365 Data Science - Deep Learning with TensorFlow [CoursesGhar]

Creation Time	Aug. 21, 2023, 9:58 a.m.
Last Access Time	Nov. 27, 2024, 9:07 p.m.
File Size	2.5 GB
Keywords	CoursesGhar with Science - Deep Learning TensorFlow 365 Data
Total Requests	75
Total Files	90
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13. Business case/4. Preprocessing the data.mp4 78.7 MB
14. Conclusion/3. An overview of CNNs.mp4 72.0 MB
6. Deep nets overview/3. Really understand deep nets.mp4 58.2 MB
13. Business case/10. Homework.mp4 57.5 MB
13. Business case/5. Creating the batching class.mp4 56.6 MB
2. Neural networks Intro/11. One-parameter gradient descent.mp4 56.4 MB
12. Deeper example/9. Commenting on the results.mp4 52.8 MB
6. Deep nets overview/7. Backpropagation.mp4 52.7 MB
13. Business case/1. The dataset.mp4 52.6 MB
2. Neural networks Intro/12. N-parameter gradient descent.mp4 50.0 MB
1. Introduction/2. What does the course cover.mp4 49.2 MB
14. Conclusion/1. Summary.mp4 48.5 MB
4. Minimal example/4. Training the model.mp4 46.6 MB
13. Business case/6. Outlining the model.mp4 46.3 MB
12. Deeper example/4. MNIST - Outlining the model.mp4 45.0 MB
13. Business case/3. Balancing a dataset.mp4 44.9 MB
5. Introduction to TensorFlow/1. TensorFlow outline.mp4 44.6 MB
2. Neural networks Intro/1. Introduction to neural networks.mp4 42.6 MB
2. Neural networks Intro/6. The linear model. Multiple inputs and multiple outputs.mp4 42.2 MB
14. Conclusion/5. Non-NN approaches.mp4 41.9 MB
2. Neural networks Intro/3. Types of machine learning.mp4 40.8 MB
11. Preprocessing/3. Standardization.mp4 40.4 MB
12. Deeper example/6. Accuracy of a model.mp4 40.2 MB
6. Deep nets overview/4. Why do we need non-linearities.mp4 38.0 MB
1. Introduction/1. Welcome to Machine Learning.mp4 37.9 MB
8. Overfitting/3. Train vs validation.mp4 37.5 MB
3. Setting up the environment/3. Installing Anaconda.mp4 37.1 MB
10. Optimizers/4. Learning rate schedules.mp4 37.1 MB
3. Setting up the environment/2. Why Python and why Jupyter.mp4 34.7 MB
10. Optimizers/1. SGD_Batching.mp4 34.5 MB
8. Overfitting/1. Underfitting and overfitting.mp4 34.1 MB
12. Deeper example/8. Optimization.mp4 34.0 MB
2. Neural networks Intro/10. Cross-entropy loss.mp4 33.4 MB
6. Deep nets overview/2. What is a deep net.mp4 32.6 MB
8. Overfitting/2. Underfitting and overfitting. A classification example.mp4 32.5 MB
11. Preprocessing/5. One-hot vs binary.mp4 32.3 MB
8. Overfitting/4. Train vs validation vs test.mp4 31.3 MB
12. Deeper example/2. How to tackle the MNIST dataset.mp4 30.9 MB
5. Introduction to TensorFlow/6. Output.mp4 30.2 MB
10. Optimizers/6. Adaptive learning schedules.mp4 29.8 MB
6. Deep nets overview/5. Activation functions.mp4 29.2 MB
10. Optimizers/7. Adaptive moment estimation.mp4 29.1 MB
8. Overfitting/6. Early stopping - motivation and types.mp4 28.3 MB
5. Introduction to TensorFlow/4. Laying down the model.mp4 27.7 MB
13. Business case/7. Optimizing the algorithm.mp4 27.5 MB
14. Conclusion/4. An overview of RNNs.mp4 27.4 MB
2. Neural networks Intro/2. Training the model.mp4 26.8 MB
9. Initialization/1. Initializaiton.mp4 26.2 MB
2. Neural networks Intro/4. The linear model.mp4 26.0 MB
8. Overfitting/5. N-fold cross validation.mp4 25.6 MB
11. Preprocessing/1. Preprocessing.mp4 25.6 MB
6. Deep nets overview/6. Softmax activation.mp4 25.0 MB
6. Deep nets overview/8. Backpropagation - intuition.mp4 24.4 MB
4. Minimal example/2. Generating the data (optional).mp4 23.7 MB
2. Neural networks Intro/5. The linear model. Multiple inputs..mp4 23.7 MB
2. Neural networks Intro/7. Graphical representation.mp4 22.0 MB
5. Introduction to TensorFlow/5. Laying down the optimizers.mp4 21.5 MB
2. Neural networks Intro/9. L2-norm loss.mp4 21.4 MB
3. Setting up the environment/5. Jupyter Dashboard - Part 2.mp4 21.0 MB
4. Minimal example/3. Initializing the variables.mp4 20.4 MB
13. Business case/8. Running the code.mp4 19.9 MB
5. Introduction to TensorFlow/2. TensorFlow introduction.mp4 19.3 MB
9. Initialization/3. Xavier_s initialization.mp4 19.1 MB
10. Optimizers/3. Momentum.mp4 19.0 MB
12. Deeper example/5. MNIST - Declaring the loss.mp4 18.7 MB
11. Preprocessing/4. Dealing with categorical data.mp4 18.2 MB
2. Neural networks Intro/8. The objective function.mp4 17.7 MB
14. Conclusion/2. Whats more out there.mp4 17.5 MB
6. Deep nets overview/1. The layer.mp4 16.4 MB
10. Optimizers/2. Local minima pitfalls.mp4 14.3 MB
3. Setting up the environment/6. Installing the TensorFlow package.mp4 14.1 MB
4. Minimal example/1. Outline.mp4 13.9 MB
5. Introduction to TensorFlow/3. Types of file formats used in TensorFlow.mp4 12.9 MB
12. Deeper example/3. MNIST - Importing libraries and data.mp4 12.7 MB
9. Initialization/2. Types of simple initializations.mp4 12.3 MB
13. Business case/2. Outlining the solution.mp4 12.1 MB
11. Preprocessing/2. Basic preprocessing.mp4 11.1 MB
10. Optimizers/5. Learning rate schedules. A picture.mp4 10.9 MB
3. Setting up the environment/4. Jupyter Dashboard - Part 1.mp4 10.3 MB
12. Deeper example/7. Early stopping and batching preparation.mp4 9.9 MB
13. Business case/9. Test.mp4 8.8 MB
12. Deeper example/1. MNIST dataset.mp4 8.5 MB
3. Setting up the environment/1. Setting up the environment - Do not skip, please!.mp4 7.9 MB
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365 Data Science - Deep Learning with TensorFlow [CoursesGhar]

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