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Название Udemy-Deep-Learning-A-Z-2025
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001 Annex - Get the Machine Learning Basics.html 3.10Кб
001 Data Preprocessing.html 2.69Кб
001 Deep Learning Autoencoders Types, Architecture, and Training Explained.mp4 8.34Мб
001 Deep Learning Autoencoders Types, Architecture, and Training Explained.srt 3.77Кб
001 Evaluating the RNN.html 3.97Кб
001 Get the code and dataset ready.html 4.01Кб
001 Get the code and dataset ready.html 3.79Кб
001 Get the code and dataset ready.html 4.51Кб
001 Get the code and dataset ready.html 4.40Кб
001 Get the code and dataset ready.html 4.41Кб
001 Get the code and dataset ready.html 4.83Кб
001 Get the code and dataset ready.html 4.83Кб
001 How Do Self-Organizing Maps Work Understanding SOM in Deep Learning.mp4 9.50Мб
001 How Do Self-Organizing Maps Work Understanding SOM in Deep Learning.srt 5.31Кб
001 Huge Congrats for completing the challenge!.html 6.99Кб
001 Step 1 - Data Preprocessing in Python Essential Tools for ML Models.mp4 18.40Мб
001 Step 1 - Data Preprocessing in Python Essential Tools for ML Models.srt 8.98Кб
001 Understanding Boltzmann Machines Deep Learning Fundamentals for AI Enthusiasts.mp4 6.50Мб
001 Understanding Boltzmann Machines Deep Learning Fundamentals for AI Enthusiasts.srt 4.60Кб
001 Understanding the Logistic Regression Equation A Step-by-Step Guide.mp4 16.91Мб
001 Understanding the Logistic Regression Equation A Step-by-Step Guide.srt 8.15Кб
001 Welcome Challenge!.html 5.80Кб
001 Welcome to Part 1 - Artificial Neural Networks.html 2.53Кб
001 Welcome to Part 2 - Convolutional Neural Networks.html 2.51Кб
001 Welcome to Part 3 - Recurrent Neural Networks.html 2.71Кб
001 Welcome to Part 4 - Self Organizing Maps.html 2.59Кб
001 Welcome to Part 5 - Boltzmann Machines.html 3.62Кб
001 Welcome to Part 6 - AutoEncoders.html 3.17Кб
001 What You'll Need for ANN.html 2.55Кб
001 What You'll Need for CNN.html 2.56Кб
001 What You'll Need for RNN.html 2.55Кб
001 What You Need for Regression & Classification.html 2.57Кб
002 Autoencoders in Machine Learning Applications and Architecture Overview.mp4 25.64Мб
002 Autoencoders in Machine Learning Applications and Architecture Overview.srt 19.46Кб
002 Boltzmann Machines vs. Neural Networks Key Differences in Deep Learning.mp4 54.47Мб
002 Boltzmann Machines vs. Neural Networks Key Differences in Deep Learning.srt 24.33Кб
002 Bonus How To UNLOCK Top Salaries (Live Training).html 4.00Кб
002 How Do Recurrent Neural Networks --(RNNs--) Work Deep Learning Explained.mp4 6.88Мб
002 How Do Recurrent Neural Networks --(RNNs--) Work Deep Learning Explained.srt 4.00Кб
002 How Neural Networks Learn Gradient Descent and Backpropagation Explained.mp4 8.10Мб
002 How Neural Networks Learn Gradient Descent and Backpropagation Explained.srt 4.51Кб
002 How to Calculate Maximum Likelihood in Logistic Regression Step-by-Step Guide.mp4 9.66Мб
002 How to Calculate Maximum Likelihood in Logistic Regression Step-by-Step Guide.srt 6.04Кб
002 How to Scale Features in Machine Learning Normalization vs Standardization.mp4 5.29Мб
002 How to Scale Features in Machine Learning Normalization vs Standardization.srt 2.74Кб
002 Improving the RNN.html 3.48Кб
002 Introduction to Deep Learning From Historical Context to Modern Applications.mp4 34.64Мб
002 Introduction to Deep Learning From Historical Context to Modern Applications.srt 21.27Кб
002 Same Data Preprocessing in Parts 5 and 6.html 2.58Кб
002 Self-Organizing Maps --(SOM--) Unsupervised Deep Learning for Dimensionality Reduct.mp4 32.54Мб
002 Self-Organizing Maps --(SOM--) Unsupervised Deep Learning for Dimensionality Reduct.srt 14.81Кб
002 Simple Linear Regression Understanding Y = B0 + B1X in Machine Learning.mp4 16.29Мб
002 Simple Linear Regression Understanding Y = B0 + B1X in Machine Learning.srt 9.57Кб
002 Step 0 - Building a Movie Recommender System with RBMs Data Preprocessing Guide.mp4 34.79Мб
002 Step 0 - Building a Movie Recommender System with RBMs Data Preprocessing Guide.srt 16.74Кб
002 Step 1 - Building a Hybrid Deep Learning Model for Credit Card Fraud Detection.mp4 10.74Мб
002 Step 1 - Building a Hybrid Deep Learning Model for Credit Card Fraud Detection.srt 5.18Кб
002 Step 1 - Building a Robust LSTM Neural Network for Stock Price Trend Prediction.mp4 24.65Мб
002 Step 1 - Building a Robust LSTM Neural Network for Stock Price Trend Prediction.srt 12.82Кб
002 Step 1 - Convolutional Neural Networks Explained Image Classification Tutorial.mp4 27.80Мб
002 Step 1 - Convolutional Neural Networks Explained Image Classification Tutorial.srt 13.60Кб
002 Step 1 - Data Preprocessing for Deep Learning Preparing Neural Network Dataset.mp4 36.33Мб
002 Step 1 - Data Preprocessing for Deep Learning Preparing Neural Network Dataset.srt 18.73Кб
002 Step 1 - Implementing Self-Organizing Maps --(SOMs--) for Fraud Detection in Python.mp4 52.00Мб
002 Step 1 - Implementing Self-Organizing Maps --(SOMs--) for Fraud Detection in Python.srt 27.72Кб
002 Step 2 - How to Handle Missing Data in Python Data Preprocessing Techniques.mp4 18.44Мб
002 Step 2 - How to Handle Missing Data in Python Data Preprocessing Techniques.srt 11.38Кб
002 Understanding CNN Architecture From Convolution to Fully Connected Layers.mp4 10.68Мб
002 Understanding CNN Architecture From Convolution to Fully Connected Layers.srt 6.03Кб
003 Autoencoder Bias in Deep Learning Improving Neural Network Performance.mp4 4.80Мб
003 Autoencoder Bias in Deep Learning Improving Neural Network Performance.srt 2.33Кб
003 Check out our free course on ANN for Regression.html 2.78Кб
003 Deep Learning Fundamentals Energy-Based Models --& Their Role in Neural Networks.mp4 40.46Мб
003 Deep Learning Fundamentals Energy-Based Models --& Their Role in Neural Networks.srt 17.87Кб
003 Get the codes, datasets and slides here.html 2.75Кб
003 How Do Convolutional Neural Networks Work Understanding CNN Architecture.mp4 54.97Мб
003 How Do Convolutional Neural Networks Work Understanding CNN Architecture.srt 26.34Кб
003 Linear Regression Explained Finding the Best Fitting Line for Data Analysis.mp4 10.86Мб
003 Linear Regression Explained Finding the Best Fitting Line for Data Analysis.srt 4.95Кб
003 Machine Learning Basics Using Train-Test Split to Evaluate Model Performance.mp4 7.02Мб
003 Machine Learning Basics Using Train-Test Split to Evaluate Model Performance.srt 3.30Кб
003 Same Data Preprocessing in Parts 5 and 6.html 2.59Кб
003 Step 1a - Machine Learning Classification Logistic Regression in Python.mp4 19.65Мб
003 Step 1a - Machine Learning Classification Logistic Regression in Python.srt 9.08Кб
003 Step 1 - Building a Movie Recommendation System with AutoEncoders Data Import.mp4 41.61Мб
003 Step 1 - Building a Movie Recommendation System with AutoEncoders Data Import.srt 20.91Кб
003 Step 1 - Importing Essential Python Libraries for Data Preprocessing --& Analysis.mp4 12.25Мб
003 Step 1 - Importing Essential Python Libraries for Data Preprocessing --& Analysis.srt 6.20Кб
003 Step 2 - Deep Learning Preprocessing Scaling --& Transforming Images for CNNs.mp4 67.42Мб
003 Step 2 - Deep Learning Preprocessing Scaling --& Transforming Images for CNNs.srt 30.55Кб
003 Step 2 - Developing a Fraud Detection System Using Self-Organizing Maps.mp4 17.78Мб
003 Step 2 - Developing a Fraud Detection System Using Self-Organizing Maps.srt 7.40Кб
003 Step 2 - Importing Training Data for LSTM Stock Price Prediction Model.mp4 26.81Мб
003 Step 2 - Importing Training Data for LSTM Stock Price Prediction Model.srt 11.25Кб
003 Step 2 - SOM Weight Initialization and Training Tutorial for Anomaly Detection.mp4 36.64Мб
003 Step 2 - SOM Weight Initialization and Training Tutorial for Anomaly Detection.srt 16.01Кб
003 Understanding Neurons The Building Blocks of Artificial Neural Networks.mp4 56.61Мб
003 Understanding Neurons The Building Blocks of Artificial Neural Networks.srt 29.50Кб
003 What is a Recurrent Neural Network --(RNN--) Deep Learning for Sequential Data.mp4 43.59Мб
003 What is a Recurrent Neural Network --(RNN--) Deep Learning for Sequential Data.srt 27.88Кб
003 Why K-Means Clustering is Essential for Understanding Self-Organizing Maps.mp4 7.14Мб
003 Why K-Means Clustering is Essential for Understanding Self-Organizing Maps.srt 3.90Кб
004 EXTRA Use ChatGPT to Boost your Deep Learning Skills.html 3.26Кб
004 How to Apply Convolution Filters in Neural Networks Feature Detection Explained.mp4 44.40Мб
004 How to Apply Convolution Filters in Neural Networks Feature Detection Explained.srt 28.06Кб
004 How to Edit Wikipedia Adding Boltzmann Distribution in Deep Learning.mp4 13.32Мб
004 How to Edit Wikipedia Adding Boltzmann Distribution in Deep Learning.srt 6.43Кб
004 How to Train an Autoencoder Step-by-Step Guide for Deep Learning Beginners.mp4 23.48Мб
004 How to Train an Autoencoder Step-by-Step Guide for Deep Learning Beginners.srt 11.23Кб
004 Machine Learning Workflow Data Splitting, Feature Scaling, and Model Training.mp4 16.54Мб
004 Machine Learning Workflow Data Splitting, Feature Scaling, and Model Training.srt 10.73Кб
004 Multiple Linear Regression - Understanding Dependent --& Independent Variables.mp4 3.33Мб
004 Multiple Linear Regression - Understanding Dependent --& Independent Variables.srt 1.70Кб
004 Self-Organizing Maps Tutorial Dimensionality Reduction in Machine Learning.mp4 53.79Мб
004 Self-Organizing Maps Tutorial Dimensionality Reduction in Machine Learning.srt 26.03Кб
004 Step 1b - Logistic Regression Analysis Importing Libraries and Splitting Data.mp4 13.71Мб
004 Step 1b - Logistic Regression Analysis Importing Libraries and Splitting Data.srt 7.07Кб
004 Step 1 - Creating a DataFrame from CSV Python Data Preprocessing Basics.mp4 17.97Мб
004 Step 1 - Creating a DataFrame from CSV Python Data Preprocessing Basics.srt 8.81Кб
004 Step 1 - Importing Movie Datasets for RBM-Based Recommender Systems in Python.mp4 35.10Мб
004 Step 1 - Importing Movie Datasets for RBM-Based Recommender Systems in Python.srt 15.92Кб
004 Step 2 - Data Preprocessing for Neural Networks Essential Steps and Techniques.mp4 69.12Мб
004 Step 2 - Data Preprocessing for Neural Networks Essential Steps and Techniques.srt 30.87Кб
004 Step 2 - Preparing Training and Test Sets for Autoencoder Recommendation System.mp4 40.63Мб
004 Step 2 - Preparing Training and Test Sets for Autoencoder Recommendation System.srt 19.99Кб
004 Step 3 - Applying Min-Max Normalization for Time Series Data in Neural Networks.mp4 22.62Мб
004 Step 3 - Applying Min-Max Normalization for Time Series Data in Neural Networks.srt 9.45Кб
004 Step 3 - Building a Hybrid Model From Unsupervised to Supervised Deep Learning.mp4 55.65Мб
004 Step 3 - Building a Hybrid Model From Unsupervised to Supervised Deep Learning.srt 30.19Кб
004 Step 3 - Building CNN Architecture Convolutional Layers --& Max Pooling Explained.mp4 68.02Мб
004 Step 3 - Building CNN Architecture Convolutional Layers --& Max Pooling Explained.srt 36.66Кб
004 Step 3 - SOM Visualization Techniques Colorbar --& Markers for Outlier Detection.mp4 64.29Мб
004 Step 3 - SOM Visualization Techniques Colorbar --& Markers for Outlier Detection.srt 29.15Кб
004 Understanding Activation Functions in Neural Networks Sigmoid, ReLU, and More.mp4 31.51Мб
004 Understanding Activation Functions in Neural Networks Sigmoid, ReLU, and More.srt 14.14Кб
004 Understanding the Vanishing Gradient Problem in Recurrent Neural Networks --(RNNs--).mp4 54.89Мб
004 Understanding the Vanishing Gradient Problem in Recurrent Neural Networks --(RNNs--).srt 26.54Кб
005 How Do Neural Networks Work Step-by-Step Guide to Property Valuation Example.mp4 29.68Мб
005 How Do Neural Networks Work Step-by-Step Guide to Property Valuation Example.srt 22.97Кб
005 How Restricted Boltzmann Machines Work Deep Learning for Recommender Systems.mp4 47.56Мб
005 How Restricted Boltzmann Machines Work Deep Learning for Recommender Systems.srt 31.55Кб
005 How Self-Organizing Maps --(SOMs--) Learn Unsupervised Deep Learning Explained.mp4 38.91Мб
005 How Self-Organizing Maps --(SOMs--) Learn Unsupervised Deep Learning Explained.srt 24.78Кб
005 How to Use Overcomplete Hidden Layers in Autoencoders for Feature Extraction.mp4 14.77Мб
005 How to Use Overcomplete Hidden Layers in Autoencoders for Feature Extraction.srt 6.53Кб
005 Rectified Linear Units --(ReLU--) in Deep Learning Optimizing CNN Performance.mp4 25.18Мб
005 Rectified Linear Units --(ReLU--) in Deep Learning Optimizing CNN Performance.srt 10.99Кб
005 Step 2a - Data Preprocessing for Logistic Regression Importing and Splitting.mp4 20.11Мб
005 Step 2a - Data Preprocessing for Logistic Regression Importing and Splitting.srt 9.98Кб
005 Step 2 - Pandas DataFrame Indexing Building Feature Matrix X with iloc Method.mp4 16.19Мб
005 Step 2 - Pandas DataFrame Indexing Building Feature Matrix X with iloc Method.srt 8.01Кб
005 Step 2 - Preparing Training and Test Sets for Restricted Boltzmann Machine.mp4 36.67Мб
005 Step 2 - Preparing Training and Test Sets for Restricted Boltzmann Machine.srt 16.23Кб
005 Step 3 - Constructing an Artificial Neural Network Adding Input --& Hidden Layers.mp4 54.85Мб
005 Step 3 - Constructing an Artificial Neural Network Adding Input --& Hidden Layers.srt 24.56Кб
005 Step 3 - Preparing Data for Recommendation Systems User --& Movie Count in Python.mp4 28.89Мб
005 Step 3 - Preparing Data for Recommendation Systems User --& Movie Count in Python.srt 16.44Кб
005 Step 4 - Building X_train and y_train Arrays for LSTM Time Series Forecasting.mp4 57.77Мб
005 Step 4 - Building X_train and y_train Arrays for LSTM Time Series Forecasting.srt 24.32Кб
005 Step 4 - Catching Cheaters with SOMs Mapping Winning Nodes to Customer Data.mp4 44.87Мб
005 Step 4 - Catching Cheaters with SOMs Mapping Winning Nodes to Customer Data.srt 21.78Кб
005 Step 4 - Implementing Fraud Detection with SOM A Deep Learning Approach.mp4 35.35Мб
005 Step 4 - Implementing Fraud Detection with SOM A Deep Learning Approach.srt 18.90Кб
005 Step 4 - Train CNN for Image Classification Optimize with Keras --& TensorFlow.mp4 27.99Мб
005 Step 4 - Train CNN for Image Classification Optimize with Keras --& TensorFlow.srt 12.28Кб
005 Understanding Logistic Regression Intuition and Probability in Classification.mp4 58.02Мб
005 Understanding Logistic Regression Intuition and Probability in Classification.srt 28.27Кб
005 Understanding Long Short-Term Memory --(LSTM--) Architecture for Deep Learning.mp4 75.06Мб
005 Understanding Long Short-Term Memory --(LSTM--) Architecture for Deep Learning.srt 33.60Кб
006 Homework Challenge - Coding Exercise.html 3.85Кб
006 How Do Neural Networks Learn Understanding Backpropagation and Cost Functions.mp4 49.23Мб
006 How Do Neural Networks Learn Understanding Backpropagation and Cost Functions.srt 21.74Кб
006 How Energy-Based Models Work Deep Dive into Contrastive Divergence Algorithm.mp4 59.23Мб
006 How Energy-Based Models Work Deep Dive into Contrastive Divergence Algorithm.srt 28.88Кб
006 How LSTMs Work in Practice Visualizing Neural Network Predictions.mp4 64.06Мб
006 How LSTMs Work in Practice Visualizing Neural Network Predictions.srt 24.86Кб
006 How to Create a Self-Organizing Map --(SOM--) in DL Step-by-Step Tutorial.mp4 25.35Мб
006 How to Create a Self-Organizing Map --(SOM--) in DL Step-by-Step Tutorial.srt 16.38Кб
006 Sparse Autoencoders in Deep Learning Preventing Overfitting in Neural Networks.mp4 23.69Мб
006 Sparse Autoencoders in Deep Learning Preventing Overfitting in Neural Networks.srt 10.19Кб
006 Step 2b - Data Preprocessing Feature Scaling for Machine Learning in Python.mp4 20.46Мб
006 Step 2b - Data Preprocessing Feature Scaling for Machine Learning in Python.srt 10.08Кб
006 Step 3 - Preparing Data for RBM Calculating Total Users and Movies in Python.mp4 31.89Мб
006 Step 3 - Preparing Data for RBM Calculating Total Users and Movies in Python.srt 16.44Кб
006 Step 3 - Preprocessing Data Extracting Features and Target Variables in Python.mp4 19.83Мб
006 Step 3 - Preprocessing Data Extracting Features and Target Variables in Python.srt 10.01Кб
006 Step 4 - Compile and Train Neural Network Optimizers, Loss Functions --& Metrics.mp4 45.41Мб
006 Step 4 - Compile and Train Neural Network Optimizers, Loss Functions --& Metrics.srt 20.31Кб
006 Step 5 - Deploying a CNN for Real-World Image Recognition.mp4 56.64Мб
006 Step 5 - Deploying a CNN for Real-World Image Recognition.srt 29.46Кб
006 Step 5 - Preparing Time Series Data for LSTM Neural Network in Stock Forecasting.mp4 41.40Мб
006 Step 5 - Preparing Time Series Data for LSTM Neural Network in Stock Forecasting.srt 19.96Кб
006 Understanding Spatial Invariance in CNNs Max Pooling Explained for Beginners.mp4 55.67Мб
006 Understanding Spatial Invariance in CNNs Max Pooling Explained for Beginners.srt 25.27Кб
007 Deep Belief Networks Understanding RBM Stacking in Deep Learning Models.mp4 20.47Мб
007 Deep Belief Networks Understanding RBM Stacking in Deep Learning Models.srt 8.77Кб
007 Denoising Autoencoders Deep Learning Regularization Technique Explained.mp4 9.65Мб
007 Denoising Autoencoders Deep Learning Regularization Technique Explained.srt 4.25Кб
007 Develop an Image Recognition System Using Convolutional Neural Networks.mp4 87.16Мб
007 Develop an Image Recognition System Using Convolutional Neural Networks.srt 36.53Кб
007 For Python learners, summary of Object-oriented programming classes & objects.html 3.71Кб
007 How to Flatten Pooled Feature Maps in Convolutional Neural Networks --(CNNs--).mp4 6.14Мб
007 How to Flatten Pooled Feature Maps in Convolutional Neural Networks --(CNNs--).srt 3.23Кб
007 Interpreting SOM Clusters Unsupervised Learning Techniques for Data Analysis.mp4 16.99Мб
007 Interpreting SOM Clusters Unsupervised Learning Techniques for Data Analysis.srt 7.57Кб
007 LSTM Variations Peepholes, Combined Gates, and GRUs in Deep Learning.mp4 13.76Мб
007 LSTM Variations Peepholes, Combined Gates, and GRUs in Deep Learning.srt 5.87Кб
007 Mastering Gradient Descent Key to Efficient Neural Network Training.mp4 34.24Мб
007 Mastering Gradient Descent Key to Efficient Neural Network Training.srt 17.55Кб
007 Step 3a - Implementing Logistic Regression for Classification with Scikit-Learn.mp4 13.67Мб
007 Step 3a - Implementing Logistic Regression for Classification with Scikit-Learn.srt 6.62Кб
007 Step 4 - Convert Training --& Test Sets to RBM-Ready Arrays in Python.mp4 79.37Мб
007 Step 4 - Convert Training --& Test Sets to RBM-Ready Arrays in Python.srt 35.14Кб
007 Step 4 - Prepare Data for Autoencoder Creating User-Movie Rating Matrices.mp4 72.17Мб
007 Step 4 - Prepare Data for Autoencoder Creating User-Movie Rating Matrices.srt 35.07Кб
007 Step 5 - How to Make Predictions and Evaluate Neural Network Model in Python.mp4 61.64Мб
007 Step 5 - How to Make Predictions and Evaluate Neural Network Model in Python.srt 26.57Кб
007 Step 6 - Create RNN Architecture Sequential Layers vs Computational Graphs.mp4 10.81Мб
007 Step 6 - Create RNN Architecture Sequential Layers vs Computational Graphs.srt 4.71Кб
008 Deep Boltzmann Machines vs Deep Belief Networks Key Differences Explained.mp4 11.20Мб
008 Deep Boltzmann Machines vs Deep Belief Networks Key Differences Explained.srt 4.94Кб
008 How Do Fully Connected Layers Work in Convolutional Neural Networks --(CNNs--).mp4 52.78Мб
008 How Do Fully Connected Layers Work in Convolutional Neural Networks --(CNNs--).srt 37.50Кб
008 How to Use Stochastic Gradient Descent for Deep Learning Optimization.mp4 33.18Мб
008 How to Use Stochastic Gradient Descent for Deep Learning Optimization.srt 14.87Кб
008 Step 1 - Handling Missing Data in Python SimpleImputer for Data Preprocessing.mp4 20.39Мб
008 Step 1 - Handling Missing Data in Python SimpleImputer for Data Preprocessing.srt 9.80Кб
008 Step 3b - Predicting Purchase Decisions with Logistic Regression in Python.mp4 12.03Мб
008 Step 3b - Predicting Purchase Decisions with Logistic Regression in Python.srt 5.61Кб
008 Step 5 - Converting NumPy Arrays to PyTorch Tensors for Deep Learning Models.mp4 19.37Мб
008 Step 5 - Converting NumPy Arrays to PyTorch Tensors for Deep Learning Models.srt 8.85Кб
008 Step 5 - Convert Training and Test Sets to PyTorch Tensors for Deep Learning.mp4 17.56Мб
008 Step 5 - Convert Training and Test Sets to PyTorch Tensors for Deep Learning.srt 8.88Кб
008 Step 7 - Adding First LSTM Layer Key Components for Stock Market Prediction.mp4 33.05Мб
008 Step 7 - Adding First LSTM Layer Key Components for Stock Market Prediction.srt 14.13Кб
008 Understanding K-Means Clustering Intuitive Explanation with Visual Examples.mp4 54.69Мб
008 Understanding K-Means Clustering Intuitive Explanation with Visual Examples.srt 24.42Кб
008 What are Contractive Autoencoders Deep Learning Regularization Techniques.mp4 9.08Мб
008 What are Contractive Autoencoders Deep Learning Regularization Techniques.srt 3.95Кб
009 CNN Building Blocks Feature Maps, ReLU, Pooling, and Fully Connected Layers.mp4 16.40Мб
009 CNN Building Blocks Feature Maps, ReLU, Pooling, and Fully Connected Layers.srt 6.83Кб
009 K-Means Clustering Avoiding the Random Initialization Trap in Machine Learning.mp4 29.66Мб
009 K-Means Clustering Avoiding the Random Initialization Trap in Machine Learning.srt 14.04Кб
009 Step 2 - Preprocessing Datasets Fit and Transform to Handle Missing Values.mp4 20.54Мб
009 Step 2 - Preprocessing Datasets Fit and Transform to Handle Missing Values.srt 9.50Кб
009 Step 4a - Using Classifier Objects to Make Predictions in Machine Learning.mp4 20.56Мб
009 Step 4a - Using Classifier Objects to Make Predictions in Machine Learning.srt 9.21Кб
009 Step 6 - Building Autoencoder Architecture Class Creation for Neural Networks.mp4 58.44Мб
009 Step 6 - Building Autoencoder Architecture Class Creation for Neural Networks.srt 29.39Кб
009 Step 6 - RBM Data Preprocessing Transforming Movie Ratings for Neural Networks.mp4 29.11Мб
009 Step 6 - RBM Data Preprocessing Transforming Movie Ratings for Neural Networks.srt 13.00Кб
009 Step 8 - Implementing Dropout Regularization in LSTM Networks for Forecasting.mp4 20.26Мб
009 Step 8 - Implementing Dropout Regularization in LSTM Networks for Forecasting.srt 8.53Кб
009 Understanding Backpropagation Algorithm Key to Optimizing Deep Learning Models.mp4 20.35Мб
009 Understanding Backpropagation Algorithm Key to Optimizing Deep Learning Models.srt 8.60Кб
009 What are Stacked Autoencoders in Deep Learning Architecture and Applications.mp4 7.21Мб
009 What are Stacked Autoencoders in Deep Learning Architecture and Applications.srt 2.85Кб
010 Deep Autoencoders vs Stacked Autoencoders Key Differences in Neural Networks.mp4 7.07Мб
010 Deep Autoencoders vs Stacked Autoencoders Key Differences in Neural Networks.srt 3.04Кб
010 How to Find the Optimal Number of Clusters in K-Means WCSS and Elbow Method.mp4 43.17Мб
010 How to Find the Optimal Number of Clusters in K-Means WCSS and Elbow Method.srt 20.40Кб
010 Step 1 - Preprocessing Categorical Variables One-Hot Encoding in Python.mp4 15.17Мб
010 Step 1 - Preprocessing Categorical Variables One-Hot Encoding in Python.srt 7.04Кб
010 Step 4b - Evaluating Logistic Regression Model Predicted vs Real Outcomes.mp4 6.27Мб
010 Step 4b - Evaluating Logistic Regression Model Predicted vs Real Outcomes.srt 3.09Кб
010 Step 7 - Implementing Restricted Boltzmann Machine Class Structure in PyTorch.mp4 38.87Мб
010 Step 7 - Implementing Restricted Boltzmann Machine Class Structure in PyTorch.srt 17.63Кб
010 Step 7 - Python Autoencoder Tutorial Implementing Activation Functions --& Layers.mp4 47.82Мб
010 Step 7 - Python Autoencoder Tutorial Implementing Activation Functions --& Layers.srt 27.41Кб
010 Step 9 - Finalizing RNN Architecture Dense Layer for Stock Price Forecasting.mp4 12.68Мб
010 Step 9 - Finalizing RNN Architecture Dense Layer for Stock Price Forecasting.srt 5.39Кб
010 Understanding Softmax Activation and Cross-Entropy Loss in Deep Learning.mp4 67.30Мб
010 Understanding Softmax Activation and Cross-Entropy Loss in Deep Learning.srt 32.01Кб
011 Step 10 - Compile RNN with Adam Optimizer for Stock Price Prediction in Python.mp4 16.56Мб
011 Step 10 - Compile RNN with Adam Optimizer for Stock Price Prediction in Python.srt 7.02Кб
011 Step 2 - Using fit_transform Method for Efficient Data Preprocessing in Python.mp4 20.27Мб
011 Step 2 - Using fit_transform Method for Efficient Data Preprocessing in Python.srt 10.10Кб
011 Step 5 - Evaluating Machine Learning Models Confusion Matrix and Accuracy.mp4 20.42Мб
011 Step 5 - Evaluating Machine Learning Models Confusion Matrix and Accuracy.srt 12.19Кб
011 Step 8 - PyTorch Techniques for Efficient Autoencoder Training on Large Datasets.mp4 51.87Мб
011 Step 8 - PyTorch Techniques for Efficient Autoencoder Training on Large Datasets.srt 32.05Кб
011 Step 8 - RBM Hidden Layer Sampling Bernoulli Distribution in PyTorch Tutorial.mp4 48.35Мб
011 Step 8 - RBM Hidden Layer Sampling Bernoulli Distribution in PyTorch Tutorial.srt 24.10Кб
012 Step 11 - Optimizing Epochs and Batch Size for LSTM Stock Price Forecasting.mp4 41.48Мб
012 Step 11 - Optimizing Epochs and Batch Size for LSTM Stock Price Forecasting.srt 14.28Кб
012 Step 3 - Preprocessing Categorical Data One-Hot and Label Encoding Techniques.mp4 16.03Мб
012 Step 3 - Preprocessing Categorical Data One-Hot and Label Encoding Techniques.srt 7.70Кб
012 Step 6a - Creating a Confusion Matrix for Machine Learning Model Evaluation.mp4 20.20Мб
012 Step 6a - Creating a Confusion Matrix for Machine Learning Model Evaluation.srt 9.91Кб
012 Step 9 - Implementing Stochastic Gradient Descent in Autoencoder Architecture.mp4 46.64Мб
012 Step 9 - Implementing Stochastic Gradient Descent in Autoencoder Architecture.srt 27.67Кб
012 Step 9 - RBM Visible Node Sampling Bernoulli Distribution in Deep Learning.mp4 23.88Мб
012 Step 9 - RBM Visible Node Sampling Bernoulli Distribution in Deep Learning.srt 10.60Кб
013 Step 10 - Machine Learning Metrics Interpreting Loss in Autoencoder Training.mp4 15.25Мб
013 Step 10 - Machine Learning Metrics Interpreting Loss in Autoencoder Training.srt 8.09Кб
013 Step 10 - RBM Training Function Updating Weights and Biases with Gibbs Sampling.mp4 44.32Мб
013 Step 10 - RBM Training Function Updating Weights and Biases with Gibbs Sampling.srt 18.91Кб
013 Step 12 - Visualizing LSTM Predictions Real vs Forecasted Google Stock Prices.mp4 21.26Мб
013 Step 12 - Visualizing LSTM Predictions Real vs Forecasted Google Stock Prices.srt 8.36Кб
013 Step 1 - Machine Learning Data Prep Splitting Dataset Before Feature Scaling.mp4 13.46Мб
013 Step 1 - Machine Learning Data Prep Splitting Dataset Before Feature Scaling.srt 6.25Кб
013 Step 6b - Visualizing Machine Learning Results Training vs Test Set Comparison.mp4 11.46Мб
013 Step 6b - Visualizing Machine Learning Results Training vs Test Set Comparison.srt 5.65Кб
014 Step 11 - How to Evaluate Recommender System Performance Using Test Set Loss.mp4 40.13Мб
014 Step 11 - How to Evaluate Recommender System Performance Using Test Set Loss.srt 20.32Кб
014 Step 11 - How to Set Up an RBM Model Choosing NV, NH, and Batch Size Parameters.mp4 27.04Мб
014 Step 11 - How to Set Up an RBM Model Choosing NV, NH, and Batch Size Parameters.srt 11.63Кб
014 Step 13 - Preparing Historical Stock Data for LSTM Model Scaling and Reshaping.mp4 64.10Мб
014 Step 13 - Preparing Historical Stock Data for LSTM Model Scaling and Reshaping.srt 25.71Кб
014 Step 2 - Split Data into Train --& Test Sets with Scikit-learn--'s train_test_split.mp4 20.58Мб
014 Step 2 - Split Data into Train --& Test Sets with Scikit-learn--'s train_test_split.srt 9.90Кб
014 Step 7a - Visualizing Logistic Regression 2D Plots for Classification Models.mp4 20.29Мб
014 Step 7a - Visualizing Logistic Regression 2D Plots for Classification Models.srt 9.17Кб
015 Step 12 - RBM Training Loop Epoch Setup and Loss Function Implementation.mp4 51.02Мб
015 Step 12 - RBM Training Loop Epoch Setup and Loss Function Implementation.srt 21.33Кб
015 Step 14 - Creating 3D Input Structure for LSTM Stock Price Prediction in Python.mp4 31.64Мб
015 Step 14 - Creating 3D Input Structure for LSTM Stock Price Prediction in Python.srt 12.69Кб
015 Step 3 - Preparing Data for ML Splitting Datasets with Python and Scikit-learn.mp4 13.33Мб
015 Step 3 - Preparing Data for ML Splitting Datasets with Python and Scikit-learn.srt 6.01Кб
015 Step 7b - Visualizing Logistic Regression Interpreting Classification Results.mp4 12.84Мб
015 Step 7b - Visualizing Logistic Regression Interpreting Classification Results.srt 6.01Кб
015 THANK YOU Video.mp4 9.18Мб
015 THANK YOU Video.srt 2.86Кб
016 Step 13 - RBM Training Updating Weights and Biases with Contrastive Divergence.mp4 73.76Мб
016 Step 13 - RBM Training Updating Weights and Biases with Contrastive Divergence.srt 29.86Кб
016 Step 15 - Visualizing LSTM Predictions Plotting Real vs Predicted Stock Prices.mp4 34.33Мб
016 Step 15 - Visualizing LSTM Predictions Plotting Real vs Predicted Stock Prices.srt 15.23Кб
016 Step 1 - How to Apply Feature Scaling for Preprocessing Machine Learning Data.mp4 20.45Мб
016 Step 1 - How to Apply Feature Scaling for Preprocessing Machine Learning Data.srt 10.06Кб
016 Step 7c - Visualizing Test Results Assessing Machine Learning Model Accuracy.mp4 11.48Мб
016 Step 7c - Visualizing Test Results Assessing Machine Learning Model Accuracy.srt 5.38Кб
017 Logistic Regression in Python - Step 7 (Colour-blind friendly image).html 2.95Кб
017 Step 14 - Optimizing RBM Models From Training to Test Set Performance Analysis.mp4 65.13Мб
017 Step 14 - Optimizing RBM Models From Training to Test Set Performance Analysis.srt 29.49Кб
017 Step 2 - Feature Scaling in Machine Learning When to Apply StandardScaler.mp4 16.34Мб
017 Step 2 - Feature Scaling in Machine Learning When to Apply StandardScaler.srt 7.90Кб
018 Evaluating the Boltzmann Machine.html 6.06Кб
018 Machine Learning Regression and Classification EXTRA.html 3.04Кб
018 Step 3 - Normalizing Data with Fit and Transform Methods in Scikit-learn.mp4 13.10Мб
018 Step 3 - Normalizing Data with Fit and Transform Methods in Scikit-learn.srt 6.30Кб
019 EXTRA CONTENT Logistic Regression Practical Case Study.html 2.85Кб
019 Step 4 - How to Apply Feature Scaling to Training --& Test Sets in ML.mp4 20.17Мб
019 Step 4 - How to Apply Feature Scaling to Training --& Test Sets in ML.srt 10.03Кб
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