. Summary.mp4 | 2.2 MB |
| 064. Chapter 12. Object detection.en.srt | 8.0 KB |
| 064. Chapter 12. Object detection.mp4 | 14.3 MB |
| 065. Chapter 12. Training a YOLO model from scratch.en.srt | 19.7 KB |
| 065. Chapter 12. Training a YOLO model from scratch.mp4 | 39.7 MB |
| 066. Chapter 12. Using a pretrained RetinaNet detector.en.srt | 5.8 KB |
| 066. Chapter 12. Using a pretrained RetinaNet detector.mp4 | 11.0 MB |
| 067. Chapter 12. Summary.en.srt | 1.8 KB |
| 067. Chapter 12. Summary.mp4 | 3.3 MB |
| 068. Chapter 13. Timeseries forecasting.en.srt | 3.8 KB |
| 068. Chapter 13. Timeseries forecasting.mp4 | 7.7 MB |
| 069. Chapter 13. A temperature forecasting example.en.srt | 21.4 KB |
| 069. Chapter 13. A temperature forecasting example.mp4 | 39.3 MB |
| 070. Chapter 13. Recurrent neural networks.en.srt | 45.0 KB |
| 070. Chapter 13. Recurrent neural networks.mp4 | 72.9 MB |
| 071. Chapter 13. Going even further.en.srt | 4.0 KB |
| 071. Chapter 13. Going even further.mp4 | 6.9 MB |
| 072. Chapter 13. Summary.en.srt | 1.6 KB |
| 072. Chapter 13. Summary.mp4 | 4.9 MB |
| 073. Chapter 14. Text classification.en.srt | 12.4 KB |
| 073. Chapter 14. Text classification.mp4 | 27.9 MB |
| 074. Chapter 14. Preparing text data.en.srt | 23.6 KB |
| 074. Chapter 14. Preparing text data.mp4 | 40.9 MB |
| 075. Chapter 14. Sets vs. sequences.en.srt | 7.8 KB |
| 075. Chapter 14. Sets vs. sequences.mp4 | 13.3 MB |
| 076. Chapter 14. Set models.en.srt | 13.6 KB |
| 076. Chapter 14. Set models.mp4 | 25.2 MB |
| 077. Chapter 14. Sequence models.en.srt | 35.6 KB |
| 077. Chapter 14. Sequence models.mp4 | 57.5 MB |
| 078. Chapter 14. Summary.en.srt | 2.0 KB |
| 078. Chapter 14. Summary.mp4 | 3.6 MB |
| 079. Chapter 15. Language models and the Transformer.en.srt | 16.2 KB |
| 079. Chapter 15. Language models and the Transformer.mp4 | 29.4 MB |
| 080. Chapter 15. Sequence-to-sequence learning.en.srt | 14.5 KB |
| 080. Chapter 15. Sequence-to-sequence learning.mp4 | 29.0 MB |
| 081. Chapter 15. The Transformer architecture.en.srt | 37.6 KB |
| 081. Chapter 15. The Transformer architecture.mp4 | 63.3 MB |
| 082. Chapter 15. Classification with a pretrained Transformer.en.srt | 19.0 KB |
| 082. Chapter 15. Classification with a pretrained Transformer.mp4 | 33.5 MB |
| 083. Chapter 15. What makes the Transformer effective.en.srt | 12.1 KB |
| 083. Chapter 15. What makes the Transformer effective.mp4 | 25.2 MB |
| 084. Chapter 15. Summary.en.srt | 2.9 KB |
| 084. Chapter 15. Summary.mp4 | 7.2 MB |
| 085. Chapter 16. Text generation.en.srt | 13.7 KB |
| 085. Chapter 16. Text generation.mp4 | 24.9 MB |
| 086. Chapter 16. Training a mini-GPT.en.srt | 29.9 KB |
| 086. Chapter 16. Training a mini-GPT.mp4 | 53.7 MB |
| 087. Chapter 16. Using a pretrained LLM.en.srt | 21.2 KB |
| 087. Chapter 16. Using a pretrained LLM.mp4 | 33.3 MB |
| 088. Chapter 16. Going further with LLMs.en.srt | 27.6 KB |
| 088. Chapter 16. Going further with LLMs.mp4 | 46.6 MB |
| 089. Chapter 16. Where are LLMs heading next.en.srt | 5.0 KB |
| 089. Chapter 16. Where are LLMs heading next.mp4 | 9.3 MB |
| 090. Chapter 16. Summary.en.srt | 2.5 KB |
| 090. Chapter 16. Summary.mp4 | 3.9 MB |
| 091. Chapter 17. Image generation.en.srt | 20.3 KB |
| 091. Chapter 17. Image generation.mp4 | 37.1 MB |
| 092. Chapter 17. Diffusion models.en.srt | 17.5 KB |
| 092. Chapter 17. Diffusion models.mp4 | 31.6 MB |
| 093. Chapter 17. Text-to-image models.en.srt | 13.5 KB |
| 093. Chapter 17. Text-to-image models.mp4 | 23.6 MB |
| 094. Chapter 17. Summary.en.srt | 1.9 KB |
| 094. Chapter 17. Summary.mp4 | 4.0 MB |
| 095. Chapter 18. Best practices for the real world.en.srt | 32.0 KB |
| 095. Chapter 18. Best practices for the real world.mp4 | 46.4 MB |
| 096. Chapter 18. Scaling up model training with multiple devices.en.srt | 25.4 KB |
| 096. Chapter 18. Scaling up model training with multiple devices.mp4 | 41.8 MB |
| 097. Chapter 18. Speeding up training and inference with lower-precision computation.en.srt | 18.5 KB |
| 097. Chapter 18. Speeding up training and inference with lower-precision computation.mp4 | 30.7 MB |
| 098. Chapter 18. Summary.en.srt | 1.1 KB |
| 098. Chapter 18. Summary.mp4 | 3.2 MB |
| 099. Chapter 19. The future of AI.en.srt | 21.7 KB |
| 099. Chapter 19. The future of AI.mp4 | 43.3 MB |
| 100. Chapter 19. Scale isn t all you need.en.srt | 22.2 KB |
| 100. Chapter 19. Scale isn t all you need.mp4 | 49.7 MB |
| 101. Chapter 19. How to build intelligence.en.srt | 28.2 KB |
| 101. Chapter 19. How to build intelligence.mp4 | 56.3 MB |
| 102. Chapter 19. The missing ingredients - Search and symbols.en.srt | 36.1 KB |
| 102. Chapter 19. The missing ingredients - Search and symbols.mp4 | 70.4 MB |
| 103. Chapter 20. Conclusions.en.srt | 31.0 KB |
| 103. Chapter 20. Conclusions.mp4 | 66.7 MB |
| 104. Chapter 20. Limitations of deep learning.en.srt | 4.6 KB |
| 104. Chapter 20. Limitations of deep learning.mp4 | 8.6 MB |
| 105. Chapter 20. What might lie ahead.en.srt | 3.3 KB |
| 105. Chapter 20. What might lie ahead.mp4 | 7.0 MB |
| 106. Chapter 20. Staying up to date in a fast-moving field.en.srt | 5.6 KB |
| 106. Chapter 20. Staying up to date in a fast-moving field.mp4 | 11.5 MB |
| 107. Chapter 20. Final words.en.srt | 764 bytes |
| 107. Chapter 20. Final words.mp4 | 1.5 MB |
| Bonus Resources.txt | 70 bytes |
Name
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36.7 MB
[3
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2023-07-01
| Uploaded by FreeCourseWeb | Size 36.7 MB | Health [ 3 /0 ] | Added 2023-07-01 |
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39.7 MB
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2023-07-01
| Uploaded by FreeCourseWeb | Size 39.7 MB | Health [ 15 /7 ] | Added 2023-07-01 |
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141.7 MB
[8
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| Uploaded by FreeCourseWeb | Size 141.7 MB | Health [ 8 /5 ] | Added 2023-07-01 |
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28.8 MB
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| Uploaded by FreeCourseWeb | Size 28.8 MB | Health [ 0 /7 ] | Added 2023-07-01 |
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2.8 MB
[10
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2023-07-01
| Uploaded by FreeCourseWeb | Size 2.8 MB | Health [ 10 /0 ] | Added 2023-07-01 |
NOTE
SOURCE: Deep Learning with Python Third Edition Video Edition
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COVER
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