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2023 Python for Deep Learning and Artificial Intelligence
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[TutsNode.net] - 2023 Python for Deep Learning and Artificial Intelligence (Size: 7.01 GB) (Files: 293)
[TutsNode.net] - 2023 Python for Deep Learning and Artificial Intelligence
7. Introduction to Convolutional Neural Networks [Theory and Intuitions]
14. MobileNet Architecture Explained.mp4
3. Convolutional Filters.mp4
15. EfficientNet Architecture Explained.mp4
5. Padding and Strides.mp4
11. AlexNet Architecture Explained.mp4
6. Pooling Layers.mp4
2. Working Principle of CNN.mp4
7. Activation Function.mp4
10. LeNet-5 Architecture Explained.mp4
12. GoogLeNet (Inception V1) Architecture Explained.mp4
4. Feature Maps.mp4
1. What is Convolutional Neural Network.mp4
13. RestNet Architecture Explained.mp4
9. CNN Architectures Comparison.mp4
8. Dropout.mp4
5. End to End Deep Learning Project
10. Data Visualization Part 2.mp4
5. What is Back Propagation.mp4
7. Steps to Build Neural Network.mp4
14. Neural Network Model Building.mp4
16. Model Training.mp4
2. Multi-Layer Perceptron.mp4
6. Optimizers in Deep Learning.mp4
19. Prediction on Real-Life Data.mp4
9. Data Visualization Part 1.mp4
15. Model Summary Explanation.mp4
4. Activation Function.mp4
12. Import Neural Networks APIs.mp4
11. Data Preprocessing.mp4
8. Customer Churn Dataset Loading.mp4
18. Model Save and Load.mp4
13. How to Get Input Shape and Class Weights.mp4
1. What is Neuron.mp4
17. Model Evaluation.mp4
3. Shallow vs Deep Neural Networks.mp4
1. Course Setup
1. Jupyter Notebook Introduction.mp4
1.1 python-for-deep-learning-and-ai.zip
10. Flowers Classification with Transfer Learning and CNN
17. Online Prediction of Flowers Classes.mp4
2. Load Flowers Dataset for Classification.mp4
13. Make CNN Model with VGG16 Transfer Learning.mp4
7. How to Calculate Number of Parameters in CNN.mp4
15. Train Any Model for Transfer Learning.mp4
1. Transfer Learning Introduction.mp4
5. Preparing Data with Image Data Generator.mp4
11. import VGG16 from Keras.mp4
3. Download Flowers Data.mp4
4. Flowers Data Visualization.mp4
6. Baseline CNN Model Building.mp4
8. Baseline CNN Model Training.mp4
9. Train Model with TFDS Data Without Saving Locally Part 1.mp4
16. Save and Load Model with Class Names.mp4
10. Train Model with TFDS Data Without Saving Locally Part 2.mp4
12. Data Augmentation for Training.mp4
14. Model Training for Better Accuracy.mp4
8. Horses vs Humans Classification with Simple CNN
6. Data Display in Subplots Matrix.mp4
4. Download Humans or Horses Dataset Part 2.mp4
2. Introduction to TensorFlow Datasets (TFDS).mp4
5. Use of Image Data Generator.mp4
11. CNN Parameter Calculations Part 3.mp4
8. Building CNN Model.mp4
12. Model Training.mp4
3. Download Humans or Horses Dataset Part 1.mp4
7. CNN Introduction.mp4
14. Image Class Prediction.mp4
9. CNN Parameter Calculation.mp4
1. Overview of Image Classification using CNNs.mp4
10. CNN Parameter Calculations Part 2.mp4
13. Model Load and Save.mp4
6. Introduction to Computer Vision with Deep Learning
4. Fashion MNIST Dataset Analysis.mp4
8. Discovering Overfitting - Early Stopping.mp4
7. Model Summary and Training.mp4
3. Fashion MNIST Dataset Download.mp4
9. Model Save and Load for Prediction.mp4
1. Introduction to Computer Vision with Deep Learning.mp4
6. Deep Neural Network Model Building.mp4
2. 5 Steps of Computer Vision Model Building.mp4
5. Train Test Split for Data.mp4
9. Building Cats and Dogs Classifier with Regularized CNN
21. Load Model and Do the Prediction.mp4
12. Load Dataset for Baseline Classifier.mp4
8. Other Types of Data Augmentation.mp4
5. Sample Data Load with ImageDataGenerator for Augmentation.mp4
15. How to Calculate Number of Parameters in CNN and FCN.mp4
14. How to Calculate Size of Output Layers of CNN and MaxPool.mp4
6. Random Rotation Augmentation.mp4
11. Store Data in Local Directory.mp4
4. What is Data Augmentation [Theory].mp4
7. Random Shift Augmentation.mp4
2. L1, L2 and Early Stopping Regularization.mp4
1. What is Overfitting.mp4
3. How Dropout and Batch Normalization Prevents Overfitting.mp4
19. Regularized CNN Model Building and Training.mp4
10. TensorFlow TFDS and Cats vs Dogs Data Download.mp4
13. Building Baseline CNN Classifier.mp4
16. Model Training and Layers Analysis.mp4
9. All Types of Augmentation at Once.mp4
17. Model Training and Validation Accuracy Plot.mp4
20. Training Log Analysis.mp4
18. Building Dataset for Regularized CNN.mp4
22. CNN Model Visualization.mp4
2. Python for Deep Learning
8. Matplotlib Introduction Part 2.mp4
7. Matplotlib Introduction Part 1.mp4
10. Seaborn Introduction Part 2.mp4
6. Pandas Introduction.mp4
4. Numpy Introduction Part 1.mp4
2. Python Introduction Part 2.mp4
5. Numpy Introduction Part 2.mp4
3. Python Introduction Part 3.mp4
1. Python Introduction Part 1.mp4
9. Seaborn Introduction Part 1.mp4
3. Introduction to Machine Learning
13. Code Along in Python Part 4.mp4
11. Code Along in Python Part 2.mp4
12. Code Along in Python Part 3.mp4
6. L2 Regularization.mp4
3. Support Vector Machine - SVM.mp4
2. Logistic Regression.mp4
10. Code Along in Python Part 1.mp4
1. Classical Machine Learning Introduction.mp4
8. Model Evaluation.mp4
4. Decision Tree.mp4
7. L1 Regularization.mp4
5. Random Forest.mp4
9. ROC-AUC Curve.mp4
11. Introduction to NLP
3. Overview of NLP Tools.mp4
8. Text Preprocessing.mp4
10. Pair Plot.mp4
2. What are Key NLP Techniques.mp4
12. TF-IDF Vectorization.mp4
9. Feature Engineering.mp4
5. Bag of Words - The Simples Word Embedding Technique.mp4
1. Introduction to NLP.mp4
13. Model Evaluation and Prediction on Real Data.mp4
14. Model Load and Store.mp4
6. Term Frequency - Inverse Document Frequency (TF-IDF).mp4
4. Common Challenges in NLP.mp4
7. Load Spam Dataset.mp4
11. Train Test Split.mp4
4. Introduction to Deep Learning and TensorFlow
4. Unsupervised Learning.mp4
1. Machine Learning Process Introduction.mp4
7. What is Neural Network.mp4
10. Deep Learning Tools.mp4
6. What is Deep Learning and ML.mp4
9. Application of Deep Learning.mp4
3. Supervised Learning.mp4
8. How Deep Learning Process Works.mp4
11. MLops with AWS.mp4
2. Types of Machine Learning.mp4
5. Reinforcement Learning.mp4
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