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Introduction to Deep Learning with PyTorch by IBM on Coursera.

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Name		Name	Last commit message	Last commit date
Latest commit History 1 Commit
1.1_1Dtensors_v2.ipynb		1.1_1Dtensors_v2.ipynb
1.1_2 Two-Dimensional Tensors_v2.ipynb		1.1_2 Two-Dimensional Tensors_v2.ipynb
1.2derivativesandGraphsinPytorch_v2.ipynb		1.2derivativesandGraphsinPytorch_v2.ipynb
1.3.1_simple_data_set_v2.ipynb		1.3.1_simple_data_set_v2.ipynb
1.3.2_Datasets_and_transforms.ipynb		1.3.2_Datasets_and_transforms.ipynb
1.3.3_pre-Built Datasets_and_transforms_v2.ipynb		1.3.3_pre-Built Datasets_and_transforms_v2.ipynb
2.1Prediction1Dregression_v3.ipynb		2.1Prediction1Dregression_v3.ipynb
2.2_linear_regression_one_parameter_v3.ipynb		2.2_linear_regression_one_parameter_v3.ipynb
2.3_training_slope_and_bias_v3.ipynb		2.3_training_slope_and_bias_v3.ipynb
3.1_stochastic_gradient_descent_v3.ipynb		3.1_stochastic_gradient_descent_v3.ipynb
3.2_mini-batch_gradient_descent_v3.ipynb		3.2_mini-batch_gradient_descent_v3.ipynb
3.3_PyTorchway_v3.ipynb		3.3_PyTorchway_v3.ipynb
3.6_training_and_validation_v3.ipynb		3.6_training_and_validation_v3.ipynb
4.1.multiple_linear_regression_prediction_v2.ipynb		4.1.multiple_linear_regression_prediction_v2.ipynb
4.2.multiple_linear_regression_training_v2.ipynb		4.2.multiple_linear_regression_training_v2.ipynb
4.3.multi-target_linear_regression.ipynb		4.3.multi-target_linear_regression.ipynb
4.4.training_multiple_output_linear_regression.ipynb		4.4.training_multiple_output_linear_regression.ipynb
5.1logistic_regression_prediction_v2.ipynb		5.1logistic_regression_prediction_v2.ipynb
5.2.2bad_inshilization_logistic_regression_with_mean_square_error_v2.ipynb		5.2.2bad_inshilization_logistic_regression_with_mean_square_error_v2.ipynb
5.3_cross_entropy_logistic_regression_v2.ipynb		5.3_cross_entropy_logistic_regression_v2.ipynb
5.4softmax_in_one_dimension_v2.ipynb		5.4softmax_in_one_dimension_v2.ipynb
6.2lab_predicting _MNIST_using_Softmax_v2.ipynb		6.2lab_predicting _MNIST_using_Softmax_v2.ipynb
7.1_simple1hiddenlayer.ipynb		7.1_simple1hiddenlayer.ipynb
7.2multiple_neurons.ipynb		7.2multiple_neurons.ipynb
7.3xor_v2.ipynb		7.3xor_v2.ipynb
7.4one_layer_neural_network_MNIST.ipynb		7.4one_layer_neural_network_MNIST.ipynb
7.5.1activationfuction_v2.ipynb		7.5.1activationfuction_v2.ipynb
7.5.2mist1layer_v2.ipynb		7.5.2mist1layer_v2.ipynb
8.1.1mist2layer_v2.ipynb		8.1.1mist2layer_v2.ipynb
8.1.2mulitclassspiralrulu_v2.ipynb		8.1.2mulitclassspiralrulu_v2.ipynb
8.2.1dropoutPredictin_v2.ipynb		8.2.1dropoutPredictin_v2.ipynb
8.2.2dropoutRegression_v2.ipynb		8.2.2dropoutRegression_v2.ipynb
8.3.1.initializationsame.ipynb		8.3.1.initializationsame.ipynb
8.3.2Xaviermist1layer_v2.ipynb		8.3.2Xaviermist1layer_v2.ipynb
8.3.3.He_Initialization_v2.ipynb		8.3.3.He_Initialization_v2.ipynb
8.4.1_MomentumwithPolynomialFunctions_v2.ipynb		8.4.1_MomentumwithPolynomialFunctions_v2.ipynb
8.4.2_NeuralNetworkswithMomentum_v2.ipynb		8.4.2_NeuralNetworkswithMomentum_v2.ipynb
8.5.1BachNorm_v2.ipynb		8.5.1BachNorm_v2.ipynb
9.1What_is_Convolution.ipynb		9.1What_is_Convolution.ipynb
9.2Activation_max_pooling.ipynb		9.2Activation_max_pooling.ipynb
9.3Multiple Channel Convolution.ipynb		9.3Multiple Channel Convolution.ipynb
9.4.1ConvolutionalNeralNetworkSimple example.ipynb		9.4.1ConvolutionalNeralNetworkSimple example.ipynb
9.4.2CNN_Small_Image.ipynb		9.4.2CNN_Small_Image.ipynb
9.4.3CNN_Small_Image_batch.ipynb		9.4.3CNN_Small_Image_batch.ipynb
README.md		README.md
index.csv		index.csv

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