Machine Learning with TensorFlow on Google Cloud

Files Included :

1 Introduction.mp4 (32.57 MB)
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1 Linear regression basics.mp4 (52.85 MB)
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2 Logistic regression basics.mp4 (62.82 MB)
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1 Single Neural Cell.mp4 (15.24 MB)
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2 Example of a Perceptron.mp4 (39.75 MB)
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3 What are Activation Functions.mp4 (15.13 MB)
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4 Sigmoid Activation Function.mp4 (25.78 MB)
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5 Linear regression case study.mp4 (12.65 MB)
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6 Linear regression case study - demonstration.mp4 (133.97 MB)
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7 Logistic regression case study.mp4 (16.67 MB)
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8 Logistic regression case study - demonstration.mp4 (97.87 MB)
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1 Parallel vs Sequential Stacking.mp4 (29.03 MB)
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2 Important terms.mp4 (19.57 MB)
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3 How Neural Networks work.mp4 (22.51 MB)
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4 Finding the optima using Gradient Descent.mp4 (28.72 MB)
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5 Concept Behind Using Gradient Descent.mp4 (18.05 MB)
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6 Types and Uses of Activation Functions.mp4 (26.46 MB)
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7 Multiclass Classification.mp4 (22.86 MB)
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8 Difference Between Gradient Descent and Stochastic Gradient Descent.mp4 (13.74 MB)
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9 Epochs.mp4 (8.33 MB)
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1 Dataset for classification.mp4 (65.18 MB)
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2 Normalization and Test-Train split.mp4 (41.96 MB)
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3 Different ways to create ANN.mp4 (11.25 MB)
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4 Building the Neural Network.mp4 (76.77 MB)
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5 Compiling and Training the Neural Network model.mp4 (75.41 MB)
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6 Evaluating performance and Predicting.mp4 (49.07 MB)
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7 Building Neural Network for Regression Problem.mp4 (151.68 MB)
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8 Complex ANN Architectures using Functional API.mp4 (115.84 MB)
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