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365 Data Science - Deep Learning with TensorFlow [CoursesGhar]

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1. Introduction/1. Welcome to Machine Learning.mp4
38 MB
1. Introduction/2. What does the course cover.mp4
49 MB
10. Optimizers/1. SGD_Batching.mp4
34 MB
10. Optimizers/2. Local minima pitfalls.mp4
14 MB
10. Optimizers/3. Momentum.mp4
19 MB
10. Optimizers/4. Learning rate schedules.mp4
37 MB
10. Optimizers/5. Learning rate schedules. A picture.mp4
11 MB
10. Optimizers/6. Adaptive learning schedules.mp4
30 MB
10. Optimizers/7. Adaptive moment estimation.mp4
29 MB
11. Preprocessing/1. Preprocessing.mp4
26 MB
11. Preprocessing/2. Basic preprocessing.mp4
11 MB
11. Preprocessing/3. Standardization.mp4
40 MB
11. Preprocessing/4. Dealing with categorical data.mp4
18 MB
11. Preprocessing/5. One-hot vs binary.mp4
32 MB
12. Deeper example/1. MNIST dataset.mp4
8.5 MB
12. Deeper example/2. How to tackle the MNIST dataset.mp4
31 MB
12. Deeper example/3. MNIST - Importing libraries and data.mp4
13 MB
12. Deeper example/4. MNIST - Outlining the model.mp4
45 MB
12. Deeper example/5. MNIST - Declaring the loss.mp4
19 MB
12. Deeper example/6. Accuracy of a model.mp4
40 MB
12. Deeper example/7. Early stopping and batching preparation.mp4
9.9 MB
12. Deeper example/8. Optimization.mp4
34 MB
12. Deeper example/9. Commenting on the results.mp4
53 MB
13. Business case/1. The dataset.mp4
53 MB
13. Business case/10. Homework.mp4
58 MB
13. Business case/2. Outlining the solution.mp4
12 MB
13. Business case/3. Balancing a dataset.mp4
45 MB
13. Business case/4. Preprocessing the data.mp4
79 MB
13. Business case/5. Creating the batching class.mp4
57 MB
13. Business case/6. Outlining the model.mp4
46 MB
13. Business case/7. Optimizing the algorithm.mp4
28 MB
13. Business case/8. Running the code.mp4
20 MB
13. Business case/9. Test.mp4
8.8 MB
14. Conclusion/1. Summary.mp4
48 MB
14. Conclusion/2. Whats more out there.mp4
18 MB
14. Conclusion/3. An overview of CNNs.mp4
72 MB
14. Conclusion/4. An overview of RNNs.mp4
27 MB
14. Conclusion/5. Non-NN approaches.mp4
42 MB
2. Neural networks Intro/1. Introduction to neural networks.mp4
43 MB
2. Neural networks Intro/10. Cross-entropy loss.mp4
33 MB
2. Neural networks Intro/11. One-parameter gradient descent.mp4
56 MB
2. Neural networks Intro/12. N-parameter gradient descent.mp4
50 MB
2. Neural networks Intro/2. Training the model.mp4
27 MB
2. Neural networks Intro/3. Types of machine learning.mp4
41 MB
2. Neural networks Intro/4. The linear model.mp4
26 MB
2. Neural networks Intro/5. The linear model. Multiple inputs..mp4
24 MB
2. Neural networks Intro/6. The linear model. Multiple inputs and multiple outputs.mp4
42 MB
2. Neural networks Intro/7. Graphical representation.mp4
22 MB
2. Neural networks Intro/8. The objective function.mp4
18 MB
2. Neural networks Intro/9. L2-norm loss.mp4
21 MB
3. Setting up the environment/1. Setting up the environment - Do not skip, please!.mp4
7.9 MB
3. Setting up the environment/2. Why Python and why Jupyter.mp4
35 MB
3. Setting up the environment/3. Installing Anaconda.mp4
37 MB
3. Setting up the environment/4. Jupyter Dashboard - Part 1.mp4
10 MB
3. Setting up the environment/5. Jupyter Dashboard - Part 2.mp4
21 MB
3. Setting up the environment/6. Installing the TensorFlow package.mp4
14 MB
4. Minimal example/1. Outline.mp4
14 MB
4. Minimal example/2. Generating the data (optional).mp4
24 MB
4. Minimal example/3. Initializing the variables.mp4
20 MB
4. Minimal example/4. Training the model.mp4
47 MB
5. Introduction to TensorFlow/1. TensorFlow outline.mp4
45 MB
5. Introduction to TensorFlow/2. TensorFlow introduction.mp4
19 MB
5. Introduction to TensorFlow/3. Types of file formats used in TensorFlow.mp4
13 MB
5. Introduction to TensorFlow/4. Laying down the model.mp4
28 MB
5. Introduction to TensorFlow/5. Laying down the optimizers.mp4
22 MB
5. Introduction to TensorFlow/6. Output.mp4
30 MB
6. Deep nets overview/1. The layer.mp4
16 MB
6. Deep nets overview/2. What is a deep net.mp4
33 MB
6. Deep nets overview/3. Really understand deep nets.mp4
58 MB
6. Deep nets overview/4. Why do we need non-linearities.mp4
38 MB
6. Deep nets overview/5. Activation functions.mp4
29 MB
6. Deep nets overview/6. Softmax activation.mp4
25 MB
6. Deep nets overview/7. Backpropagation.mp4
53 MB
6. Deep nets overview/8. Backpropagation - intuition.mp4
24 MB
8. Overfitting/1. Underfitting and overfitting.mp4
34 MB
8. Overfitting/2. Underfitting and overfitting. A classification example.mp4
32 MB
8. Overfitting/3. Train vs validation.mp4
38 MB
8. Overfitting/4. Train vs validation vs test.mp4
31 MB
8. Overfitting/5. N-fold cross validation.mp4
26 MB
8. Overfitting/6. Early stopping - motivation and types.mp4
28 MB
9. Initialization/1. Initializaiton.mp4
26 MB
9. Initialization/2. Types of simple initializations.mp4
12 MB
9. Initialization/3. Xavier_s initialization.mp4
19 MB
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