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365 Data Science - Deep Learning with TensorFlow 2.0 [CoursesGhar]

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2.38 GB

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131f089907b4d1802e8da813f93df387333530dd

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28-06-2021 12:26

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File	Size
!! IMPORTANT Note !!.txt	298.00 B
!!! Please Support !!! [CoursesGhar.Com].txt	197.00 B
00. Websites You May Like/A1movies.com.pk.url	116.00 B
00. Websites You May Like/CoursesGhar.com.url	114.00 B
1. Introduction/1. Welcome to Machine Learning.mp4	37.95 MB
1. Introduction/2. What does the course cover.mp4	49.21 MB
10. Optimizers/1. SGD_Batching.mp4	34.48 MB
10. Optimizers/2. Local minima pitfalls.mp4	14.35 MB
10. Optimizers/3. Momentum.mp4	18.96 MB
10. Optimizers/4. Learning rate schedules.mp4	37.08 MB
10. Optimizers/5. Learning rate schedules. A picture.mp4	10.93 MB
10. Optimizers/6. Adaptive learning schedules.mp4	29.83 MB
10. Optimizers/7. Adaptive moment estimation.mp4	29.08 MB
11. Preprocessing/1. Preprocessing.mp4	25.55 MB
11. Preprocessing/2. Basic preprocessing.mp4	11.11 MB
11. Preprocessing/3. Standardization.mp4	40.37 MB
11. Preprocessing/4. Dealing with categorical data.mp4	18.22 MB
11. Preprocessing/5. One-hot vs binary.mp4	32.26 MB
12. Deeper example/1. The dataset.mp4	8.53 MB
12. Deeper example/2. How to tackle the MNIST.mp4	33.29 MB
12. Deeper example/3. Importing the relevant libraries and loading the data.mp4	15.85 MB
12. Deeper example/4. Preprocess the data - create a validation dataset and scale the data.mp4	27.05 MB
12. Deeper example/5. Preprocess the data - shuffle and batch the data.mp4	36.58 MB
12. Deeper example/6. Outline the model.mp4	27.36 MB
12. Deeper example/7. Select the loss and the optimizer.mp4	12.71 MB
12. Deeper example/8. Learning.mp4	40.87 MB
12. Deeper example/9. Testing the model.mp4	26.91 MB
13. Business case/1. Exploring the dataset and identifying predictors.mp4	52.12 MB
13. Business case/2. Outlining the business case solution.mp4	9.52 MB
13. Business case/3. Balancing the dataset.mp4	44.65 MB
13. Business case/4. Preprocessing the data.mp4	77.42 MB
13. Business case/5. Load the preprocessed data.mp4	18.22 MB
13. Business case/6. Learning and interpreting the result.mp4	26.40 MB
13. Business case/7. Setting an early stopping mechanism.mp4	42.64 MB
13. Business case/8. Testing the model.mp4	9.63 MB
14. Conclusion/1. Summary.mp4	48.54 MB
14. Conclusion/2. Whats more out there.mp4	17.51 MB
14. Conclusion/3. An overview of CNNs.mp4	71.95 MB
14. Conclusion/4. An overview of RNNs.mp4	27.42 MB
14. Conclusion/5. Non-NN approaches.mp4	41.90 MB
2. Neural networks Intro/1. Introduction to neural networks.mp4	42.58 MB
2. Neural networks Intro/10. Cross-entropy loss.mp4	33.40 MB
2. Neural networks Intro/11. One-parameter gradient descent.mp4	56.41 MB
2. Neural networks Intro/12. N-parameter gradient descent.mp4	50.04 MB
2. Neural networks Intro/2. Training the model.mp4	26.82 MB
2. Neural networks Intro/3. Types of machine learning.mp4	40.85 MB
2. Neural networks Intro/4. The linear model.mp4	26.04 MB
2. Neural networks Intro/5. The linear model. Multiple inputs..mp4	23.69 MB
2. Neural networks Intro/6. The linear model. Multiple inputs and multiple outputs.mp4	42.21 MB
2. Neural networks Intro/7. Graphical representation.mp4	21.96 MB
2. Neural networks Intro/8. The objective function.mp4	17.70 MB
2. Neural networks Intro/9. L2-norm loss.mp4	21.40 MB
3. Setting up the environment/1. Setting up the environment - Do not skip, please!.mp4	6.42 MB
3. Setting up the environment/2. Why Python and why Jupyter.mp4	34.69 MB
3. Setting up the environment/3. Installing Anaconda.mp4	31.33 MB
3. Setting up the environment/4. Jupyter Dashboard - Part 1.mp4	9.24 MB
3. Setting up the environment/5. Jupyter Dashboard - Part 2.mp4	20.37 MB
3. Setting up the environment/6. Installing TensorFlow 2.0.mp4	15.74 MB
4. Minimal example/1. Outline.mp4	13.94 MB
4. Minimal example/2. Generating the data (optional).mp4	23.74 MB
4. Minimal example/3. Initializing the variables.mp4	20.43 MB
4. Minimal example/4. Training the model.mp4	46.57 MB
5. Introduction to TensorFlow 2/1. TensorFlow outline.mp4	41.21 MB
5. Introduction to TensorFlow 2/2. TensorFlow 2 intro.mp4	37.84 MB
5. Introduction to TensorFlow 2/3. A note on coding in TensorFlow.mp4	8.14 MB
5. Introduction to TensorFlow 2/4. Types of file formats in TensorFlow and data handling.mp4	13.28 MB
5. Introduction to TensorFlow 2/5. Model layout - inputs, outputs, targets, weights, biases, optimizer and loss.mp4	32.94 MB
5. Introduction to TensorFlow 2/6. Interpreting the result and extracting the weights and bias.mp4	31.38 MB
5. Introduction to TensorFlow 2/7. Customizing your model.mp4	21.62 MB
6. Deep nets overview/1. The layer.mp4	16.36 MB
6. Deep nets overview/2. What is a deep net.mp4	32.60 MB
6. Deep nets overview/3. Really understand deep nets.mp4	58.18 MB
6. Deep nets overview/4. Why do we need non-linearities.mp4	37.97 MB
6. Deep nets overview/5. Activation functions.mp4	29.18 MB
6. Deep nets overview/6. Softmax activation.mp4	24.98 MB
6. Deep nets overview/7. Backpropagation.mp4	52.73 MB
6. Deep nets overview/8. Backpropagation - intuition.mp4	24.39 MB
8. Overfitting/1. Underfitting and overfitting.mp4	34.06 MB
8. Overfitting/2. Underfitting and overfitting. A classification example.mp4	32.48 MB
8. Overfitting/3. Train vs validation.mp4	37.52 MB
8. Overfitting/4. Train vs validation vs test.mp4	31.32 MB
8. Overfitting/5. N-fold cross validation.mp4	25.57 MB
8. Overfitting/6. Early stopping - motivation and types.mp4	28.33 MB
9. Initialization/1. Initializaiton.mp4	26.17 MB
9. Initialization/2. Types of simple initializations.mp4	12.29 MB
9. Initialization/3. Xavier_s initialization.mp4	19.12 MB
Join Our Telegram Group For More Updates !!!.url	138.00 B
Uploaded by [Coursesghar.com].txt	1.10 kB
Visit coursesghar.com for more awesome tutorials.url	114.00 B

Description

365 Data Science - Deep Learning with TensorFlow 2.0 [CoursesGhar]Course description

Machine and deep learning are some of those quantitative analysis skills that differentiate the data scientist from the other members of the team. The field of machine learning is the driving force of artificial intelligence. This course will teach you how to leverage deep learning and neural networks from this powerful tool for the purposes of data science. The technology we employ is TensorFlow 2.0, which is the state-of-the-art deep learning framework.