Coursera - Introduction to Machine Learning

Files Included :

02 why-machine-learning-is-exciting.mp4 (13.61 MB)
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03 what-is-machine-learning.mp4 (12.61 MB)
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05 logistic-regression.mp4 (21.79 MB)
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06 interpretation-of-logistic-regression.mp4 (19.3 MB)
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08 motivation-for-multilayer-perceptron.mp4 (9.43 MB)
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01 multilayer-perceptron-concepts.mp4 (12.06 MB)
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02 multilayer-perceptron-math-model.mp4 (12.24 MB)
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04 deep-learning.mp4 (18.56 MB)
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05 example-document-analysis.mp4 (9.49 MB)
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06 interpretation-of-multilayer-perceptron.mp4 (16.17 MB)
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07 transfer-learning.mp4 (17.64 MB)
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09 model-selection.mp4 (22.15 MB)
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11 early-history-of-neural-networks.mp4 (27.38 MB)
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01 hierarchical-structure-of-images.mp4 (14.94 MB)
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02 convolution-filters.mp4 (14.09 MB)
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03 convolutional-neural-network.mp4 (8.49 MB)
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05 cnn-math-model.mp4 (8.83 MB)
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06 how-the-model-learns.mp4 (18.08 MB)
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07 advantages-of-hierarchical-features.mp4 (12.27 MB)
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01 cnn-on-real-images.mp4 (25.57 MB)
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02 applications-in-use-and-practice.mp4 (27.46 MB)
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03 deep-learning-and-transfer-learning.mp4 (23.22 MB)
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01 introduction-to-pytorch.mp4 (16.33 MB)
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01 how-do-we-define-learning.mp4 (41.04 MB)
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02 how-do-we-evaluate-our-networks.mp4 (25.54 MB)
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01 how-do-we-learn-our-network.mp4 (18 MB)
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02 how-do-we-handle-big-data.mp4 (28.33 MB)
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03 early-stopping.mp4 (8.19 MB)
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02 model-learning-with-pytorch.mp4 (5.68 MB)
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01 motivation-diabetic-retinopathy.mp4 (18.73 MB)
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02 breakdown-of-the-convolution-1d-and-2d.mp4 (12.77 MB)
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01 core-components-of-the-convolutional-layer.mp4 (12.34 MB)
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02 activation-functions.mp4 (6.38 MB)
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03 pooling-and-fully-connected-layers.mp4 (6.65 MB)
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01 training-the-network.mp4 (9.29 MB)
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02 transfer-learning-and-fine-tuning.mp4 (7.04 MB)
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02 cnn-with-pytorch.mp4 (3.69 MB)
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01 introduction-to-the-concept-of-word-vectors.mp4 (17.13 MB)
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02 words-to-vectors.mp4 (19.36 MB)
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03 example-of-word-embeddings.mp4 (45.3 MB)
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01 neural-model-of-text.mp4 (37.24 MB)
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02 the-softmax-function.mp4 (18.51 MB)
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03 methods-for-learning-model-parameters.mp4 (18.01 MB)
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04 more-details-on-how-to-learn-model-parameters.mp4 (20.81 MB)
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01 the-recurrent-neural-network.mp4 (19.31 MB)
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02 long-short-term-memory.mp4 (38.38 MB)
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03 long-short-term-memory-review.mp4 (24.57 MB)
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04 use-of-lstm-for-text-synthesis.mp4 (19.95 MB)
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01 simple-and-effective-alternative-methods-for-neural-nlp.mp4 (45.36 MB)
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01 natural-language-processing-with-pytorch.mp4 (4.22 MB)
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01 word-vectors-and-their-interpretation.mp4 (14.33 MB)
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02 relationships-between-word-vectors.mp4 (9.99 MB)
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03 inner-products-between-word-vectors.mp4 (13.63 MB)
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04 intuition-into-meaning-of-inner-products-of-word-vectors.mp4 (16.64 MB)
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01 introduction-of-attention-mechanism.mp4 (20.87 MB)
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02 queries-keys-and-values-of-attention-network.mp4 (14.64 MB)
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03 self-attention-and-positional-encodings.mp4 (33.24 MB)
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01 attention-based-sequence-encoder.mp4 (20.73 MB)
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02 coupling-the-sequence-encoder-and-decoder.mp4 (24.81 MB)
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03 cross-attention-in-the-sequence-to-sequence-model.mp4 (7.11 MB)
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01 multi-head-attention.mp4 (19.63 MB)
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02 the-complete-transformer-network.mp4 (27.92 MB)
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01 introduction-to-reinforcement-learning.mp4 (18.51 MB)
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02 reinforcement-learning-problem-setup.mp4 (31 MB)
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03 example-of-reinforcement-learning-in-practice.mp4 (44.1 MB)
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05 reinforcement-learning-with-pytorch.mp4 (5.12 MB)
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01 moving-to-a-non-myopic-policy.mp4 (21.89 MB)
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02 q-learning.mp4 (21.06 MB)
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03 extensions-of-q-learning.mp4 (33.5 MB)
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01 limitations-of-q-learning-and-introduction-to-deep-q-learning.mp4 (21.29 MB)
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02 deep-q-learning-based-on-images.mp4 (29.31 MB)
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03 connecting-deep-q-learning-with-conventional-q-learning.mp4 (22.17 MB)
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