12-27-2025, 10:52 PM
![[Image: 539499712_359020115_tuto.jpg]](https://img100.pixhost.to/images/617/539499712_359020115_tuto.jpg)
4.65 GB | 22min 58s | mp4 | 1920X1080 | 16:9
Genre:eLearning |Language:English
Files Included :
FileName :002 Introduction to Deep Learning From Historical Context to Modern Applications.mp4 | Size: (34.64 MB)
FileName :002 How Neural Networks Learn Gradient Descent and Backpropagation Explained.mp4 | Size: (8.1 MB)
FileName :003 Understanding Neurons The Building Blocks of Artificial Neural Networks.mp4 | Size: (56.61 MB)
FileName :004 Understanding Activation Functions in Neural Networks Sigmoid, ReLU, and More.mp4 | Size: (31.51 MB)
FileName :005 How Do Neural Networks Work Step-by-Step Guide to Property Valuation Example.mp4 | Size: (29.68 MB)
FileName :006 How Do Neural Networks Learn Understanding Backpropagation and Cost Functions.mp4 | Size: (49.23 MB)
FileName :007 Mastering Gradient Descent Key to Efficient Neural Network Training.mp4 | Size: (34.24 MB)
FileName :008 How to Use Stochastic Gradient Descent for Deep Learning Optimization.mp4 | Size: (33.18 MB)
FileName :009 Understanding Backpropagation Algorithm Key to Optimizing Deep Learning Models.mp4 | Size: (20.35 MB)
FileName :002 Step 1 - Data Preprocessing for Deep Learning Preparing Neural Network Dataset.mp4 | Size: (36.33 MB)
FileName :004 Step 2 - Data Preprocessing for Neural Networks Essential Steps and Techniques.mp4 | Size: (69.12 MB)
FileName :005 Step 3 - Constructing an Artificial Neural Network Adding Input --& Hidden Layers.mp4 | Size: (54.85 MB)
FileName :006 Step 4 - Compile and Train Neural Network Optimizers, Loss Functions --& Metrics.mp4 | Size: (45.41 MB)
FileName :007 Step 5 - How to Make Predictions and Evaluate Neural Network Model in Python.mp4 | Size: (61.64 MB)
FileName :002 Understanding CNN Architecture From Convolution to Fully Connected Layers.mp4 | Size: (10.68 MB)
FileName :003 How Do Convolutional Neural Networks Work Understanding CNN Architecture.mp4 | Size: (54.97 MB)
FileName :004 How to Apply Convolution Filters in Neural Networks Feature Detection Explained.mp4 | Size: (44.4 MB)
FileName :005 Rectified Linear Units --(ReLU--) in Deep Learning Optimizing CNN Performance.mp4 | Size: (25.18 MB)
FileName :006 Understanding Spatial Invariance in CNNs Max Pooling Explained for Beginners.mp4 | Size: (55.67 MB)
FileName :007 How to Flatten Pooled Feature Maps in Convolutional Neural Networks --(CNNs--).mp4 | Size: (6.14 MB)
FileName :008 How Do Fully Connected Layers Work in Convolutional Neural Networks --(CNNs--).mp4 | Size: (52.78 MB)
FileName :009 CNN Building Blocks Feature Maps, ReLU, Pooling, and Fully Connected Layers.mp4 | Size: (16.4 MB)
FileName :010 Understanding Softmax Activation and Cross-Entropy Loss in Deep Learning.mp4 | Size: (67.3 MB)
FileName :002 Step 1 - Convolutional Neural Networks Explained Image Classification Tutorial.mp4 | Size: (27.8 MB)
FileName :003 Step 2 - Deep Learning Preprocessing Scaling --& Transforming Images for CNNs.mp4 | Size: (67.42 MB)
FileName :004 Step 3 - Building CNN Architecture Convolutional Layers --& Max Pooling Explained.mp4 | Size: (68.02 MB)
FileName :005 Step 4 - Train CNN for Image Classification Optimize with Keras --& TensorFlow.mp4 | Size: (27.99 MB)
FileName :006 Step 5 - Deploying a CNN for Real-World Image Recognition.mp4 | Size: (56.64 MB)
FileName :007 Develop an Image Recognition System Using Convolutional Neural Networks.mp4 | Size: (87.16 MB)
FileName :002 How Do Recurrent Neural Networks --(RNNs--) Work Deep Learning Explained.mp4 | Size: (6.88 MB)
FileName :003 What is a Recurrent Neural Network --(RNN--) Deep Learning for Sequential Data.mp4 | Size: (43.59 MB)
FileName :004 Understanding the Vanishing Gradient Problem in Recurrent Neural Networks --(RNNs--).mp4 | Size: (54.89 MB)
FileName :005 Understanding Long Short-Term Memory --(LSTM--) Architecture for Deep Learning.mp4 | Size: (75.06 MB)
FileName :006 How LSTMs Work in Practice Visualizing Neural Network Predictions.mp4 | Size: (64.06 MB)
FileName :007 LSTM Variations Peepholes, Combined Gates, and GRUs in Deep Learning.mp4 | Size: (13.76 MB)
FileName :002 Step 1 - Building a Robust LSTM Neural Network for Stock Price Trend Prediction.mp4 | Size: (24.65 MB)
FileName :003 Step 2 - Importing Training Data for LSTM Stock Price Prediction Model.mp4 | Size: (26.81 MB)
FileName :004 Step 3 - Applying Min-Max Normalization for Time Series Data in Neural Networks.mp4 | Size: (22.62 MB)
FileName :005 Step 4 - Building X train and y train Arrays for LSTM Time Series Forecasting.mp4 | Size: (57.77 MB)
FileName :006 Step 5 - Preparing Time Series Data for LSTM Neural Network in Stock Forecasting.mp4 | Size: (41.4 MB)
FileName :007 Step 6 - Create RNN Architecture Sequential Layers vs Computational Graphs.mp4 | Size: (10.81 MB)
FileName :008 Step 7 - Adding First LSTM Layer Key Components for Stock Market Prediction.mp4 | Size: (33.05 MB)
FileName :009 Step 8 - Implementing Dropout Regularization in LSTM Networks for Forecasting.mp4 | Size: (20.26 MB)
FileName :010 Step 9 - Finalizing RNN Architecture Dense Layer for Stock Price Forecasting.mp4 | Size: (12.68 MB)
FileName :011 Step 10 - Compile RNN with Adam Optimizer for Stock Price Prediction in Python.mp4 | Size: (16.56 MB)
FileName :012 Step 11 - Optimizing Epochs and Batch Size for LSTM Stock Price Forecasting.mp4 | Size: (41.48 MB)
FileName :013 Step 12 - Visualizing LSTM Predictions Real vs Forecasted Google Stock Prices.mp4 | Size: (21.26 MB)
FileName :014 Step 13 - Preparing Historical Stock Data for LSTM Model Scaling and Reshaping.mp4 | Size: (64.1 MB)
FileName :015 Step 14 - Creating 3D Input Structure for LSTM Stock Price Prediction in Python.mp4 | Size: (31.64 MB)
FileName :016 Step 15 - Visualizing LSTM Predictions Plotting Real vs Predicted Stock Prices.mp4 | Size: (34.33 MB)
FileName :001 How Do Self-Organizing Maps Work Understanding SOM in Deep Learning.mp4 | Size: (9.5 MB)
FileName :002 Self-Organizing Maps --(SOM--) Unsupervised Deep Learning for Dimensionality Reduct.mp4 | Size: (32.54 MB)
FileName :003 Why K-Means Clustering is Essential for Understanding Self-Organizing Maps.mp4 | Size: (7.14 MB)
FileName :004 Self-Organizing Maps Tutorial Dimensionality Reduction in Machine Learning.mp4 | Size: (53.79 MB)
FileName :005 How Self-Organizing Maps --(SOMs--) Learn Unsupervised Deep Learning Explained.mp4 | Size: (38.91 MB)
FileName :006 How to Create a Self-Organizing Map --(SOM--) in DL Step-by-Step Tutorial.mp4 | Size: (25.35 MB)
FileName :007 Interpreting SOM Clusters Unsupervised Learning Techniques for Data Analysis.mp4 | Size: (16.99 MB)
FileName :008 Understanding K-Means Clustering Intuitive Explanation with Visual Examples.mp4 | Size: (54.69 MB)
FileName :009 K-Means Clustering Avoiding the Random Initialization Trap in Machine Learning.mp4 | Size: (29.66 MB)
FileName :010 How to Find the Optimal Number of Clusters in K-Means WCSS and Elbow Method.mp4 | Size: (43.17 MB)
FileName :002 Step 1 - Implementing Self-Organizing Maps --(SOMs--) for Fraud Detection in Python.mp4 | Size: (52 MB)
FileName :003 Step 2 - SOM Weight Initialization and Training Tutorial for Anomaly Detection.mp4 | Size: (36.64 MB)
FileName :004 Step 3 - SOM Visualization Techniques Colorbar --& Markers for Outlier Detection.mp4 | Size: (64.29 MB)
FileName :005 Step 4 - Catching Cheaters with SOMs Mapping Winning Nodes to Customer Data.mp4 | Size: (44.87 MB)
FileName :002 Step 1 - Building a Hybrid Deep Learning Model for Credit Card Fraud Detection.mp4 | Size: (10.74 MB)
FileName :003 Step 2 - Developing a Fraud Detection System Using Self-Organizing Maps.mp4 | Size: (17.78 MB)
FileName :004 Step 3 - Building a Hybrid Model From Unsupervised to Supervised Deep Learning.mp4 | Size: (55.65 MB)
FileName :005 Step 4 - Implementing Fraud Detection with SOM A Deep Learning Approach.mp4 | Size: (35.35 MB)
FileName :001 Understanding Boltzmann Machines Deep Learning Fundamentals for AI Enthusiasts.mp4 | Size: (6.5 MB)
FileName :002 Boltzmann Machines vs Neural Networks Key Differences in Deep Learning.mp4 | Size: (54.47 MB)
FileName :003 Deep Learning Fundamentals Energy-Based Models --& Their Role in Neural Networks.mp4 | Size: (40.46 MB)
FileName :004 How to Edit Wikipedia Adding Boltzmann Distribution in Deep Learning.mp4 | Size: (13.32 MB)
FileName :005 How Restricted Boltzmann Machines Work Deep Learning for Recommender Systems.mp4 | Size: (47.56 MB)
FileName :006 How Energy-Based Models Work Deep Dive into Contrastive Divergence Algorithm.mp4 | Size: (59.23 MB)
FileName :007 Deep Belief Networks Understanding RBM Stacking in Deep Learning Models.mp4 | Size: (20.47 MB)
FileName :008 Deep Boltzmann Machines vs Deep Belief Networks Key Differences Explained.mp4 | Size: (11.2 MB)
FileName :002 Step 0 - Building a Movie Recommender System with RBMs Data Preprocessing Guide.mp4 | Size: (34.79 MB)
FileName :004 Step 1 - Importing Movie Datasets for RBM-Based Recommender Systems in Python.mp4 | Size: (35.1 MB)
FileName :005 Step 2 - Preparing Training and Test Sets for Restricted Boltzmann Machine.mp4 | Size: (36.67 MB)
FileName :006 Step 3 - Preparing Data for RBM Calculating Total Users and Movies in Python.mp4 | Size: (31.89 MB)
FileName :007 Step 4 - Convert Training --& Test Sets to RBM-Ready Arrays in Python.mp4 | Size: (79.37 MB)
FileName :008 Step 5 - Converting NumPy Arrays to PyTorch Tensors for Deep Learning Models.mp4 | Size: (19.37 MB)
FileName :009 Step 6 - RBM Data Preprocessing Transforming Movie Ratings for Neural Networks.mp4 | Size: (29.11 MB)
FileName :010 Step 7 - Implementing Restricted Boltzmann Machine Class Structure in PyTorch.mp4 | Size: (38.87 MB)
FileName :011 Step 8 - RBM Hidden Layer Sampling Bernoulli Distribution in PyTorch Tutorial.mp4 | Size: (48.35 MB)
FileName :012 Step 9 - RBM Visible Node Sampling Bernoulli Distribution in Deep Learning.mp4 | Size: (23.88 MB)
FileName :013 Step 10 - RBM Training Function Updating Weights and Biases with Gibbs Sampling.mp4 | Size: (44.32 MB)
FileName :014 Step 11 - How to Set Up an RBM Model Choosing NV, NH, and Batch Size Parameters.mp4 | Size: (27.04 MB)
FileName :015 Step 12 - RBM Training Loop Epoch Setup and Loss Function Implementation.mp4 | Size: (51.02 MB)
FileName :016 Step 13 - RBM Training Updating Weights and Biases with Contrastive Divergence.mp4 | Size: (73.76 MB)
FileName :017 Step 14 - Optimizing RBM Models From Training to Test Set Performance Analysis.mp4 | Size: (65.13 MB)
FileName :001 Deep Learning Autoencoders Types, Architecture, and Training Explained.mp4 | Size: (8.34 MB)
FileName :002 Autoencoders in Machine Learning Applications and Architecture Overview.mp4 | Size: (25.64 MB)
FileName :003 Autoencoder Bias in Deep Learning Improving Neural Network Performance.mp4 | Size: (4.8 MB)
FileName :004 How to Train an Autoencoder Step-by-Step Guide for Deep Learning Beginners.mp4 | Size: (23.48 MB)
FileName :005 How to Use Overcomplete Hidden Layers in Autoencoders for Feature Extraction.mp4 | Size: (14.77 MB)
FileName :006 Sparse Autoencoders in Deep Learning Preventing Overfitting in Neural Networks.mp4 | Size: (23.69 MB)
FileName :007 Denoising Autoencoders Deep Learning Regularization Technique Explained.mp4 | Size: (9.65 MB)
FileName :008 What are Contractive Autoencoders Deep Learning Regularization Techniques.mp4 | Size: (9.08 MB)
FileName :009 What are Stacked Autoencoders in Deep Learning Architecture and Applications.mp4 | Size: (7.21 MB)
FileName :010 Deep Autoencoders vs Stacked Autoencoders Key Differences in Neural Networks.mp4 | Size: (7.07 MB)
FileName :003 Step 1 - Building a Movie Recommendation System with AutoEncoders Data Import.mp4 | Size: (41.61 MB)
FileName :004 Step 2 - Preparing Training and Test Sets for Autoencoder Recommendation System.mp4 | Size: (40.63 MB)
FileName :005 Step 3 - Preparing Data for Recommendation Systems User --& Movie Count in Python.mp4 | Size: (28.89 MB)
FileName :007 Step 4 - Prepare Data for Autoencoder Creating User-Movie Rating Matrices.mp4 | Size: (72.17 MB)
FileName :008 Step 5 - Convert Training and Test Sets to PyTorch Tensors for Deep Learning.mp4 | Size: (17.56 MB)
FileName :009 Step 6 - Building Autoencoder Architecture Class Creation for Neural Networks.mp4 | Size: (58.44 MB)
FileName :010 Step 7 - Python Autoencoder Tutorial Implementing Activation Functions --& Layers.mp4 | Size: (47.82 MB)
FileName :011 Step 8 - PyTorch Techniques for Efficient Autoencoder Training on Large Datasets.mp4 | Size: (51.87 MB)
FileName :012 Step 9 - Implementing Stochastic Gradient Descent in Autoencoder Architecture.mp4 | Size: (46.64 MB)
FileName :013 Step 10 - Machine Learning Metrics Interpreting Loss in Autoencoder Training.mp4 | Size: (15.25 MB)
FileName :014 Step 11 - How to Evaluate Recommender System Performance Using Test Set Loss.mp4 | Size: (40.13 MB)
FileName :015 THANK YOU Video.mp4 | Size: (9.18 MB)
FileName :002 Simple Linear Regression Understanding Y = B0 + B1X in Machine Learning.mp4 | Size: (16.29 MB)
FileName :003 Linear Regression Explained Finding the Best Fitting Line for Data Analysis.mp4 | Size: (10.86 MB)
FileName :004 Multiple Linear Regression - Understanding Dependent --& Independent Variables.mp4 | Size: (3.33 MB)
FileName :005 Understanding Logistic Regression Intuition and Probability in Classification.mp4 | Size: (58.02 MB)
FileName :002 How to Scale Features in Machine Learning Normalization vs Standardization.mp4 | Size: (5.29 MB)
FileName :003 Machine Learning Basics Using Train-Test Split to Evaluate Model Performance.mp4 | Size: (7.02 MB)
FileName :004 Machine Learning Workflow Data Splitting, Feature Scaling, and Model Training.mp4 | Size: (16.54 MB)
FileName :001 Step 1 - Data Preprocessing in Python Essential Tools for ML Models.mp4 | Size: (18.4 MB)
FileName :002 Step 2 - How to Handle Missing Data in Python Data Preprocessing Techniques.mp4 | Size: (18.44 MB)
FileName :003 Step 1 - Importing Essential Python Libraries for Data Preprocessing --& Analysis.mp4 | Size: (12.25 MB)
FileName :004 Step 1 - Creating a DataFrame from CSV Python Data Preprocessing Basics.mp4 | Size: (17.97 MB)
FileName :005 Step 2 - Pandas DataFrame Indexing Building Feature Matrix X with iloc Method.mp4 | Size: (16.19 MB)
FileName :006 Step 3 - Preprocessing Data Extracting Features and Target Variables in Python.mp4 | Size: (19.83 MB)
FileName :008 Step 1 - Handling Missing Data in Python SimpleImputer for Data Preprocessing.mp4 | Size: (20.39 MB)
FileName :009 Step 2 - Preprocessing Datasets Fit and Transform to Handle Missing Values.mp4 | Size: (20.54 MB)
FileName :010 Step 1 - Preprocessing Categorical Variables One-Hot Encoding in Python.mp4 | Size: (15.17 MB)
FileName :011 Step 2 - Using fit transform Method for Efficient Data Preprocessing in Python.mp4 | Size: (20.27 MB)
FileName :012 Step 3 - Preprocessing Categorical Data One-Hot and Label Encoding Techniques.mp4 | Size: (16.03 MB)
FileName :013 Step 1 - Machine Learning Data Prep Splitting Dataset Before Feature Scaling.mp4 | Size: (13.46 MB)
FileName :014 Step 2 - Split Data into Train --& Test Sets with Scikit-learn--'s train test split.mp4 | Size: (20.58 MB)
FileName :015 Step 3 - Preparing Data for ML Splitting Datasets with Python and Scikit-learn.mp4 | Size: (13.33 MB)
FileName :016 Step 1 - How to Apply Feature Scaling for Preprocessing Machine Learning Data.mp4 | Size: (20.45 MB)
FileName :017 Step 2 - Feature Scaling in Machine Learning When to Apply StandardScaler.mp4 | Size: (16.34 MB)
FileName :018 Step 3 - Normalizing Data with Fit and Transform Methods in Scikit-learn.mp4 | Size: (13.1 MB)
FileName :019 Step 4 - How to Apply Feature Scaling to Training --& Test Sets in ML.mp4 | Size: (20.17 MB)
FileName :001 Understanding the Logistic Regression Equation A Step-by-Step Guide.mp4 | Size: (16.91 MB)
FileName :002 How to Calculate Maximum Likelihood in Logistic Regression Step-by-Step Guide.mp4 | Size: (9.66 MB)
FileName :003 Step 1a - Machine Learning Classification Logistic Regression in Python.mp4 | Size: (19.65 MB)
FileName :004 Step 1b - Logistic Regression Analysis Importing Libraries and Splitting Data.mp4 | Size: (13.71 MB)
FileName :005 Step 2a - Data Preprocessing for Logistic Regression Importing and Splitting.mp4 | Size: (20.11 MB)
FileName :006 Step 2b - Data Preprocessing Feature Scaling for Machine Learning in Python.mp4 | Size: (20.46 MB)
FileName :007 Step 3a - Implementing Logistic Regression for Classification with Scikit-Learn.mp4 | Size: (13.67 MB)
FileName :008 Step 3b - Predicting Purchase Decisions with Logistic Regression in Python.mp4 | Size: (12.03 MB)
FileName :009 Step 4a - Using Classifier Objects to Make Predictions in Machine Learning.mp4 | Size: (20.56 MB)
FileName :010 Step 4b - Evaluating Logistic Regression Model Predicted vs Real Outcomes.mp4 | Size: (6.27 MB)
FileName :011 Step 5 - Evaluating Machine Learning Models Confusion Matrix and Accuracy.mp4 | Size: (20.42 MB)
FileName :012 Step 6a - Creating a Confusion Matrix for Machine Learning Model Evaluation.mp4 | Size: (20.2 MB)
FileName :013 Step 6b - Visualizing Machine Learning Results Training vs Test Set Comparison.mp4 | Size: (11.46 MB)
FileName :014 Step 7a - Visualizing Logistic Regression 2D Plots for Classification Models.mp4 | Size: (20.29 MB)
FileName :015 Step 7b - Visualizing Logistic Regression Interpreting Classification Results.mp4 | Size: (12.84 MB)
FileName :016 Step 7c - Visualizing Test Results Assessing Machine Learning Model Accuracy.mp4 | Size: (11.48 MB)]
Screenshot
![[Image: ykSUki2a_o.jpg]](https://images2.imgbox.com/e4/87/ykSUki2a_o.jpg)
FileName :002 Introduction to Deep Learning From Historical Context to Modern Applications.mp4 | Size: (34.64 MB)
FileName :002 How Neural Networks Learn Gradient Descent and Backpropagation Explained.mp4 | Size: (8.1 MB)
FileName :003 Understanding Neurons The Building Blocks of Artificial Neural Networks.mp4 | Size: (56.61 MB)
FileName :004 Understanding Activation Functions in Neural Networks Sigmoid, ReLU, and More.mp4 | Size: (31.51 MB)
FileName :005 How Do Neural Networks Work Step-by-Step Guide to Property Valuation Example.mp4 | Size: (29.68 MB)
FileName :006 How Do Neural Networks Learn Understanding Backpropagation and Cost Functions.mp4 | Size: (49.23 MB)
FileName :007 Mastering Gradient Descent Key to Efficient Neural Network Training.mp4 | Size: (34.24 MB)
FileName :008 How to Use Stochastic Gradient Descent for Deep Learning Optimization.mp4 | Size: (33.18 MB)
FileName :009 Understanding Backpropagation Algorithm Key to Optimizing Deep Learning Models.mp4 | Size: (20.35 MB)
FileName :002 Step 1 - Data Preprocessing for Deep Learning Preparing Neural Network Dataset.mp4 | Size: (36.33 MB)
FileName :004 Step 2 - Data Preprocessing for Neural Networks Essential Steps and Techniques.mp4 | Size: (69.12 MB)
FileName :005 Step 3 - Constructing an Artificial Neural Network Adding Input --& Hidden Layers.mp4 | Size: (54.85 MB)
FileName :006 Step 4 - Compile and Train Neural Network Optimizers, Loss Functions --& Metrics.mp4 | Size: (45.41 MB)
FileName :007 Step 5 - How to Make Predictions and Evaluate Neural Network Model in Python.mp4 | Size: (61.64 MB)
FileName :002 Understanding CNN Architecture From Convolution to Fully Connected Layers.mp4 | Size: (10.68 MB)
FileName :003 How Do Convolutional Neural Networks Work Understanding CNN Architecture.mp4 | Size: (54.97 MB)
FileName :004 How to Apply Convolution Filters in Neural Networks Feature Detection Explained.mp4 | Size: (44.4 MB)
FileName :005 Rectified Linear Units --(ReLU--) in Deep Learning Optimizing CNN Performance.mp4 | Size: (25.18 MB)
FileName :006 Understanding Spatial Invariance in CNNs Max Pooling Explained for Beginners.mp4 | Size: (55.67 MB)
FileName :007 How to Flatten Pooled Feature Maps in Convolutional Neural Networks --(CNNs--).mp4 | Size: (6.14 MB)
FileName :008 How Do Fully Connected Layers Work in Convolutional Neural Networks --(CNNs--).mp4 | Size: (52.78 MB)
FileName :009 CNN Building Blocks Feature Maps, ReLU, Pooling, and Fully Connected Layers.mp4 | Size: (16.4 MB)
FileName :010 Understanding Softmax Activation and Cross-Entropy Loss in Deep Learning.mp4 | Size: (67.3 MB)
FileName :002 Step 1 - Convolutional Neural Networks Explained Image Classification Tutorial.mp4 | Size: (27.8 MB)
FileName :003 Step 2 - Deep Learning Preprocessing Scaling --& Transforming Images for CNNs.mp4 | Size: (67.42 MB)
FileName :004 Step 3 - Building CNN Architecture Convolutional Layers --& Max Pooling Explained.mp4 | Size: (68.02 MB)
FileName :005 Step 4 - Train CNN for Image Classification Optimize with Keras --& TensorFlow.mp4 | Size: (27.99 MB)
FileName :006 Step 5 - Deploying a CNN for Real-World Image Recognition.mp4 | Size: (56.64 MB)
FileName :007 Develop an Image Recognition System Using Convolutional Neural Networks.mp4 | Size: (87.16 MB)
FileName :002 How Do Recurrent Neural Networks --(RNNs--) Work Deep Learning Explained.mp4 | Size: (6.88 MB)
FileName :003 What is a Recurrent Neural Network --(RNN--) Deep Learning for Sequential Data.mp4 | Size: (43.59 MB)
FileName :004 Understanding the Vanishing Gradient Problem in Recurrent Neural Networks --(RNNs--).mp4 | Size: (54.89 MB)
FileName :005 Understanding Long Short-Term Memory --(LSTM--) Architecture for Deep Learning.mp4 | Size: (75.06 MB)
FileName :006 How LSTMs Work in Practice Visualizing Neural Network Predictions.mp4 | Size: (64.06 MB)
FileName :007 LSTM Variations Peepholes, Combined Gates, and GRUs in Deep Learning.mp4 | Size: (13.76 MB)
FileName :002 Step 1 - Building a Robust LSTM Neural Network for Stock Price Trend Prediction.mp4 | Size: (24.65 MB)
FileName :003 Step 2 - Importing Training Data for LSTM Stock Price Prediction Model.mp4 | Size: (26.81 MB)
FileName :004 Step 3 - Applying Min-Max Normalization for Time Series Data in Neural Networks.mp4 | Size: (22.62 MB)
FileName :005 Step 4 - Building X train and y train Arrays for LSTM Time Series Forecasting.mp4 | Size: (57.77 MB)
FileName :006 Step 5 - Preparing Time Series Data for LSTM Neural Network in Stock Forecasting.mp4 | Size: (41.4 MB)
FileName :007 Step 6 - Create RNN Architecture Sequential Layers vs Computational Graphs.mp4 | Size: (10.81 MB)
FileName :008 Step 7 - Adding First LSTM Layer Key Components for Stock Market Prediction.mp4 | Size: (33.05 MB)
FileName :009 Step 8 - Implementing Dropout Regularization in LSTM Networks for Forecasting.mp4 | Size: (20.26 MB)
FileName :010 Step 9 - Finalizing RNN Architecture Dense Layer for Stock Price Forecasting.mp4 | Size: (12.68 MB)
FileName :011 Step 10 - Compile RNN with Adam Optimizer for Stock Price Prediction in Python.mp4 | Size: (16.56 MB)
FileName :012 Step 11 - Optimizing Epochs and Batch Size for LSTM Stock Price Forecasting.mp4 | Size: (41.48 MB)
FileName :013 Step 12 - Visualizing LSTM Predictions Real vs Forecasted Google Stock Prices.mp4 | Size: (21.26 MB)
FileName :014 Step 13 - Preparing Historical Stock Data for LSTM Model Scaling and Reshaping.mp4 | Size: (64.1 MB)
FileName :015 Step 14 - Creating 3D Input Structure for LSTM Stock Price Prediction in Python.mp4 | Size: (31.64 MB)
FileName :016 Step 15 - Visualizing LSTM Predictions Plotting Real vs Predicted Stock Prices.mp4 | Size: (34.33 MB)
FileName :001 How Do Self-Organizing Maps Work Understanding SOM in Deep Learning.mp4 | Size: (9.5 MB)
FileName :002 Self-Organizing Maps --(SOM--) Unsupervised Deep Learning for Dimensionality Reduct.mp4 | Size: (32.54 MB)
FileName :003 Why K-Means Clustering is Essential for Understanding Self-Organizing Maps.mp4 | Size: (7.14 MB)
FileName :004 Self-Organizing Maps Tutorial Dimensionality Reduction in Machine Learning.mp4 | Size: (53.79 MB)
FileName :005 How Self-Organizing Maps --(SOMs--) Learn Unsupervised Deep Learning Explained.mp4 | Size: (38.91 MB)
FileName :006 How to Create a Self-Organizing Map --(SOM--) in DL Step-by-Step Tutorial.mp4 | Size: (25.35 MB)
FileName :007 Interpreting SOM Clusters Unsupervised Learning Techniques for Data Analysis.mp4 | Size: (16.99 MB)
FileName :008 Understanding K-Means Clustering Intuitive Explanation with Visual Examples.mp4 | Size: (54.69 MB)
FileName :009 K-Means Clustering Avoiding the Random Initialization Trap in Machine Learning.mp4 | Size: (29.66 MB)
FileName :010 How to Find the Optimal Number of Clusters in K-Means WCSS and Elbow Method.mp4 | Size: (43.17 MB)
FileName :002 Step 1 - Implementing Self-Organizing Maps --(SOMs--) for Fraud Detection in Python.mp4 | Size: (52 MB)
FileName :003 Step 2 - SOM Weight Initialization and Training Tutorial for Anomaly Detection.mp4 | Size: (36.64 MB)
FileName :004 Step 3 - SOM Visualization Techniques Colorbar --& Markers for Outlier Detection.mp4 | Size: (64.29 MB)
FileName :005 Step 4 - Catching Cheaters with SOMs Mapping Winning Nodes to Customer Data.mp4 | Size: (44.87 MB)
FileName :002 Step 1 - Building a Hybrid Deep Learning Model for Credit Card Fraud Detection.mp4 | Size: (10.74 MB)
FileName :003 Step 2 - Developing a Fraud Detection System Using Self-Organizing Maps.mp4 | Size: (17.78 MB)
FileName :004 Step 3 - Building a Hybrid Model From Unsupervised to Supervised Deep Learning.mp4 | Size: (55.65 MB)
FileName :005 Step 4 - Implementing Fraud Detection with SOM A Deep Learning Approach.mp4 | Size: (35.35 MB)
FileName :001 Understanding Boltzmann Machines Deep Learning Fundamentals for AI Enthusiasts.mp4 | Size: (6.5 MB)
FileName :002 Boltzmann Machines vs Neural Networks Key Differences in Deep Learning.mp4 | Size: (54.47 MB)
FileName :003 Deep Learning Fundamentals Energy-Based Models --& Their Role in Neural Networks.mp4 | Size: (40.46 MB)
FileName :004 How to Edit Wikipedia Adding Boltzmann Distribution in Deep Learning.mp4 | Size: (13.32 MB)
FileName :005 How Restricted Boltzmann Machines Work Deep Learning for Recommender Systems.mp4 | Size: (47.56 MB)
FileName :006 How Energy-Based Models Work Deep Dive into Contrastive Divergence Algorithm.mp4 | Size: (59.23 MB)
FileName :007 Deep Belief Networks Understanding RBM Stacking in Deep Learning Models.mp4 | Size: (20.47 MB)
FileName :008 Deep Boltzmann Machines vs Deep Belief Networks Key Differences Explained.mp4 | Size: (11.2 MB)
FileName :002 Step 0 - Building a Movie Recommender System with RBMs Data Preprocessing Guide.mp4 | Size: (34.79 MB)
FileName :004 Step 1 - Importing Movie Datasets for RBM-Based Recommender Systems in Python.mp4 | Size: (35.1 MB)
FileName :005 Step 2 - Preparing Training and Test Sets for Restricted Boltzmann Machine.mp4 | Size: (36.67 MB)
FileName :006 Step 3 - Preparing Data for RBM Calculating Total Users and Movies in Python.mp4 | Size: (31.89 MB)
FileName :007 Step 4 - Convert Training --& Test Sets to RBM-Ready Arrays in Python.mp4 | Size: (79.37 MB)
FileName :008 Step 5 - Converting NumPy Arrays to PyTorch Tensors for Deep Learning Models.mp4 | Size: (19.37 MB)
FileName :009 Step 6 - RBM Data Preprocessing Transforming Movie Ratings for Neural Networks.mp4 | Size: (29.11 MB)
FileName :010 Step 7 - Implementing Restricted Boltzmann Machine Class Structure in PyTorch.mp4 | Size: (38.87 MB)
FileName :011 Step 8 - RBM Hidden Layer Sampling Bernoulli Distribution in PyTorch Tutorial.mp4 | Size: (48.35 MB)
FileName :012 Step 9 - RBM Visible Node Sampling Bernoulli Distribution in Deep Learning.mp4 | Size: (23.88 MB)
FileName :013 Step 10 - RBM Training Function Updating Weights and Biases with Gibbs Sampling.mp4 | Size: (44.32 MB)
FileName :014 Step 11 - How to Set Up an RBM Model Choosing NV, NH, and Batch Size Parameters.mp4 | Size: (27.04 MB)
FileName :015 Step 12 - RBM Training Loop Epoch Setup and Loss Function Implementation.mp4 | Size: (51.02 MB)
FileName :016 Step 13 - RBM Training Updating Weights and Biases with Contrastive Divergence.mp4 | Size: (73.76 MB)
FileName :017 Step 14 - Optimizing RBM Models From Training to Test Set Performance Analysis.mp4 | Size: (65.13 MB)
FileName :001 Deep Learning Autoencoders Types, Architecture, and Training Explained.mp4 | Size: (8.34 MB)
FileName :002 Autoencoders in Machine Learning Applications and Architecture Overview.mp4 | Size: (25.64 MB)
FileName :003 Autoencoder Bias in Deep Learning Improving Neural Network Performance.mp4 | Size: (4.8 MB)
FileName :004 How to Train an Autoencoder Step-by-Step Guide for Deep Learning Beginners.mp4 | Size: (23.48 MB)
FileName :005 How to Use Overcomplete Hidden Layers in Autoencoders for Feature Extraction.mp4 | Size: (14.77 MB)
FileName :006 Sparse Autoencoders in Deep Learning Preventing Overfitting in Neural Networks.mp4 | Size: (23.69 MB)
FileName :007 Denoising Autoencoders Deep Learning Regularization Technique Explained.mp4 | Size: (9.65 MB)
FileName :008 What are Contractive Autoencoders Deep Learning Regularization Techniques.mp4 | Size: (9.08 MB)
FileName :009 What are Stacked Autoencoders in Deep Learning Architecture and Applications.mp4 | Size: (7.21 MB)
FileName :010 Deep Autoencoders vs Stacked Autoencoders Key Differences in Neural Networks.mp4 | Size: (7.07 MB)
FileName :003 Step 1 - Building a Movie Recommendation System with AutoEncoders Data Import.mp4 | Size: (41.61 MB)
FileName :004 Step 2 - Preparing Training and Test Sets for Autoencoder Recommendation System.mp4 | Size: (40.63 MB)
FileName :005 Step 3 - Preparing Data for Recommendation Systems User --& Movie Count in Python.mp4 | Size: (28.89 MB)
FileName :007 Step 4 - Prepare Data for Autoencoder Creating User-Movie Rating Matrices.mp4 | Size: (72.17 MB)
FileName :008 Step 5 - Convert Training and Test Sets to PyTorch Tensors for Deep Learning.mp4 | Size: (17.56 MB)
FileName :009 Step 6 - Building Autoencoder Architecture Class Creation for Neural Networks.mp4 | Size: (58.44 MB)
FileName :010 Step 7 - Python Autoencoder Tutorial Implementing Activation Functions --& Layers.mp4 | Size: (47.82 MB)
FileName :011 Step 8 - PyTorch Techniques for Efficient Autoencoder Training on Large Datasets.mp4 | Size: (51.87 MB)
FileName :012 Step 9 - Implementing Stochastic Gradient Descent in Autoencoder Architecture.mp4 | Size: (46.64 MB)
FileName :013 Step 10 - Machine Learning Metrics Interpreting Loss in Autoencoder Training.mp4 | Size: (15.25 MB)
FileName :014 Step 11 - How to Evaluate Recommender System Performance Using Test Set Loss.mp4 | Size: (40.13 MB)
FileName :015 THANK YOU Video.mp4 | Size: (9.18 MB)
FileName :002 Simple Linear Regression Understanding Y = B0 + B1X in Machine Learning.mp4 | Size: (16.29 MB)
FileName :003 Linear Regression Explained Finding the Best Fitting Line for Data Analysis.mp4 | Size: (10.86 MB)
FileName :004 Multiple Linear Regression - Understanding Dependent --& Independent Variables.mp4 | Size: (3.33 MB)
FileName :005 Understanding Logistic Regression Intuition and Probability in Classification.mp4 | Size: (58.02 MB)
FileName :002 How to Scale Features in Machine Learning Normalization vs Standardization.mp4 | Size: (5.29 MB)
FileName :003 Machine Learning Basics Using Train-Test Split to Evaluate Model Performance.mp4 | Size: (7.02 MB)
FileName :004 Machine Learning Workflow Data Splitting, Feature Scaling, and Model Training.mp4 | Size: (16.54 MB)
FileName :001 Step 1 - Data Preprocessing in Python Essential Tools for ML Models.mp4 | Size: (18.4 MB)
FileName :002 Step 2 - How to Handle Missing Data in Python Data Preprocessing Techniques.mp4 | Size: (18.44 MB)
FileName :003 Step 1 - Importing Essential Python Libraries for Data Preprocessing --& Analysis.mp4 | Size: (12.25 MB)
FileName :004 Step 1 - Creating a DataFrame from CSV Python Data Preprocessing Basics.mp4 | Size: (17.97 MB)
FileName :005 Step 2 - Pandas DataFrame Indexing Building Feature Matrix X with iloc Method.mp4 | Size: (16.19 MB)
FileName :006 Step 3 - Preprocessing Data Extracting Features and Target Variables in Python.mp4 | Size: (19.83 MB)
FileName :008 Step 1 - Handling Missing Data in Python SimpleImputer for Data Preprocessing.mp4 | Size: (20.39 MB)
FileName :009 Step 2 - Preprocessing Datasets Fit and Transform to Handle Missing Values.mp4 | Size: (20.54 MB)
FileName :010 Step 1 - Preprocessing Categorical Variables One-Hot Encoding in Python.mp4 | Size: (15.17 MB)
FileName :011 Step 2 - Using fit transform Method for Efficient Data Preprocessing in Python.mp4 | Size: (20.27 MB)
FileName :012 Step 3 - Preprocessing Categorical Data One-Hot and Label Encoding Techniques.mp4 | Size: (16.03 MB)
FileName :013 Step 1 - Machine Learning Data Prep Splitting Dataset Before Feature Scaling.mp4 | Size: (13.46 MB)
FileName :014 Step 2 - Split Data into Train --& Test Sets with Scikit-learn--'s train test split.mp4 | Size: (20.58 MB)
FileName :015 Step 3 - Preparing Data for ML Splitting Datasets with Python and Scikit-learn.mp4 | Size: (13.33 MB)
FileName :016 Step 1 - How to Apply Feature Scaling for Preprocessing Machine Learning Data.mp4 | Size: (20.45 MB)
FileName :017 Step 2 - Feature Scaling in Machine Learning When to Apply StandardScaler.mp4 | Size: (16.34 MB)
FileName :018 Step 3 - Normalizing Data with Fit and Transform Methods in Scikit-learn.mp4 | Size: (13.1 MB)
FileName :019 Step 4 - How to Apply Feature Scaling to Training --& Test Sets in ML.mp4 | Size: (20.17 MB)
FileName :001 Understanding the Logistic Regression Equation A Step-by-Step Guide.mp4 | Size: (16.91 MB)
FileName :002 How to Calculate Maximum Likelihood in Logistic Regression Step-by-Step Guide.mp4 | Size: (9.66 MB)
FileName :003 Step 1a - Machine Learning Classification Logistic Regression in Python.mp4 | Size: (19.65 MB)
FileName :004 Step 1b - Logistic Regression Analysis Importing Libraries and Splitting Data.mp4 | Size: (13.71 MB)
FileName :005 Step 2a - Data Preprocessing for Logistic Regression Importing and Splitting.mp4 | Size: (20.11 MB)
FileName :006 Step 2b - Data Preprocessing Feature Scaling for Machine Learning in Python.mp4 | Size: (20.46 MB)
FileName :007 Step 3a - Implementing Logistic Regression for Classification with Scikit-Learn.mp4 | Size: (13.67 MB)
FileName :008 Step 3b - Predicting Purchase Decisions with Logistic Regression in Python.mp4 | Size: (12.03 MB)
FileName :009 Step 4a - Using Classifier Objects to Make Predictions in Machine Learning.mp4 | Size: (20.56 MB)
FileName :010 Step 4b - Evaluating Logistic Regression Model Predicted vs Real Outcomes.mp4 | Size: (6.27 MB)
FileName :011 Step 5 - Evaluating Machine Learning Models Confusion Matrix and Accuracy.mp4 | Size: (20.42 MB)
FileName :012 Step 6a - Creating a Confusion Matrix for Machine Learning Model Evaluation.mp4 | Size: (20.2 MB)
FileName :013 Step 6b - Visualizing Machine Learning Results Training vs Test Set Comparison.mp4 | Size: (11.46 MB)
FileName :014 Step 7a - Visualizing Logistic Regression 2D Plots for Classification Models.mp4 | Size: (20.29 MB)
FileName :015 Step 7b - Visualizing Logistic Regression Interpreting Classification Results.mp4 | Size: (12.84 MB)
FileName :016 Step 7c - Visualizing Test Results Assessing Machine Learning Model Accuracy.mp4 | Size: (11.48 MB)]
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