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Files

1. Get Bonus Downloads Here.url180 bytes
2. 001. Chapter 1. What is deep learning.en.srt2.3 KB
3. 001. Chapter 1. What is deep learning.mp45.0 MB
4. 002. Chapter 1. Artificial intelligence.en.srt3.8 KB
5. 002. Chapter 1. Artificial intelligence.mp47.5 MB
6. 003. Chapter 1. Machine learning.en.srt6.1 KB
7. 003. Chapter 1. Machine learning.mp412.6 MB
8. 004. Chapter 1. Learning rules and representations from data.en.srt9.6 KB
9. 004. Chapter 1. Learning rules and representations from data.mp417.0 MB
10. 005. Chapter 1. The deep in deep learning .en.srt4.5 KB
11. 005. Chapter 1. The deep in deep learning .mp49.8 MB
12. 006. Chapter 1. Understanding how deep learning works, in three figures.en.srt4.3 KB
13. 006. Chapter 1. Understanding how deep learning works, in three figures.mp46.9 MB
14. 007. Chapter 1. Understanding how deep learning works, in three figures.en.srt3.7 KB
15. 007. Chapter 1. Understanding how deep learning works, in three figures.mp47.9 MB
16. 008. Chapter 1. The age of generative AI.en.srt3.0 KB
17. 008. Chapter 1. The age of generative AI.mp44.4 MB
18. 009. Chapter 1. What deep learning has achieved so far.en.srt2.7 KB
19. 009. Chapter 1. What deep learning has achieved so far.mp46.5 MB
20. 010. Chapter 1. Beware of the short-term hype.en.srt6.6 KB
21. 010. Chapter 1. Beware of the short-term hype.mp415.1 MB
22. 011. Chapter 1. Summer can turn to winter.en.srt4.3 KB
23. 011. Chapter 1. Summer can turn to winter.mp411.0 MB
24. 012. Chapter 1. The promise of AI.en.srt4.3 KB
25. 012. Chapter 1. The promise of AI.mp48.5 MB
26. 013. Chapter 2. The mathematical building blocks of neural networks.en.srt14.7 KB
27. 013. Chapter 2. The mathematical building blocks of neural networks.mp422.2 MB
28. 014. Chapter 2. Data representations for neural networks.en.srt17.7 KB
29. 014. Chapter 2. Data representations for neural networks.mp432.6 MB
30. 015. Chapter 2. The gears of neural networks - Tensor operations.en.srt23.8 KB
31. 015. Chapter 2. The gears of neural networks - Tensor operations.mp430.7 MB
32. 016. Chapter 2. The engine of neural networks - Gradient-based optimization.en.srt35.2 KB
33. 016. Chapter 2. The engine of neural networks - Gradient-based optimization.mp460.2 MB
34. 017. Chapter 2. Looking back at our first example.en.srt11.4 KB
35. 017. Chapter 2. Looking back at our first example.mp419.3 MB
36. 018. Chapter 2. Summary.en.srt2.9 KB
37. 018. Chapter 2. Summary.mp44.5 MB
38. 019. Chapter 3. Introduction to TensorFlow, PyTorch, JAX, and Keras.en.srt9.4 KB
39. 019. Chapter 3. Introduction to TensorFlow, PyTorch, JAX, and Keras.mp420.0 MB
40. 020. Chapter 3. How these frameworks relate to each other.en.srt3.0 KB
41. 020. Chapter 3. How these frameworks relate to each other.mp45.9 MB
42. 021. Chapter 3. Introduction to TensorFlow.en.srt21.2 KB
43. 021. Chapter 3. Introduction to TensorFlow.mp435.5 MB
44. 022. Chapter 3. Introduction to PyTorch.en.srt17.9 KB
45. 022. Chapter 3. Introduction to PyTorch.mp426.9 MB
46. 023. Chapter 3. Introduction to JAX.en.srt17.5 KB
47. 023. Chapter 3. Introduction to JAX.mp427.5 MB
48. 024. Chapter 3. Introduction to Keras.en.srt28.1 KB
49. 024. Chapter 3. Introduction to Keras.mp448.3 MB
50. 025. Chapter 3. Summary.en.srt1.3 KB
51. 025. Chapter 3. Summary.mp44.0 MB
52. 026. Chapter 4. Classification and regression.en.srt28.0 KB
53. 026. Chapter 4. Classification and regression.mp447.8 MB
54. 027. Chapter 4. Classifying newswires - A multiclass classification example.en.srt14.4 KB
55. 027. Chapter 4. Classifying newswires - A multiclass classification example.mp423.6 MB
56. 028. Chapter 4. Predicting house prices - A regression example.en.srt15.5 KB
57. 028. Chapter 4. Predicting house prices - A regression example.mp425.0 MB
58. 029. Chapter 4. Summary.en.srt1.4 KB
59. 029. Chapter 4. Summary.mp42.1 MB
60. 030. Chapter 5. Fundamentals of machine learning.en.srt32.7 KB
61. 030. Chapter 5. Fundamentals of machine learning.mp451.8 MB
62. 031. Chapter 5. Evaluating machine-learning models.en.srt14.6 KB
63. 031. Chapter 5. Evaluating machine-learning models.mp425.3 MB
64. 032. Chapter 5. Improving model fit.en.srt9.5 KB
65. 032. Chapter 5. Improving model fit.mp415.7 MB
66. 033. Chapter 5. Improving generalization.en.srt25.0 KB
67. 033. Chapter 5. Improving generalization.mp440.4 MB
68. 034. Chapter 5. Summary.en.srt2.9 KB
69. 034. Chapter 5. Summary.mp46.9 MB
70. 035. Chapter 6. The universal workflow of machine learning.en.srt30.1 KB
71. 035. Chapter 6. The universal workflow of machine learning.mp460.2 MB
72. 036. Chapter 6. Developing a model.en.srt18.5 KB
73. 036. Chapter 6. Developing a model.mp431.7 MB
74. 037. Chapter 6. Deploying your model.en.srt21.6 KB
75. 037. Chapter 6. Deploying your model.mp437.9 MB
76. 038. Chapter 6. Summary.en.srt1.8 KB
77. 038. Chapter 6. Summary.mp43.9 MB
78. 039. Chapter 7. A deep dive on Keras.en.srt5.6 KB
79. 039. Chapter 7. A deep dive on Keras.mp411.0 MB
80. 040. Chapter 7. Different ways to build Keras models.en.srt20.2 KB
81. 040. Chapter 7. Different ways to build Keras models.mp432.5 MB
82. 041. Chapter 7. Using built-in training and evaluation loops.en.srt14.7 KB
83. 041. Chapter 7. Using built-in training and evaluation loops.mp424.6 MB
84. 042. Chapter 7. Writing your own training and evaluation loops.en.srt23.7 KB
85. 042. Chapter 7. Writing your own training and evaluation loops.mp438.6 MB
86. 043. Chapter 7. Summary.en.srt1.3 KB
87. 043. Chapter 7. Summary.mp44.0 MB
88. 044. Chapter 8. Image classification.en.srt27.0 KB
89. 044. Chapter 8. Image classification.mp447.7 MB
90. 045. Chapter 8. Training a ConvNet from scratch on a small dataset.en.srt27.4 KB
91. 045. Chapter 8. Training a ConvNet from scratch on a small dataset.mp448.3 MB
92. 046. Chapter 8. Using a pretrained model.en.srt23.6 KB
93. 046. Chapter 8. Using a pretrained model.mp442.4 MB
94. 047. Chapter 8. Summary.en.srt1.1 KB
95. 047. Chapter 8. Summary.mp42.9 MB
96. 048. Chapter 9. ConvNet architecture patterns.en.srt11.6 KB
97. 048. Chapter 9. ConvNet architecture patterns.mp424.1 MB
98. 049. Chapter 9. Residual connections.en.srt4.7 KB
99. 049. Chapter 9. Residual connections.mp48.5 MB
100. 050. Chapter 9. Batch normalization.en.srt7.0 KB
101. 050. Chapter 9. Batch normalization.mp412.6 MB
102. 051. Chapter 9. Depthwise separable convolutions.en.srt7.6 KB
103. 051. Chapter 9. Depthwise separable convolutions.mp417.3 MB
104. 052. Chapter 9. Putting it together - A mini Xception-like model.en.srt2.9 KB
105. 052. Chapter 9. Putting it together - A mini Xception-like model.mp45.9 MB
106. 053. Chapter 9. Beyond convolution - Vision Transformers.en.srt3.5 KB
107. 053. Chapter 9. Beyond convolution - Vision Transformers.mp46.1 MB
108. 054. Chapter 9. Summary.en.srt716 bytes
109. 054. Chapter 9. Summary.mp41.7 MB
110. 055. Chapter 10. Interpreting what ConvNets learn.en.srt11.0 KB
111. 055. Chapter 10. Interpreting what ConvNets learn.mp421.8 MB
112. 056. Chapter 10. Visualizing ConvNet filters.en.srt10.9 KB
113. 056. Chapter 10. Visualizing ConvNet filters.mp417.7 MB
114. 057. Chapter 10. Visualizing heatmaps of class activation.en.srt8.2 KB
115. 057. Chapter 10. Visualizing heatmaps of class activation.mp415.6 MB
116. 058. Chapter 10. Visualizing the latent space of a ConvNet.en.srt4.8 KB
117. 058. Chapter 10. Visualizing the latent space of a ConvNet.mp48.0 MB
118. 059. Chapter 10. Summary.en.srt789 bytes
119. 059. Chapter 10. Summary.mp41.6 MB
120. 060. Chapter 11. Image segmentation.en.srt6.4 KB
121. 060. Chapter 11. Image segmentation.mp412.2 MB
122. 061. Chapter 11. Training a segmentation model from scratch.en.srt10.3 KB
123. 061. Chapter 11. Training a segmentation model from scratch.mp423.8 MB
124. 062. Chapter 11. Using a pretrained segmentation model.en.srt13.8 KB
125. 062. Chapter 11. Using a pretrained segmentation model.mp420.8 MB
126. 063. Chapter 11. Summary.en.srt856 bytes
127. 063. Chapter 11. Summary.mp42.2 MB
128. 064. Chapter 12. Object detection.en.srt8.0 KB
129. 064. Chapter 12. Object detection.mp414.3 MB
130. 065. Chapter 12. Training a YOLO model from scratch.en.srt19.7 KB
131. 065. Chapter 12. Training a YOLO model from scratch.mp439.7 MB
132. 066. Chapter 12. Using a pretrained RetinaNet detector.en.srt5.8 KB
133. 066. Chapter 12. Using a pretrained RetinaNet detector.mp411.0 MB
134. 067. Chapter 12. Summary.en.srt1.8 KB
135. 067. Chapter 12. Summary.mp43.3 MB
136. 068. Chapter 13. Timeseries forecasting.en.srt3.8 KB
137. 068. Chapter 13. Timeseries forecasting.mp47.7 MB
138. 069. Chapter 13. A temperature forecasting example.en.srt21.4 KB
139. 069. Chapter 13. A temperature forecasting example.mp439.3 MB
140. 070. Chapter 13. Recurrent neural networks.en.srt45.0 KB
141. 070. Chapter 13. Recurrent neural networks.mp472.9 MB
142. 071. Chapter 13. Going even further.en.srt4.0 KB
143. 071. Chapter 13. Going even further.mp46.9 MB
144. 072. Chapter 13. Summary.en.srt1.6 KB
145. 072. Chapter 13. Summary.mp44.9 MB
146. 073. Chapter 14. Text classification.en.srt12.4 KB
147. 073. Chapter 14. Text classification.mp427.9 MB
148. 074. Chapter 14. Preparing text data.en.srt23.6 KB
149. 074. Chapter 14. Preparing text data.mp440.9 MB
150. 075. Chapter 14. Sets vs. sequences.en.srt7.8 KB
151. 075. Chapter 14. Sets vs. sequences.mp413.3 MB
152. 076. Chapter 14. Set models.en.srt13.6 KB
153. 076. Chapter 14. Set models.mp425.2 MB
154. 077. Chapter 14. Sequence models.en.srt35.6 KB
155. 077. Chapter 14. Sequence models.mp457.5 MB
156. 078. Chapter 14. Summary.en.srt2.0 KB
157. 078. Chapter 14. Summary.mp43.6 MB
158. 079. Chapter 15. Language models and the Transformer.en.srt16.2 KB
159. 079. Chapter 15. Language models and the Transformer.mp429.4 MB
160. 080. Chapter 15. Sequence-to-sequence learning.en.srt14.5 KB
161. 080. Chapter 15. Sequence-to-sequence learning.mp429.0 MB
162. 081. Chapter 15. The Transformer architecture.en.srt37.6 KB
163. 081. Chapter 15. The Transformer architecture.mp463.3 MB
164. 082. Chapter 15. Classification with a pretrained Transformer.en.srt19.0 KB
165. 082. Chapter 15. Classification with a pretrained Transformer.mp433.5 MB
166. 083. Chapter 15. What makes the Transformer effective.en.srt12.1 KB
167. 083. Chapter 15. What makes the Transformer effective.mp425.2 MB
168. 084. Chapter 15. Summary.en.srt2.9 KB
169. 084. Chapter 15. Summary.mp47.2 MB
170. 085. Chapter 16. Text generation.en.srt13.7 KB
171. 085. Chapter 16. Text generation.mp424.9 MB
172. 086. Chapter 16. Training a mini-GPT.en.srt29.9 KB
173. 086. Chapter 16. Training a mini-GPT.mp453.7 MB
174. 087. Chapter 16. Using a pretrained LLM.en.srt21.2 KB
175. 087. Chapter 16. Using a pretrained LLM.mp433.3 MB
176. 088. Chapter 16. Going further with LLMs.en.srt27.6 KB
177. 088. Chapter 16. Going further with LLMs.mp446.6 MB
178. 089. Chapter 16. Where are LLMs heading next.en.srt5.0 KB
179. 089. Chapter 16. Where are LLMs heading next.mp49.3 MB
180. 090. Chapter 16. Summary.en.srt2.5 KB
181. 090. Chapter 16. Summary.mp43.9 MB
182. 091. Chapter 17. Image generation.en.srt20.3 KB
183. 091. Chapter 17. Image generation.mp437.1 MB
184. 092. Chapter 17. Diffusion models.en.srt17.5 KB
185. 092. Chapter 17. Diffusion models.mp431.6 MB
186. 093. Chapter 17. Text-to-image models.en.srt13.5 KB
187. 093. Chapter 17. Text-to-image models.mp423.6 MB
188. 094. Chapter 17. Summary.en.srt1.9 KB
189. 094. Chapter 17. Summary.mp44.0 MB
190. 095. Chapter 18. Best practices for the real world.en.srt32.0 KB
191. 095. Chapter 18. Best practices for the real world.mp446.4 MB
192. 096. Chapter 18. Scaling up model training with multiple devices.en.srt25.4 KB
193. 096. Chapter 18. Scaling up model training with multiple devices.mp441.8 MB
194. 097. Chapter 18. Speeding up training and inference with lower-precision computation.en.srt18.5 KB
195. 097. Chapter 18. Speeding up training and inference with lower-precision computation.mp430.7 MB
196. 098. Chapter 18. Summary.en.srt1.1 KB
197. 098. Chapter 18. Summary.mp43.2 MB
198. 099. Chapter 19. The future of AI.en.srt21.7 KB
199. 099. Chapter 19. The future of AI.mp443.3 MB
200. 100. Chapter 19. Scale isn t all you need.en.srt22.2 KB
201. 100. Chapter 19. Scale isn t all you need.mp449.7 MB
202. 101. Chapter 19. How to build intelligence.en.srt28.2 KB
203. 101. Chapter 19. How to build intelligence.mp456.3 MB
204. 102. Chapter 19. The missing ingredients - Search and symbols.en.srt36.1 KB
205. 102. Chapter 19. The missing ingredients - Search and symbols.mp470.4 MB
206. 103. Chapter 20. Conclusions.en.srt31.0 KB
207. 103. Chapter 20. Conclusions.mp466.7 MB
208. 104. Chapter 20. Limitations of deep learning.en.srt4.6 KB
209. 104. Chapter 20. Limitations of deep learning.mp48.6 MB
210. 105. Chapter 20. What might lie ahead.en.srt3.3 KB
211. 105. Chapter 20. What might lie ahead.mp47.0 MB
212. 106. Chapter 20. Staying up to date in a fast-moving field.en.srt5.6 KB
213. 106. Chapter 20. Staying up to date in a fast-moving field.mp411.5 MB
214. 107. Chapter 20. Final words.en.srt764 bytes
215. 107. Chapter 20. Final words.mp41.5 MB
216. Bonus Resources.txt70 bytes

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