WebToolTip com Deep Learning with Python Third Edition Video Edition

WebToolTip com Deep Learning with Python Third Edition Video Edition

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Total Files	216
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001. Chapter 1. What is deep learning.en.srt	2.4 KB
001. Chapter 1. What is deep learning.mp4	5.2 MB
002. Chapter 1. Artificial intelligence.en.srt	3.9 KB
002. Chapter 1. Artificial intelligence.mp4	7.9 MB
003. Chapter 1. Machine learning.en.srt	6.2 KB
003. Chapter 1. Machine learning.mp4	13.2 MB
004. Chapter 1. Learning rules and representations from data.en.srt	9.8 KB
004. Chapter 1. Learning rules and representations from data.mp4	17.8 MB
005. Chapter 1. The deep in deep learning .en.srt	4.6 KB
005. Chapter 1. The deep in deep learning .mp4	10.3 MB
006. Chapter 1. Understanding how deep learning works, in three figures.en.srt	4.4 KB
006. Chapter 1. Understanding how deep learning works, in three figures.mp4	7.2 MB
007. Chapter 1. Understanding how deep learning works, in three figures.en.srt	3.8 KB
007. Chapter 1. Understanding how deep learning works, in three figures.mp4	8.3 MB
008. Chapter 1. The age of generative AI.en.srt	3.1 KB
008. Chapter 1. The age of generative AI.mp4	4.6 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	6.8 MB
010. Chapter 1. Beware of the short-term hype.en.srt	6.7 KB
010. Chapter 1. Beware of the short-term hype.mp4	15.8 MB
011. Chapter 1. Summer can turn to winter.en.srt	4.4 KB
011. Chapter 1. Summer can turn to winter.mp4	11.6 MB
012. Chapter 1. The promise of AI.en.srt	4.4 KB
012. Chapter 1. The promise of AI.mp4	8.9 MB
013. Chapter 2. The mathematical building blocks of neural networks.en.srt	15.0 KB
013. Chapter 2. The mathematical building blocks of neural networks.mp4	23.3 MB
014. Chapter 2. Data representations for neural networks.en.srt	18.2 KB
014. Chapter 2. Data representations for neural networks.mp4	34.1 MB
015. Chapter 2. The gears of neural networks - Tensor operations.en.srt	24.3 KB
015. Chapter 2. The gears of neural networks - Tensor operations.mp4	32.2 MB
016. Chapter 2. The engine of neural networks - Gradient-based optimization.en.srt	36.0 KB
016. Chapter 2. The engine of neural networks - Gradient-based optimization.mp4	63.2 MB
017. Chapter 2. Looking back at our first example.en.srt	11.6 KB
017. Chapter 2. Looking back at our first example.mp4	20.2 MB
018. Chapter 2. Summary.en.srt	3.0 KB
018. Chapter 2. Summary.mp4	4.7 MB
019. Chapter 3. Introduction to TensorFlow, PyTorch, JAX, and Keras.en.srt	9.6 KB
019. Chapter 3. Introduction to TensorFlow, PyTorch, JAX, and Keras.mp4	21.0 MB
020. Chapter 3. How these frameworks relate to each other.en.srt	3.1 KB
020. Chapter 3. How these frameworks relate to each other.mp4	6.2 MB
021. Chapter 3. Introduction to TensorFlow.en.srt	21.7 KB
021. Chapter 3. Introduction to TensorFlow.mp4	37.2 MB
022. Chapter 3. Introduction to PyTorch.en.srt	18.3 KB
022. Chapter 3. Introduction to PyTorch.mp4	28.2 MB
023. Chapter 3. Introduction to JAX.en.srt	17.9 KB
023. Chapter 3. Introduction to JAX.mp4	28.8 MB
024. Chapter 3. Introduction to Keras.en.srt	28.7 KB
024. Chapter 3. Introduction to Keras.mp4	50.6 MB
025. Chapter 3. Summary.en.srt	1.4 KB
025. Chapter 3. Summary.mp4	4.2 MB
026. Chapter 4. Classification and regression.en.srt	28.6 KB
026. Chapter 4. Classification and regression.mp4	50.1 MB
027. Chapter 4. Classifying newswires - A multiclass classification example.en.srt	14.8 KB
027. Chapter 4. Classifying newswires - A multiclass classification example.mp4	24.8 MB
028. Chapter 4. Predicting house prices - A regression example.en.srt	15.9 KB
028. Chapter 4. Predicting house prices - A regression example.mp4	26.2 MB
029. Chapter 4. Summary.en.srt	1.4 KB
029. Chapter 4. Summary.mp4	2.2 MB
030. Chapter 5. Fundamentals of machine learning.en.srt	33.5 KB
030. Chapter 5. Fundamentals of machine learning.mp4	54.3 MB
031. Chapter 5. Evaluating machine-learning models.en.srt	15.0 KB
031. Chapter 5. Evaluating machine-learning models.mp4	26.5 MB
032. Chapter 5. Improving model fit.en.srt	9.7 KB
032. Chapter 5. Improving model fit.mp4	16.4 MB
033. Chapter 5. Improving generalization.en.srt	25.6 KB
033. Chapter 5. Improving generalization.mp4	42.4 MB
034. Chapter 5. Summary.en.srt	3.0 KB
034. Chapter 5. Summary.mp4	7.2 MB
035. Chapter 6. The universal workflow of machine learning.en.srt	30.8 KB
035. Chapter 6. The universal workflow of machine learning.mp4	63.1 MB
036. Chapter 6. Developing a model.en.srt	19.0 KB
036. Chapter 6. Developing a model.mp4	33.3 MB
037. Chapter 6. Deploying your model.en.srt	22.1 KB
037. Chapter 6. Deploying your model.mp4	39.7 MB
038. Chapter 6. Summary.en.srt	1.9 KB
038. Chapter 6. Summary.mp4	4.1 MB
039. Chapter 7. A deep dive on Keras.en.srt	5.8 KB
039. Chapter 7. A deep dive on Keras.mp4	11.5 MB
040. Chapter 7. Different ways to build Keras models.en.srt	20.7 KB
040. Chapter 7. Different ways to build Keras models.mp4	34.1 MB
041. Chapter 7. Using built-in training and evaluation loops.en.srt	15.1 KB
041. Chapter 7. Using built-in training and evaluation loops.mp4	25.8 MB
042. Chapter 7. Writing your own training and evaluation loops.en.srt	24.3 KB
042. Chapter 7. Writing your own training and evaluation loops.mp4	40.4 MB
043. Chapter 7. Summary.en.srt	1.3 KB
043. Chapter 7. Summary.mp4	4.2 MB
044. Chapter 8. Image classification.en.srt	27.6 KB
044. Chapter 8. Image classification.mp4	50.1 MB
045. Chapter 8. Training a ConvNet from scratch on a small dataset.en.srt	28.1 KB
045. Chapter 8. Training a ConvNet from scratch on a small dataset.mp4	50.7 MB
046. Chapter 8. Using a pretrained model.en.srt	24.2 KB
046. Chapter 8. Using a pretrained model.mp4	44.5 MB
047. Chapter 8. Summary.en.srt	1.1 KB
047. Chapter 8. Summary.mp4	3.0 MB
048. Chapter 9. ConvNet architecture patterns.en.srt	11.9 KB
048. Chapter 9. ConvNet architecture patterns.mp4	25.3 MB
049. Chapter 9. Residual connections.en.srt	4.8 KB
049. Chapter 9. Residual connections.mp4	8.9 MB
050. Chapter 9. Batch normalization.en.srt	7.1 KB
050. Chapter 9. Batch normalization.mp4	13.2 MB
051. Chapter 9. Depthwise separable convolutions.en.srt	7.8 KB
051. Chapter 9. Depthwise separable convolutions.mp4	18.1 MB
052. Chapter 9. Putting it together - A mini Xception-like model.en.srt	3.0 KB
052. Chapter 9. Putting it together - A mini Xception-like model.mp4	6.2 MB
053. Chapter 9. Beyond convolution - Vision Transformers.en.srt	3.6 KB
053. Chapter 9. Beyond convolution - Vision Transformers.mp4	6.4 MB
054. Chapter 9. Summary.en.srt	0.7 KB
054. Chapter 9. Summary.mp4	1.8 MB
055. Chapter 10. Interpreting what ConvNets learn.en.srt	11.3 KB
055. Chapter 10. Interpreting what ConvNets learn.mp4	22.9 MB
056. Chapter 10. Visualizing ConvNet filters.en.srt	11.2 KB
056. Chapter 10. Visualizing ConvNet filters.mp4	18.6 MB
057. Chapter 10. Visualizing heatmaps of class activation.en.srt	8.4 KB
057. Chapter 10. Visualizing heatmaps of class activation.mp4	16.4 MB
058. Chapter 10. Visualizing the latent space of a ConvNet.en.srt	4.9 KB
058. Chapter 10. Visualizing the latent space of a ConvNet.mp4	8.4 MB
059. Chapter 10. Summary.en.srt	0.8 KB
059. Chapter 10. Summary.mp4	1.7 MB
060. Chapter 11. Image segmentation.en.srt	6.5 KB
060. Chapter 11. Image segmentation.mp4	12.8 MB
061. Chapter 11. Training a segmentation model from scratch.en.srt	10.5 KB
061. Chapter 11. Training a segmentation model from scratch.mp4	24.9 MB
062. Chapter 11. Using a pretrained segmentation model.en.srt	14.1 KB
062. Chapter 11. Using a pretrained segmentation model.mp4	21.8 MB
063. Chapter 11. Summary.en.srt	0.9 KB
063. Chapter 11. Summary.mp4	2.3 MB
064. Chapter 12. Object detection.en.srt	8.2 KB
064. Chapter 12. Object detection.mp4	15.0 MB
065. Chapter 12. Training a YOLO model from scratch.en.srt	20.2 KB
065. Chapter 12. Training a YOLO model from scratch.mp4	41.6 MB
066. Chapter 12. Using a pretrained RetinaNet detector.en.srt	5.9 KB
066. Chapter 12. Using a pretrained RetinaNet detector.mp4	11.6 MB
067. Chapter 12. Summary.en.srt	1.8 KB
067. Chapter 12. Summary.mp4	3.4 MB
068. Chapter 13. Timeseries forecasting.en.srt	3.9 KB
068. Chapter 13. Timeseries forecasting.mp4	8.1 MB
069. Chapter 13. A temperature forecasting example.en.srt	21.9 KB
069. Chapter 13. A temperature forecasting example.mp4	41.2 MB
070. Chapter 13. Recurrent neural networks.en.srt	46.1 KB
070. Chapter 13. Recurrent neural networks.mp4	76.5 MB
071. Chapter 13. Going even further.en.srt	4.1 KB
071. Chapter 13. Going even further.mp4	7.2 MB
072. Chapter 13. Summary.en.srt	1.7 KB
072. Chapter 13. Summary.mp4	5.2 MB
073. Chapter 14. Text classification.en.srt	12.7 KB
073. Chapter 14. Text classification.mp4	29.2 MB
074. Chapter 14. Preparing text data.en.srt	24.2 KB
074. Chapter 14. Preparing text data.mp4	42.8 MB
075. Chapter 14. Sets vs. sequences.en.srt	8.0 KB
075. Chapter 14. Sets vs. sequences.mp4	14.0 MB
076. Chapter 14. Set models.en.srt	14.0 KB
076. Chapter 14. Set models.mp4	26.5 MB
077. Chapter 14. Sequence models.en.srt	36.4 KB
077. Chapter 14. Sequence models.mp4	60.3 MB
078. Chapter 14. Summary.en.srt	2.1 KB
078. Chapter 14. Summary.mp4	3.7 MB
079. Chapter 15. Language models and the Transformer.en.srt	16.6 KB
079. Chapter 15. Language models and the Transformer.mp4	30.8 MB
080. Chapter 15. Sequence-to-sequence learning.en.srt	14.9 KB
080. Chapter 15. Sequence-to-sequence learning.mp4	30.4 MB
081. Chapter 15. The Transformer architecture.en.srt	38.5 KB
081. Chapter 15. The Transformer architecture.mp4	66.3 MB
082. Chapter 15. Classification with a pretrained Transformer.en.srt	19.5 KB
082. Chapter 15. Classification with a pretrained Transformer.mp4	35.1 MB
083. Chapter 15. What makes the Transformer effective.en.srt	12.3 KB
083. Chapter 15. What makes the Transformer effective.mp4	26.5 MB
084. Chapter 15. Summary.en.srt	2.9 KB
084. Chapter 15. Summary.mp4	7.6 MB
085. Chapter 16. Text generation.en.srt	14.0 KB
085. Chapter 16. Text generation.mp4	26.1 MB
086. Chapter 16. Training a mini-GPT.en.srt	30.6 KB
086. Chapter 16. Training a mini-GPT.mp4	56.3 MB
087. Chapter 16. Using a pretrained LLM.en.srt	21.7 KB
087. Chapter 16. Using a pretrained LLM.mp4	34.9 MB
088. Chapter 16. Going further with LLMs.en.srt	28.3 KB
088. Chapter 16. Going further with LLMs.mp4	48.8 MB
089. Chapter 16. Where are LLMs heading next.en.srt	5.2 KB
089. Chapter 16. Where are LLMs heading next.mp4	9.8 MB
090. Chapter 16. Summary.en.srt	2.5 KB
090. Chapter 16. Summary.mp4	4.1 MB
091. Chapter 17. Image generation.en.srt	20.8 KB
091. Chapter 17. Image generation.mp4	38.9 MB
092. Chapter 17. Diffusion models.en.srt	18.0 KB
092. Chapter 17. Diffusion models.mp4	33.2 MB
093. Chapter 17. Text-to-image models.en.srt	13.8 KB
093. Chapter 17. Text-to-image models.mp4	24.7 MB
094. Chapter 17. Summary.en.srt	2.0 KB
094. Chapter 17. Summary.mp4	4.2 MB
095. Chapter 18. Best practices for the real world.en.srt	32.8 KB
095. Chapter 18. Best practices for the real world.mp4	48.7 MB
096. Chapter 18. Scaling up model training with multiple devices.en.srt	26.0 KB
096. Chapter 18. Scaling up model training with multiple devices.mp4	43.8 MB
097. Chapter 18. Speeding up training and inference with lower-precision computation.en.srt	18.9 KB
097. Chapter 18. Speeding up training and inference with lower-precision computation.mp4	32.2 MB
098. Chapter 18. Summary.en.srt	1.1 KB
098. Chapter 18. Summary.mp4	3.4 MB
099. Chapter 19. The future of AI.en.srt	22.2 KB
099. Chapter 19. The future of AI.mp4	45.4 MB
100. Chapter 19. Scale isn t all you need.en.srt	22.7 KB
100. Chapter 19. Scale isn t all you need.mp4	52.1 MB
101. Chapter 19. How to build intelligence.en.srt	28.9 KB
101. Chapter 19. How to build intelligence.mp4	59.0 MB
102. Chapter 19. The missing ingredients - Search and symbols.en.srt	37.0 KB
102. Chapter 19. The missing ingredients - Search and symbols.mp4	73.8 MB
103. Chapter 20. Conclusions.en.srt	31.8 KB
103. Chapter 20. Conclusions.mp4	69.9 MB
104. Chapter 20. Limitations of deep learning.en.srt	4.7 KB
104. Chapter 20. Limitations of deep learning.mp4	9.1 MB
105. Chapter 20. What might lie ahead.en.srt	3.4 KB
105. Chapter 20. What might lie ahead.mp4	7.3 MB
106. Chapter 20. Staying up to date in a fast-moving field.en.srt	5.8 KB
106. Chapter 20. Staying up to date in a fast-moving field.mp4	12.1 MB
107. Chapter 20. Final words.en.srt	0.8 KB
107. Chapter 20. Final words.mp4	1.6 MB
Bonus Resources.txt	0.1 KB
Total files 216