2023 Python for Deep Learning and Artificial Intelligence | 4BCB56EE8F0BA19DA5774EF00191B47718B18A4B

2023 Python for Deep Learning and Artificial Intelligence

Name	2023 Python for Deep Learning and Artificial Intelligence	DOWNLOAD Copy Link Trouble downloading? see How To
Total Size	7.5 GB
Total Files	293
Last Seen	2025-11-06 00:15
Hash	4BCB56EE8F0BA19DA5774EF00191B47718B18A4B

/.../7. Introduction to Convolutional Neural Networks [Theory and Intuitions]/
14. MobileNet Architecture Explained.mp4	127.2 MB
3. Convolutional Filters.mp4	119.6 MB
15. EfficientNet Architecture Explained.mp4	109.4 MB
5. Padding and Strides.mp4	107.3 MB
11. AlexNet Architecture Explained.mp4	103.5 MB
6. Pooling Layers.mp4	90.7 MB
2. Working Principle of CNN.mp4	84.1 MB
7. Activation Function.mp4	76.2 MB
10. LeNet-5 Architecture Explained.mp4	74.6 MB
12. GoogLeNet (Inception V1) Architecture Explained.mp4	71.7 MB
4. Feature Maps.mp4	70.1 MB
1. What is Convolutional Neural Network.mp4	67.3 MB
13. RestNet Architecture Explained.mp4	59.6 MB
9. CNN Architectures Comparison.mp4	58.3 MB
8. Dropout.mp4	33.9 MB
/
TutsNode.net.txt	0.1 KB
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/.../5. End to End Deep Learning Project/
10. Data Visualization Part 2.mp4	112.5 MB
5. What is Back Propagation.mp4	83.3 MB
7. Steps to Build Neural Network.mp4	67.2 MB
14. Neural Network Model Building.mp4	63.8 MB
16. Model Training.mp4	59.0 MB
2. Multi-Layer Perceptron.mp4	57.8 MB
6. Optimizers in Deep Learning.mp4	54.6 MB
19. Prediction on Real-Life Data.mp4	53.4 MB
9. Data Visualization Part 1.mp4	52.7 MB
15. Model Summary Explanation.mp4	51.2 MB
4. Activation Function.mp4	42.3 MB
12. Import Neural Networks APIs.mp4	38.9 MB
11. Data Preprocessing.mp4	38.1 MB
8. Customer Churn Dataset Loading.mp4	27.3 MB
18. Model Save and Load.mp4	24.8 MB
13. How to Get Input Shape and Class Weights.mp4	22.2 MB
1. What is Neuron.mp4	21.9 MB
17. Model Evaluation.mp4	16.9 MB
3. Shallow vs Deep Neural Networks.mp4	14.5 MB
/1. Course Setup/
1. Jupyter Notebook Introduction.mp4	108.1 MB
1.1 python-for-deep-learning-and-ai.zip	78.3 MB
/.../10. Flowers Classification with Transfer Learning and CNN/
17. Online Prediction of Flowers Classes.mp4	101.6 MB
2. Load Flowers Dataset for Classification.mp4	71.3 MB
13. Make CNN Model with VGG16 Transfer Learning.mp4	67.0 MB
7. How to Calculate Number of Parameters in CNN.mp4	66.9 MB
15. Train Any Model for Transfer Learning.mp4	66.3 MB
1. Transfer Learning Introduction.mp4	60.2 MB
5. Preparing Data with Image Data Generator.mp4	53.6 MB
11. import VGG16 from Keras.mp4	53.5 MB
3. Download Flowers Data.mp4	52.4 MB
4. Flowers Data Visualization.mp4	51.1 MB
6. Baseline CNN Model Building.mp4	48.6 MB
8. Baseline CNN Model Training.mp4	48.6 MB
9. Train Model with TFDS Data Without Saving Locally Part 1.mp4	43.3 MB
16. Save and Load Model with Class Names.mp4	42.3 MB
10. Train Model with TFDS Data Without Saving Locally Part 2.mp4	40.3 MB
12. Data Augmentation for Training.mp4	26.9 MB
14. Model Training for Better Accuracy.mp4	24.5 MB
/.../8. Horses vs Humans Classification with Simple CNN/
6. Data Display in Subplots Matrix.mp4	93.6 MB
4. Download Humans or Horses Dataset Part 2.mp4	79.7 MB
2. Introduction to TensorFlow Datasets (TFDS).mp4	78.3 MB
5. Use of Image Data Generator.mp4	76.9 MB
11. CNN Parameter Calculations Part 3.mp4	64.4 MB
8. Building CNN Model.mp4	63.6 MB
12. Model Training.mp4	61.8 MB
3. Download Humans or Horses Dataset Part 1.mp4	58.9 MB
7. CNN Introduction.mp4	55.6 MB
14. Image Class Prediction.mp4	54.9 MB
9. CNN Parameter Calculation.mp4	47.0 MB
1. Overview of Image Classification using CNNs.mp4	46.2 MB
10. CNN Parameter Calculations Part 2.mp4	45.2 MB
13. Model Load and Save.mp4	33.6 MB
/.../6. Introduction to Computer Vision with Deep Learning/
4. Fashion MNIST Dataset Analysis.mp4	92.0 MB
8. Discovering Overfitting - Early Stopping.mp4	81.3 MB
7. Model Summary and Training.mp4	68.1 MB
3. Fashion MNIST Dataset Download.mp4	66.3 MB
9. Model Save and Load for Prediction.mp4	46.9 MB
1. Introduction to Computer Vision with Deep Learning.mp4	45.1 MB
6. Deep Neural Network Model Building.mp4	38.3 MB
2. 5 Steps of Computer Vision Model Building.mp4	29.0 MB
5. Train Test Split for Data.mp4	27.1 MB
/.../9. Building Cats and Dogs Classifier with Regularized CNN/
21. Load Model and Do the Prediction.mp4	87.5 MB
12. Load Dataset for Baseline Classifier.mp4	87.0 MB
8. Other Types of Data Augmentation.mp4	76.9 MB
5. Sample Data Load with ImageDataGenerator for Augmentation.mp4	74.5 MB
15. How to Calculate Number of Parameters in CNN and FCN.mp4	72.0 MB
14. How to Calculate Size of Output Layers of CNN and MaxPool.mp4	64.3 MB
6. Random Rotation Augmentation.mp4	58.5 MB
11. Store Data in Local Directory.mp4	55.6 MB
4. What is Data Augmentation [Theory].mp4	50.9 MB
7. Random Shift Augmentation.mp4	48.2 MB
2. L1, L2 and Early Stopping Regularization.mp4	47.0 MB
1. What is Overfitting.mp4	44.8 MB
3. How Dropout and Batch Normalization Prevents Overfitting.mp4	44.8 MB
19. Regularized CNN Model Building and Training.mp4	44.5 MB
10. TensorFlow TFDS and Cats vs Dogs Data Download.mp4	44.4 MB
13. Building Baseline CNN Classifier.mp4	43.6 MB
16. Model Training and Layers Analysis.mp4	41.8 MB
9. All Types of Augmentation at Once.mp4	34.2 MB
17. Model Training and Validation Accuracy Plot.mp4	27.2 MB
20. Training Log Analysis.mp4	26.8 MB
18. Building Dataset for Regularized CNN.mp4	18.4 MB
22. CNN Model Visualization.mp4	15.0 MB
/.../2. Python for Deep Learning/
8. Matplotlib Introduction Part 2.mp4	73.5 MB
7. Matplotlib Introduction Part 1.mp4	67.9 MB
10. Seaborn Introduction Part 2.mp4	62.6 MB
6. Pandas Introduction.mp4	52.0 MB
4. Numpy Introduction Part 1.mp4	42.0 MB
2. Python Introduction Part 2.mp4	39.7 MB
5. Numpy Introduction Part 2.mp4	38.1 MB
3. Python Introduction Part 3.mp4	36.3 MB
1. Python Introduction Part 1.mp4	35.2 MB
9. Seaborn Introduction Part 1.mp4	32.1 MB
/.../3. Introduction to Machine Learning/
13. Code Along in Python Part 4.mp4	69.9 MB
11. Code Along in Python Part 2.mp4	62.0 MB
12. Code Along in Python Part 3.mp4	46.2 MB
6. L2 Regularization.mp4	40.2 MB
3. Support Vector Machine - SVM.mp4	39.5 MB
2. Logistic Regression.mp4	36.1 MB
10. Code Along in Python Part 1.mp4	36.0 MB
1. Classical Machine Learning Introduction.mp4	33.5 MB
8. Model Evaluation.mp4	33.0 MB
4. Decision Tree.mp4	26.7 MB
7. L1 Regularization.mp4	19.7 MB
5. Random Forest.mp4	18.3 MB
9. ROC-AUC Curve.mp4	14.1 MB
/.../11. Introduction to NLP/
3. Overview of NLP Tools.mp4	67.7 MB
8. Text Preprocessing.mp4	48.0 MB
10. Pair Plot.mp4	44.0 MB
2. What are Key NLP Techniques.mp4	41.5 MB
12. TF-IDF Vectorization.mp4	36.4 MB
9. Feature Engineering.mp4	35.3 MB
5. Bag of Words - The Simples Word Embedding Technique.mp4	28.6 MB
1. Introduction to NLP.mp4	23.6 MB
13. Model Evaluation and Prediction on Real Data.mp4	23.3 MB
14. Model Load and Store.mp4	23.1 MB
6. Term Frequency - Inverse Document Frequency (TF-IDF).mp4	21.0 MB
4. Common Challenges in NLP.mp4	20.1 MB
7. Load Spam Dataset.mp4	19.5 MB
11. Train Test Split.mp4	9.2 MB
/.../4. Introduction to Deep Learning and TensorFlow/
4. Unsupervised Learning.mp4	45.3 MB
1. Machine Learning Process Introduction.mp4	39.8 MB
7. What is Neural Network.mp4	34.8 MB
10. Deep Learning Tools.mp4	33.3 MB
6. What is Deep Learning and ML.mp4	31.7 MB
9. Application of Deep Learning.mp4	30.0 MB
3. Supervised Learning.mp4	26.8 MB
8. How Deep Learning Process Works.mp4	25.1 MB
11. MLops with AWS.mp4	22.7 MB
2. Types of Machine Learning.mp4	20.2 MB
5. Reinforcement Learning.mp4	17.1 MB
Total files 293