2023 Python for Deep Learning and Artificial Intelligence
Language: English | Size:6.86 GB
Genre:eLearning

Files Included :

1  Jupyter Notebook Introduction.mp4 (103.08 MB)
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1 1 python-for-deep-learning-and-ai.zip (74.67 MB)
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1  Transfer Learning Introduction.mp4 (57.43 MB)
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10  Train Model with TFDS Data Without Saving Locally Part 2.mp4 (38.45 MB)
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11  import VGG16 from Keras.mp4 (51.02 MB)
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12  Data Augmentation for Training.mp4 (25.63 MB)
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13  Make CNN Model with VGG16 Transfer Learning.mp4 (63.92 MB)
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14  Model Training for Better Accuracy.mp4 (23.35 MB)
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15  Train Any Model for Transfer Learning.mp4 (63.27 MB)
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16  Save and Load Model with Class Names.mp4 (40.38 MB)
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17  Online Prediction of Flowers Classes.mp4 (96.93 MB)
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2  Load Flowers Dataset for Classification.mp4 (67.98 MB)
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3  download скачать Flowers Data.mp4 (50 MB)
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4  Flowers Data Visualization.mp4 (48.69 MB)
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5  Preparing Data with Image Data Generator.mp4 (51.15 MB)
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6  Baseline CNN Model Building.mp4 (46.36 MB)
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7  How to Calculate Number of Parameters in CNN.mp4 (63.81 MB)
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8  Baseline CNN Model Training.mp4 (46.34 MB)
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9  Train Model with TFDS Data Without Saving Locally Part 1.mp4 (41.3 MB)
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1  Introduction to NLP.mp4 (22.55 MB)
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10  Pair Plot.mp4 (41.94 MB)
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11  Train Test Split.mp4 (8.74 MB)
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12  TF-IDF Vectorization.mp4 (34.71 MB)
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13  Model Evaluation and Prediction on Real Data.mp4 (22.26 MB)
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14  Model Load and Store.mp4 (22.07 MB)
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2  What are Key NLP Techniques.mp4 (39.58 MB)
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3  Overview of NLP Tools.mp4 (64.53 MB)
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4  Common Challenges in NLP.mp4 (19.13 MB)
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5  Bag of Words - The Simples Word Embedding Technique.mp4 (27.31 MB)
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6  Term Frequency - Inverse Document Frequency (TF-IDF).mp4 (20.02 MB)
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7  Load Spam Dataset.mp4 (18.56 MB)
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8  Text Preprocessing.mp4 (45.77 MB)
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9  Feature Engineering.mp4 (33.7 MB)
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1  Python Introduction Part 1.mp4 (33.55 MB)
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10  Seaborn Introduction Part 2.mp4 (59.72 MB)
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2  Python Introduction Part 2.mp4 (37.83 MB)
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3  Python Introduction Part 3.mp4 (34.65 MB)
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4  Numpy Introduction Part 1.mp4 (40.09 MB)
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5  Numpy Introduction Part 2.mp4 (36.3 MB)
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6  Pandas Introduction.mp4 (49.56 MB)
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7  Matplotlib Introduction Part 1.mp4 (64.76 MB)
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8  Matplotlib Introduction Part 2.mp4 (70.1 MB)
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9  Seaborn Introduction Part 1.mp4 (30.58 MB)
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1  Classical Machine Learning Introduction.mp4 (31.98 MB)
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10  Code Along in Python Part 1.mp4 (34.36 MB)
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11  Code Along in Python Part 2.mp4 (59.15 MB)
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12  Code Along in Python Part 3.mp4 (44.1 MB)
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13  Code Along in Python Part 4.mp4 (66.67 MB)
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2  Logistic Regression.mp4 (34.43 MB)
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3  Support Vector Machine - SVM.mp4 (37.66 MB)
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4  Decision Tree.mp4 (25.51 MB)
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5  Random Forest.mp4 (17.48 MB)
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6  L2 Regularization.mp4 (38.3 MB)
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7  L1 Regularization.mp4 (18.74 MB)
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8  Model Evaluation.mp4 (31.51 MB)
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9  ROC-AUC Curve.mp4 (13.46 MB)
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1  Machine Learning Process Introduction.mp4 (37.98 MB)
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10  Deep Learning Tools.mp4 (31.78 MB)
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11  MLops with AWS.mp4 (21.64 MB)
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2  Types of Machine Learning.mp4 (19.29 MB)
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3  Supervised Learning.mp4 (25.54 MB)
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4  Unsupervised Learning.mp4 (43.24 MB)
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5  Reinforcement Learning.mp4 (16.27 MB)
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6  What is Deep Learning and ML.mp4 (30.21 MB)
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7  What is Neural Network.mp4 (33.22 MB)
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8  How Deep Learning Process Works.mp4 (23.95 MB)
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9  Application of Deep Learning.mp4 (28.61 MB)
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1  What is Neuron.mp4 (20.85 MB)
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10  Data Visualization Part 2.mp4 (107.25 MB)
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11  Data Preprocessing.mp4 (36.38 MB)
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12  Import Neural Networks APIs.mp4 (37.05 MB)
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13  How to Get Input Shape and Class Weights.mp4 (21.19 MB)
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14  Neural Network Model Building.mp4 (60.87 MB)
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15  Model Summary Explanation.mp4 (48.8 MB)
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16  Model Training.mp4 (56.27 MB)
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17  Model Evaluation.mp4 (16.11 MB)
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18  Model Save and Load.mp4 (23.62 MB)
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19  Prediction on Real-Life Data.mp4 (50.94 MB)
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2  Multi-Layer Perceptron.mp4 (55.11 MB)
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3  Shallow vs Deep Neural Networks.mp4 (13.85 MB)
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4  Activation Function.mp4 (40.33 MB)
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5  What is Back Propagation.mp4 (79.4 MB)
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6  Optimizers in Deep Learning.mp4 (52.07 MB)
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7  Steps to Build Neural Network.mp4 (64.08 MB)
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8  Customer Churn Dataset Loading.mp4 (26 MB)
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9  Data Visualization Part 1.mp4 (50.22 MB)
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1  Introduction to Computer Vision with Deep Learning.mp4 (42.97 MB)
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2  5 Steps of Computer Vision Model Building.mp4 (27.68 MB)
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3  Fashion MNIST Dataset download скачать.mp4 (63.2 MB)
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4  Fashion MNIST Dataset Analysis.mp4 (87.76 MB)
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5  Train Test Split for Data.mp4 (25.82 MB)
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6  Deep Neural Network Model Building.mp4 (36.53 MB)
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7  Model Summary and Training.mp4 (64.97 MB)
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8  Discovering Overfitting - Early Stopping.mp4 (77.54 MB)
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9  Model Save and Load for Prediction.mp4 (44.68 MB)
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1  What is Convolutional Neural Network.mp4 (64.2 MB)
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10  LeNet-5 Architecture Explained.mp4 (71.12 MB)
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11  AlexNet Architecture Explained.mp4 (98.72 MB)
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12  GoogLeNet (Inception V1) Architecture Explained.mp4 (68.39 MB)
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13  RestNet Architecture Explained.mp4 (56.84 MB)
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14  MobileNet Architecture Explained.mp4 (121.33 MB)
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15  EfficientNet Architecture Explained.mp4 (104.3 MB)
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2  Working Principle of CNN.mp4 (80.21 MB)
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3  Convolutional Filters.mp4 (114.04 MB)
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4  Feature Maps.mp4 (66.88 MB)
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5  Padding and Strides.mp4 (102.33 MB)
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6  Pooling Layers.mp4 (86.51 MB)
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7  Activation Function.mp4 (72.66 MB)
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8  Dropout.mp4 (32.36 MB)
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9  CNN Architectures Comparison.mp4 (55.56 MB)
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1  Overview of Image Classification using CNNs.mp4 (44.06 MB)
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10  CNN Parameter Calculations Part 2.mp4 (43.15 MB)
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11  CNN Parameter Calculations Part 3.mp4 (61.42 MB)
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12  Model Training.mp4 (58.94 MB)
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13  Model Load and Save.mp4 (32.08 MB)
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14  Image Class Prediction.mp4 (52.31 MB)
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2  Introduction to TensorFlow Datasets (TFDS).mp4 (74.65 MB)
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3  download скачать Humans or Horses Dataset Part 1.mp4 (56.15 MB)
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4  download скачать Humans or Horses Dataset Part 2.mp4 (76.02 MB)
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5  Use of Image Data Generator.mp4 (73.36 MB)
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6  Data Display in Subplots Matrix.mp4 (89.22 MB)
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7  CNN Introduction.mp4 (53.03 MB)
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8  Building CNN Model.mp4 (60.68 MB)
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9  CNN Parameter Calculation.mp4 (44.78 MB)
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1  What is Overfitting.mp4 (42.76 MB)
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10  TensorFlow TFDS and Cats vs Dogs Data download скачать.mp4 (42.39 MB)
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11  Store Data in Local Directory.mp4 (53.07 MB)
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12  Load Dataset for Baseline Classifier.mp4 (82.94 MB)
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13  Building Baseline CNN Classifier.mp4 (41.62 MB)
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14  How to Calculate Size of Output Layers of CNN and MaxPool.mp4 (61.32 MB)
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15  How to Calculate Number of Parameters in CNN and FCN.mp4 (68.68 MB)
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16  Model Training and Layers Analysis.mp4 (39.91 MB)
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17  Model Training and Validation Accuracy Plot.mp4 (25.97 MB)
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18  Building Dataset for Regularized CNN.mp4 (17.5 MB)
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19  Regularized CNN Model Building and Training.mp4 (42.45 MB)
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2  L1, L2 and Early Stopping Regularization.mp4 (44.8 MB)
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20  Training Log Analysis.mp4 (25.54 MB)
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21  Load Model and Do the Prediction.mp4 (83.4 MB)
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22  CNN Model Visualization.mp4 (14.31 MB)
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3  How Dropout and Batch Normalization Prevents Overfitting.mp4 (42.73 MB)
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4  What is Data Augmentation [Theory].mp4 (48.57 MB)
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5  Sample Data Load with ImageDataGenerator for Augmentation.mp4 (71.05 MB)
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6  Random Rotation Augmentation.mp4 (55.82 MB)
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7  Random Shift Augmentation.mp4 (45.94 MB)
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8  Other Types of Data Augmentation.mp4 (73.34 MB)
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9  All Types of Augmentation at Once.mp4 (32.66 MB)
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