SCRiPTMAFiA.ORG > Practical Deep Learning with Python

Files Included :
02 course-introduction.mp4 (16.72 MB)
03 environment-configuration.mp4 (11.19 MB)
01 machine-learning-vs-deep-learning.mp4 (20.38 MB)
02 what-is-deep-learning.mp4 (11.82 MB)
03 neural-networks.mp4 (24.43 MB)
04 artificial-neural-network-ann.mp4 (16.15 MB)
05 ann-types-and-applications.mp4 (11.34 MB)
06 forward-propagation.mp4 (13.19 MB)
07 perceptron.mp4 (20.54 MB)
08 learning-rate.mp4 (19.98 MB)
09 what-is-activation-function.mp4 (11.81 MB)
10 activation-function-and-its-types.mp4 (14.65 MB)
11 importance-of-epoch.mp4 (15.95 MB)
12 single-layer-perceptron-define-sigmoid-function.mp4 (25.28 MB)
13 single-layer-perceptron-decision-boundary.mp4 (42.76 MB)
01 limitations-of-single-layered-perceptron.mp4 (6.86 MB)
02 multi-layered-perceptron.mp4 (7.72 MB)
03 what-is-backpropagation.mp4 (6.49 MB)
04 backpropagation.mp4 (10.45 MB)
05 demonstration-building-a-simple-neural-network.mp4 (20.81 MB)
06 demonstration-understanding-how-backpropagation-has-worked.mp4 (22.74 MB)
07 demonstration-handwritten-digits-classification-data-preprocessing.mp4 (22.63 MB)
08 demonstration-handwritten-digits-classification-designing-the-model.mp4 (38.07 MB)
09 demonstration-handwritten-digits-classification-optimizing-the-model.mp4 (44.62 MB)
01 summary-of-deep-learning-components.mp4 (21.66 MB)
01 limitations-of-mlp.mp4 (16.37 MB)
02 mlp-limitations-resolving-the-issue-with-cnn.mp4 (12.96 MB)
03 visual-cortex-and-cnn.mp4 (20.59 MB)
04 convolutional-layer.mp4 (20.38 MB)
05 working-of-convolutional-layer.mp4 (20.38 MB)
06 demonstration-load-and-preprocess-the-data.mp4 (24.16 MB)
07 demonstration-designing-the-model.mp4 (29.4 MB)
08 demonstration-building-the-cnn-model.mp4 (22.04 MB)
09 demonstration-model-accuracy.mp4 (12.09 MB)
10 demonstration-adding-more-layers.mp4 (34.12 MB)
11 demonstration-building-basic-cnn-model-with-new-parameters.mp4 (42.94 MB)
12 demonstration-pre-trained-model.mp4 (21.2 MB)
01 classification-and-object-detection.mp4 (19.19 MB)
02 introduction-to-rcnn.mp4 (19.12 MB)
03 r-cnn-bounding-box-regression.mp4 (7.65 MB)
04 pre-trained-model.mp4 (18.68 MB)
05 fast-regional-cnn.mp4 (17.77 MB)
06 demonstration-creating-base-variables-and-loading-the-model.mp4 (20.23 MB)
07 demonstration-training-the-model-and-visualizing-the-predictions.mp4 (29.08 MB)
08 demonstration-svm-as-a-classifier.mp4 (13.57 MB)
01 fast-rcnn-limitations.mp4 (16 MB)
02 advent-of-faster-r-cnn.mp4 (17.05 MB)
03 tensorflow-hub.mp4 (12.52 MB)
04 demonstration-object-detection-with-faster-rcnn-pretrained-model-setup.mp4 (41.06 MB)
05 demonstration-object-detection-with-faster-rcnn-building-the-model.mp4 (43.66 MB)
01 summary-of-cnn-in-deep-learning.mp4 (8.47 MB)
02 summary-of-faster-rcnn.mp4 (13.77 MB)
01 rnn-fundamentals.mp4 (13.83 MB)
02 rnn-architecture.mp4 (13.94 MB)
03 rnn-architecture-workflow.mp4 (17.74 MB)
04 implementing-rnn.mp4 (19.15 MB)
05 demonstration-rnn-dataset-preparation.mp4 (33.66 MB)
06 demonstration-rnn-building-the-model.mp4 (34.82 MB)
01 basics-of-lstm.mp4 (18.39 MB)
02 lstm-structure.mp4 (16.28 MB)
03 forget-gate-and-input-gate.mp4 (14.39 MB)
04 output-gate.mp4 (8.74 MB)
05 importance-of-lstm-architecture.mp4 (14.33 MB)
06 types-of-lstm.mp4 (12.05 MB)
07 demonstration-next-word-prediction-processing-the-corpus.mp4 (28.32 MB)
08 demonstration-next-word-prediction-layers.mp4 (32.79 MB)
09 demonstration-next-word-prediction-model-compilation-and-prediction.mp4 (52.14 MB)
01 improving-a-model.mp4 (20.63 MB)
02 model-optimization.mp4 (13.69 MB)
03 using-adam-optimizer.mp4 (20.41 MB)
04 model-compilation.mp4 (9.68 MB)
05 model-compilation-with-popular-frameworks.mp4 (16.32 MB)
06 demonstration-model-compilation-preparing-the-dataset.mp4 (29.98 MB)
07 demonstration-building-and-compiling-model.mp4 (25.61 MB)
08 demonstration-from-rmsprop-to-adam.mp4 (24.64 MB)
01 summary-of-deep-learning-with-rnn-and-lstm-with-model-optimization.mp4 (19.64 MB)
01 course-summary-for-practical-deep-learning-with-python.mp4 (14.05 MB)]
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