Deep Learning Brasília - Lição 7

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Fonte : Wiki: lesson7

Lesson resources

Other links

Video timeline

  • 00:03:04 Review of last week lesson on RNNs,
    Part 1, what to expect in Part 2 (start date: 19/03/2018)

  • 00:08:48 Building the RNN model with ‘self.init_hidden(bs)’ and ‘self.h’, the “back prop through time (BPTT)” approach

  • 00:17:50 Creating mini-batches, “split in 64 equal size chunks” not “split in chunks of size 64”, questions on data augmentation and choosing a BPTT size, PyTorch QRNN

  • 00:23:41 Using the data formats for your API, changing your data format vs creating a new dataset class, ‘data.Field()’

  • 00:24:45 How to create Nietzsche training/validation data

  • 00:35:43 Dealing with PyTorch not accepting a “Rank 3 Tensor”, only Rank 2 or 4, ‘F.log_softmax()’

  • 00:44:05 Question on ‘F.tanh()’, tanh activation function,
    replacing the ‘RNNCell’ by ‘GRUCell’

  • 00:47:15 Intro to GRU cell (RNNCell has gradient explosion problem - i.e. you need to use low learning rate and small BPTT)

  • 00:53:40 Long Short Term Memory (LSTM), ‘LayerOptimizer()’, Cosine Annealing ‘CosAnneal()’

  • 01:01:47 Pause

  • 01:01:57 Back to Computer Vision with CIFAR 10 and ‘lesson7-cifar10.ipynb’ notebook, Why study research on CIFAR 10 vs ImageNet vs MNIST ?

  • 01:08:54 Looking at a Fully Connected Model, based on a notebook from student ‘Kerem Turgutlu’, then a CNN model (with Excel demo)

  • 01:21:54 Refactored the model with new class ‘ConvLayer()’ and ‘padding’

  • 01:25:40 Using Batch Normalization (BatchNorm) to make the model more resilient, ‘BnLayer()’ and ‘ConvBnNet()’

  • 01:36:02 Previous bug in ‘Mini net’ in ‘lesson5-movielens.ipynb’, and many questions on BatchNorm, Lesson 7 Cifar10, AI/DL researchers vs practioners, ‘Yann Lecun’ & ‘Ali Rahimi talk at NIPS 2017’ rigor/rigueur/theory/experiment.

  • 01:50:51 ‘Deep BatchNorm’

  • 01:52:43 Replace the model with ResNet, class ‘ResnetLayer()’, using ‘boosting’

  • 01:58:38 ‘Bottleneck’ layer with ‘BnLayer()’, ‘ResNet 2’ with ‘Resnet2()’, Skipping Connections.

  • 02:02:01 ‘lesson7-CAM.ipynb’ notebook, an intro to Part #2 using ‘Dogs v Cats’.

  • 02:08:55 Class Activation Maps (CAM) of ‘Dogs v Cats’.

  • 02:14:27 Questions to Jeremy: “Your journey into Deep Learning” and “How to keep up with important research for practioners”,
    “If you intend to come to Part 2, you are expected to master all the techniques in Part 1”, Jeremy’s advice to master Part 1 and help new students in the incoming MOOC version to be released in January 2018.

As fotos da turma da terça 15 de maio 2018 :slight_smile:

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As fotos da turma do sábado 19/05/2018 :slight_smile:

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