2.12 GB | 00:31:18 | mp4 | 1920X1080  | 16:9
Genre:eLearning |Language:English

Files Included :
001  Chapter 1  What is deep learning  (79.53 MB)
002  Chapter 1  Before deep learning A brief history of machine learning  (47.83 MB)
003  Chapter 1  Why deep learning Why now  (36.86 MB)
004  Chapter 2  The mathematical building blocks of neural networks  (22.29 MB)
005  Chapter 2  Data representations for neural networks  (35.86 MB)
006  Chapter 2  The gears of neural networks Tensor operations  (32.98 MB)
007  Chapter 2  The engine of neural networks Gradient-based optimization  (63.63 MB)
008  Chapter 2  Looking back at our first example  (19.37 MB)
009  Chapter 2  Summary  (3.92 MB)
010  Chapter 3  Introduction to Keras and TensorFlow  (8.59 MB)
011  Chapter 3  What s Keras  (9.04 MB)
012  Chapter 3  Keras and TensorFlow A brief history  (5.9 MB)
013  Chapter 3  Python and R interfaces A brief history  (2.22 MB)
014  Chapter 3  Setting up a deep learning workspace  (11.54 MB)
015  Chapter 3  First steps with TensorFlow  (5.09 MB)
016  Chapter 3  Tensor attributes  (46.5 MB)
017  Chapter 3  Anatomy of a neural network Understanding core Keras APIs  (48.64 MB)
018  Chapter 3  Summary  (3.46 MB)
019  Chapter 4  Getting started with neural networks Classification and regression  (44.74 MB)
020  Chapter 4  Classifying newswires A multiclass classification example  (24.88 MB)
021  Chapter 4  Predicting house prices A regression example  (25.84 MB)
022  Chapter 4  Summary  (1.79 MB)
023  Chapter 5  Fundamentals of machine learning  (50.36 MB)
024  Chapter 5  Evaluating machine learning models  (24.29 MB)
025  Chapter 5  Improving model fit  (14.73 MB)
026  Chapter 5  Improving generalization  (42.72 MB)
027  Chapter 5  Summary  (5.51 MB)
028  Chapter 6  The universal workflow of machine learning  (55.06 MB)
029  Chapter 6  Develop a model  (31.99 MB)
030  Chapter 6  Deploy the model  (36.23 MB)
031  Chapter 6  Summary  (3.29 MB)
032  Chapter 7  Working with Keras A deep dive  (6.06 MB)
033  Chapter 7  Different ways to build Keras models  (37.11 MB)
034  Chapter 7  Using built-in training and evaluation loops  (29.5 MB)
035  Chapter 7  Writing your own training and evaluation loops  (32.42 MB)
036  Chapter 7  Summary  (2.76 MB)
037  Chapter 8  Introduction to deep learning for computer vision  (43.59 MB)
038  Chapter 8  Training a convnet from scratch on a small dataset  (50.91 MB)
039  Chapter 8  Leveraging a pretrained model  (41.16 MB)
040  Chapter 8  Summary  (2.45 MB)
041  Chapter 9  Advanced deep learning for computer vision  (9 MB)
042  Chapter 9  An image segmentation example  (27.85 MB)
043  Chapter 9  Modern convnet architecture patterns  (68.16 MB)
044  Chapter 9  Interpreting what convnets learn  (50.5 MB)
045  Chapter 9  Summary  (1.92 MB)
046  Chapter 10  Deep learning for time series  (9.38 MB)
047  Chapter 10  A temperature-forecasting example  (42.8 MB)
048  Chapter 10  Understanding recurrent neural networks  (30.39 MB)
049  Chapter 10  Advanced use of recurrent neural networks  (41.18 MB)
050  Chapter 10  Summary  (4.04 MB)
051  Chapter 11  Deep learning for text  (18.47 MB)
052  Chapter 11  Preparing text data  (34.82 MB)
053  Chapter 11  Two approaches for representing groups of words Sets and sequences  (77.34 MB)
054  Chapter 11  The Transformer architecture  (54.02 MB)
055  Chapter 11  Beyond text classification Sequence-to-sequence learning  (51.63 MB)
056  Chapter 11  Summary  (4.78 MB)
057  Chapter 12  Generative deep learning  (79.33 MB)
058  Chapter 12  DeepDream  (23.73 MB)
059  Chapter 12  Neural style transfer  (33.91 MB)
060  Chapter 12  Generating images with variational autoencoders  (34.39 MB)
061  Chapter 12  Introduction to generative adversarial networks  (43.27 MB)
062  Chapter 12  Summary  (2.52 MB)
063  Chapter 13  Best practices for the real world  (53.81 MB)
064  Chapter 13  Scaling-up model training  (48.23 MB)
065  Chapter 13  Summary  (2.41 MB)
066  Chapter 14  Conclusions  (72.26 MB)
067  Chapter 14  The limitations of deep learning  (56.08 MB)
068  Chapter 14  Setting the course toward greater generality in AI  (23.02 MB)
069  Chapter 14  Implementing intelligence The missing ingredients  (33.7 MB)
070  Chapter 14   The future of deep learning  (36.74 MB)
071  Chapter 14  Staying up-to-date in a fast-moving field  (13.31 MB)
072  Chapter 14   Final words  (1.6 MB)
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