Edureka Practical Deep Learning With Python 2025

2.45 GB | 00:07:14 | mp4 | 1920X1080 | 16:9
Genre:eLearning |Language:English

Files Included :
02-course introduction.mp4 (27.98 MB)
03-environment configuration.mp4 (21.81 MB)
01-machine learning vs deep learning.mp4 (34.27 MB)
02-what is deep learning.mp4 (20.31 MB)
03-neural networks.mp4 (42.16 MB)
04-artificial neural network ann.mp4 (24.4 MB)
05-ann types and applications.mp4 (17.78 MB)
06-forward propagation.mp4 (20.61 MB)
07-perceptron.mp4 (30.93 MB)
08-learning rate.mp4 (29.25 MB)
09-what is activation function.mp4 (17.83 MB)
10-activation function and its types.mp4 (23.41 MB)
11-importance of epoch.mp4 (24.78 MB)
12-single layer perceptron define sigmoid function.mp4 (44.01 MB)
13-single layer perceptron decision boundary.mp4 (77.15 MB)
01-limitations of single layered perceptron.mp4 (11.05 MB)
02-multi layered perceptron.mp4 (12.04 MB)
03-what is backpropagation.mp4 (10.26 MB)
04-backpropagation.mp4 (17 MB)
05-demonstration building a simple neural network.mp4 (40.88 MB)
06-demonstration understanding how backpropagation has worked.mp4 (40.45 MB)
07-demonstration handwritten digits classification data preprocessing.mp4 (41.79 MB)
08-demonstration handwritten digits classification designing the model.mp4 (73.21 MB)
09-demonstration handwritten digits classification optimizing the model.mp4 (88.77 MB)
01-summary of deep learning components.mp4 (36.33 MB)
01-limitations of mlp.mp4 (27.91 MB)
02-mlp limitations resolving the issue with cnn.mp4 (21.51 MB)
03-visual cortex and cnn.mp4 (31.61 MB)
04-convolutional layer.mp4 (31.99 MB)
05-working of convolutional layer.mp4 (31.99 MB)
06-demonstration load and preprocess the data.mp4 (42.04 MB)
07-demonstration designing the model.mp4 (52.84 MB)
08-demonstration building the cnn model.mp4 (37.97 MB)
09-demonstration model accuracy.mp4 (21.45 MB)
10-demonstration adding more layers.mp4 (62.39 MB)
11-demonstration building basic cnn model with new parameters.mp4 (78.21 MB)
12-demonstration pre trained model.mp4 (37.38 MB)
01-classification and object detection.mp4 (29.81 MB)
02-introduction to rcnn.mp4 (31.51 MB)
03-r cnn bounding box regression.mp4 (12.46 MB)
04-pre trained model.mp4 (29.04 MB)
05-fast regional cnn.mp4 (32.1 MB)
06-demonstration creating base variables and loading the model.mp4 (37 MB)
08-demonstration svm as a classifier.mp4 (23.4 MB)
01-fast rcnn limitations.mp4 (24.9 MB)
02-advent of faster r cnn.mp4 (25.24 MB)
03-tensorflow hub.mp4 (20.32 MB)
01-summary of cnn in deep learning.mp4 (13.32 MB)
02-summary of faster rcnn.mp4 (22.48 MB)
01-rnn fundamentals.mp4 (20.5 MB)
02-rnn architecture.mp4 (22.59 MB)
03-rnn architecture workflow.mp4 (28.92 MB)
04-implementing rnn.mp4 (28.87 MB)
05-demonstration rnn dataset preparation.mp4 (62.04 MB)
06-demonstration rnn building the model.mp4 (62.37 MB)
01-basics of lstm.mp4 (28.36 MB)
02-lstm structure.mp4 (24.24 MB)
03-forget gate and input gate.mp4 (20.87 MB)
04-output gate.mp4 (14.09 MB)
05-importance of lstm architecture.mp4 (23.04 MB)
06-types of lstm.mp4 (19.16 MB)
07-demonstration next word prediction processing the corpus.mp4 (50.16 MB)
08-demonstration next word prediction layers.mp4 (58.92 MB)
09-demonstration next word prediction model compilation and prediction.mp4 (96.56 MB)
01-improving a model.mp4 (32.93 MB)
02-model optimization.mp4 (21.83 MB)
03-using adam optimizer.mp4 (31.96 MB)
04-model compilation.mp4 (14.37 MB)
05-model compilation with popular frameworks.mp4 (27.34 MB)
06-demonstration model compilation preparing the dataset.mp4 (55.53 MB)
07-demonstration building and compiling model.mp4 (46.26 MB)
08-demonstration from rmsprop to adam.mp4 (45.17 MB)
01-summary of deep learning with rnn and lstm with model optimization.mp4 (32.88 MB)
01-course summary for practical deep learning with python.mp4 (23.39 MB)]
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