Udemy-Deep-Learning-A-Z-2025

磁力链接/BT种子名称

Udemy-Deep-Learning-A-Z-2025

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文件列表

  • 07 - Building a CNN/007 Develop an Image Recognition System Using Convolutional Neural Networks.mp4 91.4 MB
  • 18 - Building a Boltzmann Machine/007 Step 4 - Convert Training --& Test Sets to RBM-Ready Arrays in Python.mp4 83.2 MB
  • 09 - RNN Intuition/005 Understanding Long Short-Term Memory --(LSTM--) Architecture for Deep Learning.mp4 78.7 MB
  • 18 - Building a Boltzmann Machine/016 Step 13 - RBM Training Updating Weights and Biases with Contrastive Divergence.mp4 77.3 MB
  • 21 - Building an AutoEncoder/007 Step 4 - Prepare Data for Autoencoder Creating User-Movie Rating Matrices.mp4 75.7 MB
  • 04 - Building an ANN/004 Step 2 - Data Preprocessing for Neural Networks Essential Steps and Techniques.mp4 72.5 MB
  • 07 - Building a CNN/004 Step 3 - Building CNN Architecture Convolutional Layers --& Max Pooling Explained.mp4 71.3 MB
  • 07 - Building a CNN/003 Step 2 - Deep Learning Preprocessing Scaling --& Transforming Images for CNNs.mp4 70.7 MB
  • 06 - CNN Intuition/010 Understanding Softmax Activation and Cross-Entropy Loss in Deep Learning.mp4 70.6 MB
  • 18 - Building a Boltzmann Machine/017 Step 14 - Optimizing RBM Models From Training to Test Set Performance Analysis.mp4 68.3 MB
  • 14 - Building a SOM/004 Step 3 - SOM Visualization Techniques Colorbar --& Markers for Outlier Detection.mp4 67.4 MB
  • 10 - Building a RNN/014 Step 13 - Preparing Historical Stock Data for LSTM Model Scaling and Reshaping.mp4 67.2 MB
  • 09 - RNN Intuition/006 How LSTMs Work in Practice Visualizing Neural Network Predictions.mp4 67.2 MB
  • 04 - Building an ANN/007 Step 5 - How to Make Predictions and Evaluate Neural Network Model in Python.mp4 64.6 MB
  • 17 - Boltzmann Machine Intuition/006 How Energy-Based Models Work Deep Dive into Contrastive Divergence Algorithm.mp4 62.1 MB
  • 21 - Building an AutoEncoder/009 Step 6 - Building Autoencoder Architecture Class Creation for Neural Networks.mp4 61.3 MB
  • 23 - Regression & Classification Intuition/005 Understanding Logistic Regression Intuition and Probability in Classification.mp4 60.8 MB
  • 10 - Building a RNN/005 Step 4 - Building X_train and y_train Arrays for LSTM Time Series Forecasting.mp4 60.6 MB
  • 07 - Building a CNN/006 Step 5 - Deploying a CNN for Real-World Image Recognition.mp4 59.4 MB
  • 03 - ANN Intuition/003 Understanding Neurons The Building Blocks of Artificial Neural Networks.mp4 59.4 MB
  • 06 - CNN Intuition/006 Understanding Spatial Invariance in CNNs Max Pooling Explained for Beginners.mp4 58.4 MB
  • 15 - Mega Case Study/004 Step 3 - Building a Hybrid Model From Unsupervised to Supervised Deep Learning.mp4 58.4 MB
  • 06 - CNN Intuition/003 How Do Convolutional Neural Networks Work Understanding CNN Architecture.mp4 57.6 MB
  • 09 - RNN Intuition/004 Understanding the Vanishing Gradient Problem in Recurrent Neural Networks --(RNNs--).mp4 57.6 MB
  • 04 - Building an ANN/005 Step 3 - Constructing an Artificial Neural Network Adding Input --& Hidden Layers.mp4 57.5 MB
  • 13 - SOMs Intuition/008 Understanding K-Means Clustering Intuitive Explanation with Visual Examples.mp4 57.3 MB
  • 17 - Boltzmann Machine Intuition/002 Boltzmann Machines vs. Neural Networks Key Differences in Deep Learning.mp4 57.1 MB
  • 13 - SOMs Intuition/004 Self-Organizing Maps Tutorial Dimensionality Reduction in Machine Learning.mp4 56.4 MB
  • 06 - CNN Intuition/008 How Do Fully Connected Layers Work in Convolutional Neural Networks --(CNNs--).mp4 55.3 MB
  • 14 - Building a SOM/002 Step 1 - Implementing Self-Organizing Maps --(SOMs--) for Fraud Detection in Python.mp4 54.5 MB
  • 21 - Building an AutoEncoder/011 Step 8 - PyTorch Techniques for Efficient Autoencoder Training on Large Datasets.mp4 54.4 MB
  • 18 - Building a Boltzmann Machine/015 Step 12 - RBM Training Loop Epoch Setup and Loss Function Implementation.mp4 53.5 MB
  • 03 - ANN Intuition/006 How Do Neural Networks Learn Understanding Backpropagation and Cost Functions.mp4 51.6 MB
  • 18 - Building a Boltzmann Machine/011 Step 8 - RBM Hidden Layer Sampling Bernoulli Distribution in PyTorch Tutorial.mp4 50.7 MB
  • 21 - Building an AutoEncoder/010 Step 7 - Python Autoencoder Tutorial Implementing Activation Functions --& Layers.mp4 50.1 MB
  • 17 - Boltzmann Machine Intuition/005 How Restricted Boltzmann Machines Work Deep Learning for Recommender Systems.mp4 49.9 MB
  • 21 - Building an AutoEncoder/012 Step 9 - Implementing Stochastic Gradient Descent in Autoencoder Architecture.mp4 48.9 MB
  • 04 - Building an ANN/006 Step 4 - Compile and Train Neural Network Optimizers, Loss Functions --& Metrics.mp4 47.6 MB
  • 14 - Building a SOM/005 Step 4 - Catching Cheaters with SOMs Mapping Winning Nodes to Customer Data.mp4 47.0 MB
  • 06 - CNN Intuition/004 How to Apply Convolution Filters in Neural Networks Feature Detection Explained.mp4 46.6 MB
  • 18 - Building a Boltzmann Machine/013 Step 10 - RBM Training Function Updating Weights and Biases with Gibbs Sampling.mp4 46.5 MB
  • 09 - RNN Intuition/003 What is a Recurrent Neural Network --(RNN--) Deep Learning for Sequential Data.mp4 45.7 MB
  • 13 - SOMs Intuition/010 How to Find the Optimal Number of Clusters in K-Means WCSS and Elbow Method.mp4 45.3 MB
  • 21 - Building an AutoEncoder/003 Step 1 - Building a Movie Recommendation System with AutoEncoders Data Import.mp4 43.6 MB
  • 10 - Building a RNN/012 Step 11 - Optimizing Epochs and Batch Size for LSTM Stock Price Forecasting.mp4 43.5 MB
  • 10 - Building a RNN/006 Step 5 - Preparing Time Series Data for LSTM Neural Network in Stock Forecasting.mp4 43.4 MB
  • 21 - Building an AutoEncoder/004 Step 2 - Preparing Training and Test Sets for Autoencoder Recommendation System.mp4 42.6 MB
  • 17 - Boltzmann Machine Intuition/003 Deep Learning Fundamentals Energy-Based Models --& Their Role in Neural Networks.mp4 42.4 MB
  • 21 - Building an AutoEncoder/014 Step 11 - How to Evaluate Recommender System Performance Using Test Set Loss.mp4 42.1 MB
  • 13 - SOMs Intuition/005 How Self-Organizing Maps --(SOMs--) Learn Unsupervised Deep Learning Explained.mp4 40.8 MB
  • 18 - Building a Boltzmann Machine/010 Step 7 - Implementing Restricted Boltzmann Machine Class Structure in PyTorch.mp4 40.8 MB
  • 18 - Building a Boltzmann Machine/005 Step 2 - Preparing Training and Test Sets for Restricted Boltzmann Machine.mp4 38.5 MB
  • 14 - Building a SOM/003 Step 2 - SOM Weight Initialization and Training Tutorial for Anomaly Detection.mp4 38.4 MB
  • 04 - Building an ANN/002 Step 1 - Data Preprocessing for Deep Learning Preparing Neural Network Dataset.mp4 38.1 MB
  • 15 - Mega Case Study/005 Step 4 - Implementing Fraud Detection with SOM A Deep Learning Approach.mp4 37.1 MB
  • 18 - Building a Boltzmann Machine/004 Step 1 - Importing Movie Datasets for RBM-Based Recommender Systems in Python.mp4 36.8 MB
  • 18 - Building a Boltzmann Machine/002 Step 0 - Building a Movie Recommender System with RBMs Data Preprocessing Guide.mp4 36.5 MB
  • 01 - Welcome to the course!/002 Introduction to Deep Learning From Historical Context to Modern Applications.mp4 36.3 MB
  • 10 - Building a RNN/016 Step 15 - Visualizing LSTM Predictions Plotting Real vs Predicted Stock Prices.mp4 36.0 MB
  • 03 - ANN Intuition/007 Mastering Gradient Descent Key to Efficient Neural Network Training.mp4 35.9 MB
  • 03 - ANN Intuition/008 How to Use Stochastic Gradient Descent for Deep Learning Optimization.mp4 34.8 MB
  • 10 - Building a RNN/008 Step 7 - Adding First LSTM Layer Key Components for Stock Market Prediction.mp4 34.7 MB
  • 13 - SOMs Intuition/002 Self-Organizing Maps --(SOM--) Unsupervised Deep Learning for Dimensionality Reduct.mp4 34.1 MB
  • 18 - Building a Boltzmann Machine/006 Step 3 - Preparing Data for RBM Calculating Total Users and Movies in Python.mp4 33.4 MB
  • 10 - Building a RNN/015 Step 14 - Creating 3D Input Structure for LSTM Stock Price Prediction in Python.mp4 33.2 MB
  • 03 - ANN Intuition/004 Understanding Activation Functions in Neural Networks Sigmoid, ReLU, and More.mp4 33.0 MB
  • 03 - ANN Intuition/005 How Do Neural Networks Work Step-by-Step Guide to Property Valuation Example.mp4 31.1 MB
  • 13 - SOMs Intuition/009 K-Means Clustering Avoiding the Random Initialization Trap in Machine Learning.mp4 31.1 MB
  • 18 - Building a Boltzmann Machine/009 Step 6 - RBM Data Preprocessing Transforming Movie Ratings for Neural Networks.mp4 30.5 MB
  • 21 - Building an AutoEncoder/005 Step 3 - Preparing Data for Recommendation Systems User --& Movie Count in Python.mp4 30.3 MB
  • 07 - Building a CNN/005 Step 4 - Train CNN for Image Classification Optimize with Keras --& TensorFlow.mp4 29.3 MB
  • 07 - Building a CNN/002 Step 1 - Convolutional Neural Networks Explained Image Classification Tutorial.mp4 29.2 MB
  • 18 - Building a Boltzmann Machine/014 Step 11 - How to Set Up an RBM Model Choosing NV, NH, and Batch Size Parameters.mp4 28.3 MB
  • 10 - Building a RNN/003 Step 2 - Importing Training Data for LSTM Stock Price Prediction Model.mp4 28.1 MB
  • 20 - AutoEncoders Intuition/002 Autoencoders in Machine Learning Applications and Architecture Overview.mp4 26.9 MB
  • 13 - SOMs Intuition/006 How to Create a Self-Organizing Map --(SOM--) in DL Step-by-Step Tutorial.mp4 26.6 MB
  • 06 - CNN Intuition/005 Rectified Linear Units --(ReLU--) in Deep Learning Optimizing CNN Performance.mp4 26.4 MB
  • 10 - Building a RNN/002 Step 1 - Building a Robust LSTM Neural Network for Stock Price Trend Prediction.mp4 25.8 MB
  • 18 - Building a Boltzmann Machine/012 Step 9 - RBM Visible Node Sampling Bernoulli Distribution in Deep Learning.mp4 25.0 MB
  • 20 - AutoEncoders Intuition/006 Sparse Autoencoders in Deep Learning Preventing Overfitting in Neural Networks.mp4 24.8 MB
  • 20 - AutoEncoders Intuition/004 How to Train an Autoencoder Step-by-Step Guide for Deep Learning Beginners.mp4 24.6 MB
  • 10 - Building a RNN/004 Step 3 - Applying Min-Max Normalization for Time Series Data in Neural Networks.mp4 23.7 MB
  • 10 - Building a RNN/013 Step 12 - Visualizing LSTM Predictions Real vs Forecasted Google Stock Prices.mp4 22.3 MB
  • 25 - Data Preprocessing in Python/014 Step 2 - Split Data into Train --& Test Sets with Scikit-learn--'s train_test_split.mp4 21.6 MB
  • 26 - Logistic Regression/009 Step 4a - Using Classifier Objects to Make Predictions in Machine Learning.mp4 21.6 MB
  • 25 - Data Preprocessing in Python/009 Step 2 - Preprocessing Datasets Fit and Transform to Handle Missing Values.mp4 21.5 MB
  • 17 - Boltzmann Machine Intuition/007 Deep Belief Networks Understanding RBM Stacking in Deep Learning Models.mp4 21.5 MB
  • 26 - Logistic Regression/006 Step 2b - Data Preprocessing Feature Scaling for Machine Learning in Python.mp4 21.5 MB
  • 25 - Data Preprocessing in Python/016 Step 1 - How to Apply Feature Scaling for Preprocessing Machine Learning Data.mp4 21.4 MB
  • 26 - Logistic Regression/011 Step 5 - Evaluating Machine Learning Models Confusion Matrix and Accuracy.mp4 21.4 MB
  • 25 - Data Preprocessing in Python/008 Step 1 - Handling Missing Data in Python SimpleImputer for Data Preprocessing.mp4 21.4 MB
  • 03 - ANN Intuition/009 Understanding Backpropagation Algorithm Key to Optimizing Deep Learning Models.mp4 21.3 MB
  • 26 - Logistic Regression/014 Step 7a - Visualizing Logistic Regression 2D Plots for Classification Models.mp4 21.3 MB
  • 25 - Data Preprocessing in Python/011 Step 2 - Using fit_transform Method for Efficient Data Preprocessing in Python.mp4 21.3 MB
  • 10 - Building a RNN/009 Step 8 - Implementing Dropout Regularization in LSTM Networks for Forecasting.mp4 21.2 MB
  • 26 - Logistic Regression/012 Step 6a - Creating a Confusion Matrix for Machine Learning Model Evaluation.mp4 21.2 MB
  • 25 - Data Preprocessing in Python/019 Step 4 - How to Apply Feature Scaling to Training --& Test Sets in ML.mp4 21.2 MB
  • 26 - Logistic Regression/005 Step 2a - Data Preprocessing for Logistic Regression Importing and Splitting.mp4 21.1 MB
  • 25 - Data Preprocessing in Python/006 Step 3 - Preprocessing Data Extracting Features and Target Variables in Python.mp4 20.8 MB
  • 26 - Logistic Regression/003 Step 1a - Machine Learning Classification Logistic Regression in Python.mp4 20.6 MB
  • 18 - Building a Boltzmann Machine/008 Step 5 - Converting NumPy Arrays to PyTorch Tensors for Deep Learning Models.mp4 20.3 MB
  • 25 - Data Preprocessing in Python/002 Step 2 - How to Handle Missing Data in Python Data Preprocessing Techniques.mp4 19.3 MB
  • 25 - Data Preprocessing in Python/001 Step 1 - Data Preprocessing in Python Essential Tools for ML Models.mp4 19.3 MB
  • 25 - Data Preprocessing in Python/004 Step 1 - Creating a DataFrame from CSV Python Data Preprocessing Basics.mp4 18.8 MB
  • 15 - Mega Case Study/003 Step 2 - Developing a Fraud Detection System Using Self-Organizing Maps.mp4 18.6 MB
  • 21 - Building an AutoEncoder/008 Step 5 - Convert Training and Test Sets to PyTorch Tensors for Deep Learning.mp4 18.4 MB
  • 13 - SOMs Intuition/007 Interpreting SOM Clusters Unsupervised Learning Techniques for Data Analysis.mp4 17.8 MB
  • 26 - Logistic Regression/001 Understanding the Logistic Regression Equation A Step-by-Step Guide.mp4 17.7 MB
  • 10 - Building a RNN/011 Step 10 - Compile RNN with Adam Optimizer for Stock Price Prediction in Python.mp4 17.4 MB
  • 24 - Data Preprocessing/004 Machine Learning Workflow Data Splitting, Feature Scaling, and Model Training.mp4 17.3 MB
  • 06 - CNN Intuition/009 CNN Building Blocks Feature Maps, ReLU, Pooling, and Fully Connected Layers.mp4 17.2 MB
  • 25 - Data Preprocessing in Python/017 Step 2 - Feature Scaling in Machine Learning When to Apply StandardScaler.mp4 17.1 MB
  • 23 - Regression & Classification Intuition/002 Simple Linear Regression Understanding Y = B0 + B1X in Machine Learning.mp4 17.1 MB
  • 25 - Data Preprocessing in Python/005 Step 2 - Pandas DataFrame Indexing Building Feature Matrix X with iloc Method.mp4 17.0 MB
  • 25 - Data Preprocessing in Python/012 Step 3 - Preprocessing Categorical Data One-Hot and Label Encoding Techniques.mp4 16.8 MB
  • 21 - Building an AutoEncoder/013 Step 10 - Machine Learning Metrics Interpreting Loss in Autoencoder Training.mp4 16.0 MB
  • 25 - Data Preprocessing in Python/010 Step 1 - Preprocessing Categorical Variables One-Hot Encoding in Python.mp4 15.9 MB
  • 20 - AutoEncoders Intuition/005 How to Use Overcomplete Hidden Layers in Autoencoders for Feature Extraction.mp4 15.5 MB
  • 09 - RNN Intuition/007 LSTM Variations Peepholes, Combined Gates, and GRUs in Deep Learning.mp4 14.4 MB
  • 26 - Logistic Regression/004 Step 1b - Logistic Regression Analysis Importing Libraries and Splitting Data.mp4 14.4 MB
  • 26 - Logistic Regression/007 Step 3a - Implementing Logistic Regression for Classification with Scikit-Learn.mp4 14.3 MB
  • 25 - Data Preprocessing in Python/013 Step 1 - Machine Learning Data Prep Splitting Dataset Before Feature Scaling.mp4 14.1 MB
  • 25 - Data Preprocessing in Python/015 Step 3 - Preparing Data for ML Splitting Datasets with Python and Scikit-learn.mp4 14.0 MB
  • 17 - Boltzmann Machine Intuition/004 How to Edit Wikipedia Adding Boltzmann Distribution in Deep Learning.mp4 14.0 MB
  • 25 - Data Preprocessing in Python/018 Step 3 - Normalizing Data with Fit and Transform Methods in Scikit-learn.mp4 13.7 MB
  • 26 - Logistic Regression/015 Step 7b - Visualizing Logistic Regression Interpreting Classification Results.mp4 13.5 MB
  • 10 - Building a RNN/010 Step 9 - Finalizing RNN Architecture Dense Layer for Stock Price Forecasting.mp4 13.3 MB
  • 25 - Data Preprocessing in Python/003 Step 1 - Importing Essential Python Libraries for Data Preprocessing --& Analysis.mp4 12.8 MB
  • 26 - Logistic Regression/008 Step 3b - Predicting Purchase Decisions with Logistic Regression in Python.mp4 12.6 MB
  • 26 - Logistic Regression/016 Step 7c - Visualizing Test Results Assessing Machine Learning Model Accuracy.mp4 12.0 MB
  • 26 - Logistic Regression/013 Step 6b - Visualizing Machine Learning Results Training vs Test Set Comparison.mp4 12.0 MB
  • 17 - Boltzmann Machine Intuition/008 Deep Boltzmann Machines vs Deep Belief Networks Key Differences Explained.mp4 11.7 MB
  • 23 - Regression & Classification Intuition/003 Linear Regression Explained Finding the Best Fitting Line for Data Analysis.mp4 11.4 MB
  • 10 - Building a RNN/007 Step 6 - Create RNN Architecture Sequential Layers vs Computational Graphs.mp4 11.3 MB
  • 15 - Mega Case Study/002 Step 1 - Building a Hybrid Deep Learning Model for Credit Card Fraud Detection.mp4 11.3 MB
  • 06 - CNN Intuition/002 Understanding CNN Architecture From Convolution to Fully Connected Layers.mp4 11.2 MB
  • 26 - Logistic Regression/002 How to Calculate Maximum Likelihood in Logistic Regression Step-by-Step Guide.mp4 10.1 MB
  • 20 - AutoEncoders Intuition/007 Denoising Autoencoders Deep Learning Regularization Technique Explained.mp4 10.1 MB
  • 13 - SOMs Intuition/001 How Do Self-Organizing Maps Work Understanding SOM in Deep Learning.mp4 10.0 MB
  • 21 - Building an AutoEncoder/015 THANK YOU Video.mp4 9.6 MB
  • 20 - AutoEncoders Intuition/008 What are Contractive Autoencoders Deep Learning Regularization Techniques.mp4 9.5 MB
  • 20 - AutoEncoders Intuition/001 Deep Learning Autoencoders Types, Architecture, and Training Explained.mp4 8.7 MB
  • 03 - ANN Intuition/002 How Neural Networks Learn Gradient Descent and Backpropagation Explained.mp4 8.5 MB
  • 20 - AutoEncoders Intuition/009 What are Stacked Autoencoders in Deep Learning Architecture and Applications.mp4 7.6 MB
  • 13 - SOMs Intuition/003 Why K-Means Clustering is Essential for Understanding Self-Organizing Maps.mp4 7.5 MB
  • 20 - AutoEncoders Intuition/010 Deep Autoencoders vs Stacked Autoencoders Key Differences in Neural Networks.mp4 7.4 MB
  • 24 - Data Preprocessing/003 Machine Learning Basics Using Train-Test Split to Evaluate Model Performance.mp4 7.4 MB
  • 09 - RNN Intuition/002 How Do Recurrent Neural Networks --(RNNs--) Work Deep Learning Explained.mp4 7.2 MB
  • 17 - Boltzmann Machine Intuition/001 Understanding Boltzmann Machines Deep Learning Fundamentals for AI Enthusiasts.mp4 6.8 MB
  • 26 - Logistic Regression/010 Step 4b - Evaluating Logistic Regression Model Predicted vs Real Outcomes.mp4 6.6 MB
  • 06 - CNN Intuition/007 How to Flatten Pooled Feature Maps in Convolutional Neural Networks --(CNNs--).mp4 6.4 MB
  • 24 - Data Preprocessing/002 How to Scale Features in Machine Learning Normalization vs Standardization.mp4 5.5 MB
  • 20 - AutoEncoders Intuition/003 Autoencoder Bias in Deep Learning Improving Neural Network Performance.mp4 5.0 MB
  • 23 - Regression & Classification Intuition/004 Multiple Linear Regression - Understanding Dependent --& Independent Variables.mp4 3.5 MB
  • 06 - CNN Intuition/008 How Do Fully Connected Layers Work in Convolutional Neural Networks --(CNNs--).srt 38.4 kB
  • 07 - Building a CNN/004 Step 3 - Building CNN Architecture Convolutional Layers --& Max Pooling Explained.srt 37.5 kB
  • 07 - Building a CNN/007 Develop an Image Recognition System Using Convolutional Neural Networks.srt 37.4 kB
  • 18 - Building a Boltzmann Machine/007 Step 4 - Convert Training --& Test Sets to RBM-Ready Arrays in Python.srt 36.0 kB
  • 21 - Building an AutoEncoder/007 Step 4 - Prepare Data for Autoencoder Creating User-Movie Rating Matrices.srt 35.9 kB
  • 09 - RNN Intuition/005 Understanding Long Short-Term Memory --(LSTM--) Architecture for Deep Learning.srt 34.4 kB
  • 21 - Building an AutoEncoder/011 Step 8 - PyTorch Techniques for Efficient Autoencoder Training on Large Datasets.srt 32.8 kB
  • 06 - CNN Intuition/010 Understanding Softmax Activation and Cross-Entropy Loss in Deep Learning.srt 32.8 kB
  • 17 - Boltzmann Machine Intuition/005 How Restricted Boltzmann Machines Work Deep Learning for Recommender Systems.srt 32.3 kB
  • 04 - Building an ANN/004 Step 2 - Data Preprocessing for Neural Networks Essential Steps and Techniques.srt 31.6 kB
  • 07 - Building a CNN/003 Step 2 - Deep Learning Preprocessing Scaling --& Transforming Images for CNNs.srt 31.3 kB
  • 15 - Mega Case Study/004 Step 3 - Building a Hybrid Model From Unsupervised to Supervised Deep Learning.srt 30.9 kB
  • 18 - Building a Boltzmann Machine/016 Step 13 - RBM Training Updating Weights and Biases with Contrastive Divergence.srt 30.6 kB
  • 03 - ANN Intuition/003 Understanding Neurons The Building Blocks of Artificial Neural Networks.srt 30.2 kB
  • 18 - Building a Boltzmann Machine/017 Step 14 - Optimizing RBM Models From Training to Test Set Performance Analysis.srt 30.2 kB
  • 07 - Building a CNN/006 Step 5 - Deploying a CNN for Real-World Image Recognition.srt 30.2 kB
  • 21 - Building an AutoEncoder/009 Step 6 - Building Autoencoder Architecture Class Creation for Neural Networks.srt 30.1 kB
  • 14 - Building a SOM/004 Step 3 - SOM Visualization Techniques Colorbar --& Markers for Outlier Detection.srt 29.9 kB
  • 17 - Boltzmann Machine Intuition/006 How Energy-Based Models Work Deep Dive into Contrastive Divergence Algorithm.srt 29.6 kB
  • 23 - Regression & Classification Intuition/005 Understanding Logistic Regression Intuition and Probability in Classification.srt 29.0 kB
  • 06 - CNN Intuition/004 How to Apply Convolution Filters in Neural Networks Feature Detection Explained.srt 28.7 kB
  • 09 - RNN Intuition/003 What is a Recurrent Neural Network --(RNN--) Deep Learning for Sequential Data.srt 28.6 kB
  • 14 - Building a SOM/002 Step 1 - Implementing Self-Organizing Maps --(SOMs--) for Fraud Detection in Python.srt 28.4 kB
  • 21 - Building an AutoEncoder/012 Step 9 - Implementing Stochastic Gradient Descent in Autoencoder Architecture.srt 28.3 kB
  • 21 - Building an AutoEncoder/010 Step 7 - Python Autoencoder Tutorial Implementing Activation Functions --& Layers.srt 28.1 kB
  • 04 - Building an ANN/007 Step 5 - How to Make Predictions and Evaluate Neural Network Model in Python.srt 27.2 kB
  • 09 - RNN Intuition/004 Understanding the Vanishing Gradient Problem in Recurrent Neural Networks --(RNNs--).srt 27.2 kB
  • 06 - CNN Intuition/003 How Do Convolutional Neural Networks Work Understanding CNN Architecture.srt 27.0 kB
  • 13 - SOMs Intuition/004 Self-Organizing Maps Tutorial Dimensionality Reduction in Machine Learning.srt 26.7 kB
  • 10 - Building a RNN/014 Step 13 - Preparing Historical Stock Data for LSTM Model Scaling and Reshaping.srt 26.3 kB
  • 06 - CNN Intuition/006 Understanding Spatial Invariance in CNNs Max Pooling Explained for Beginners.srt 25.9 kB
  • 09 - RNN Intuition/006 How LSTMs Work in Practice Visualizing Neural Network Predictions.srt 25.5 kB
  • 13 - SOMs Intuition/005 How Self-Organizing Maps --(SOMs--) Learn Unsupervised Deep Learning Explained.srt 25.4 kB
  • 04 - Building an ANN/005 Step 3 - Constructing an Artificial Neural Network Adding Input --& Hidden Layers.srt 25.1 kB
  • 13 - SOMs Intuition/008 Understanding K-Means Clustering Intuitive Explanation with Visual Examples.srt 25.0 kB
  • 17 - Boltzmann Machine Intuition/002 Boltzmann Machines vs. Neural Networks Key Differences in Deep Learning.srt 24.9 kB
  • 10 - Building a RNN/005 Step 4 - Building X_train and y_train Arrays for LSTM Time Series Forecasting.srt 24.9 kB
  • 18 - Building a Boltzmann Machine/011 Step 8 - RBM Hidden Layer Sampling Bernoulli Distribution in PyTorch Tutorial.srt 24.7 kB
  • 03 - ANN Intuition/005 How Do Neural Networks Work Step-by-Step Guide to Property Valuation Example.srt 23.5 kB
  • 14 - Building a SOM/005 Step 4 - Catching Cheaters with SOMs Mapping Winning Nodes to Customer Data.srt 22.3 kB
  • 03 - ANN Intuition/006 How Do Neural Networks Learn Understanding Backpropagation and Cost Functions.srt 22.3 kB
  • 18 - Building a Boltzmann Machine/015 Step 12 - RBM Training Loop Epoch Setup and Loss Function Implementation.srt 21.8 kB
  • 01 - Welcome to the course!/002 Introduction to Deep Learning From Historical Context to Modern Applications.srt 21.8 kB
  • 21 - Building an AutoEncoder/003 Step 1 - Building a Movie Recommendation System with AutoEncoders Data Import.srt 21.4 kB
  • 13 - SOMs Intuition/010 How to Find the Optimal Number of Clusters in K-Means WCSS and Elbow Method.srt 20.9 kB
  • 21 - Building an AutoEncoder/014 Step 11 - How to Evaluate Recommender System Performance Using Test Set Loss.srt 20.8 kB
  • 04 - Building an ANN/006 Step 4 - Compile and Train Neural Network Optimizers, Loss Functions --& Metrics.srt 20.8 kB
  • 21 - Building an AutoEncoder/004 Step 2 - Preparing Training and Test Sets for Autoencoder Recommendation System.srt 20.5 kB
  • 10 - Building a RNN/006 Step 5 - Preparing Time Series Data for LSTM Neural Network in Stock Forecasting.srt 20.4 kB
  • 20 - AutoEncoders Intuition/002 Autoencoders in Machine Learning Applications and Architecture Overview.srt 19.9 kB
  • 18 - Building a Boltzmann Machine/013 Step 10 - RBM Training Function Updating Weights and Biases with Gibbs Sampling.srt 19.4 kB
  • 15 - Mega Case Study/005 Step 4 - Implementing Fraud Detection with SOM A Deep Learning Approach.srt 19.4 kB
  • 04 - Building an ANN/002 Step 1 - Data Preprocessing for Deep Learning Preparing Neural Network Dataset.srt 19.2 kB
  • 17 - Boltzmann Machine Intuition/003 Deep Learning Fundamentals Energy-Based Models --& Their Role in Neural Networks.srt 18.3 kB
  • 18 - Building a Boltzmann Machine/010 Step 7 - Implementing Restricted Boltzmann Machine Class Structure in PyTorch.srt 18.1 kB
  • 03 - ANN Intuition/007 Mastering Gradient Descent Key to Efficient Neural Network Training.srt 18.0 kB
  • 18 - Building a Boltzmann Machine/002 Step 0 - Building a Movie Recommender System with RBMs Data Preprocessing Guide.srt 17.1 kB
  • 18 - Building a Boltzmann Machine/006 Step 3 - Preparing Data for RBM Calculating Total Users and Movies in Python.srt 16.8 kB
  • 21 - Building an AutoEncoder/005 Step 3 - Preparing Data for Recommendation Systems User --& Movie Count in Python.srt 16.8 kB
  • 13 - SOMs Intuition/006 How to Create a Self-Organizing Map --(SOM--) in DL Step-by-Step Tutorial.srt 16.8 kB
  • 18 - Building a Boltzmann Machine/005 Step 2 - Preparing Training and Test Sets for Restricted Boltzmann Machine.srt 16.6 kB
  • 14 - Building a SOM/003 Step 2 - SOM Weight Initialization and Training Tutorial for Anomaly Detection.srt 16.4 kB
  • 18 - Building a Boltzmann Machine/004 Step 1 - Importing Movie Datasets for RBM-Based Recommender Systems in Python.srt 16.3 kB
  • 10 - Building a RNN/016 Step 15 - Visualizing LSTM Predictions Plotting Real vs Predicted Stock Prices.srt 15.6 kB
  • 03 - ANN Intuition/008 How to Use Stochastic Gradient Descent for Deep Learning Optimization.srt 15.2 kB
  • 13 - SOMs Intuition/002 Self-Organizing Maps --(SOM--) Unsupervised Deep Learning for Dimensionality Reduct.srt 15.2 kB
  • 10 - Building a RNN/012 Step 11 - Optimizing Epochs and Batch Size for LSTM Stock Price Forecasting.srt 14.6 kB
  • 03 - ANN Intuition/004 Understanding Activation Functions in Neural Networks Sigmoid, ReLU, and More.srt 14.5 kB
  • 10 - Building a RNN/008 Step 7 - Adding First LSTM Layer Key Components for Stock Market Prediction.srt 14.5 kB
  • 13 - SOMs Intuition/009 K-Means Clustering Avoiding the Random Initialization Trap in Machine Learning.srt 14.4 kB
  • 07 - Building a CNN/002 Step 1 - Convolutional Neural Networks Explained Image Classification Tutorial.srt 13.9 kB
  • 18 - Building a Boltzmann Machine/009 Step 6 - RBM Data Preprocessing Transforming Movie Ratings for Neural Networks.srt 13.3 kB
  • 10 - Building a RNN/002 Step 1 - Building a Robust LSTM Neural Network for Stock Price Trend Prediction.srt 13.1 kB
  • 10 - Building a RNN/015 Step 14 - Creating 3D Input Structure for LSTM Stock Price Prediction in Python.srt 13.0 kB
  • 07 - Building a CNN/005 Step 4 - Train CNN for Image Classification Optimize with Keras --& TensorFlow.srt 12.6 kB
  • 26 - Logistic Regression/011 Step 5 - Evaluating Machine Learning Models Confusion Matrix and Accuracy.srt 12.5 kB
  • 18 - Building a Boltzmann Machine/014 Step 11 - How to Set Up an RBM Model Choosing NV, NH, and Batch Size Parameters.srt 11.9 kB
  • 25 - Data Preprocessing in Python/002 Step 2 - How to Handle Missing Data in Python Data Preprocessing Techniques.srt 11.7 kB
  • 10 - Building a RNN/003 Step 2 - Importing Training Data for LSTM Stock Price Prediction Model.srt 11.5 kB
  • 20 - AutoEncoders Intuition/004 How to Train an Autoencoder Step-by-Step Guide for Deep Learning Beginners.srt 11.5 kB
  • 06 - CNN Intuition/005 Rectified Linear Units --(ReLU--) in Deep Learning Optimizing CNN Performance.srt 11.2 kB
  • 24 - Data Preprocessing/004 Machine Learning Workflow Data Splitting, Feature Scaling, and Model Training.srt 11.0 kB
  • 18 - Building a Boltzmann Machine/012 Step 9 - RBM Visible Node Sampling Bernoulli Distribution in Deep Learning.srt 10.9 kB
  • 20 - AutoEncoders Intuition/006 Sparse Autoencoders in Deep Learning Preventing Overfitting in Neural Networks.srt 10.4 kB
  • 25 - Data Preprocessing in Python/011 Step 2 - Using fit_transform Method for Efficient Data Preprocessing in Python.srt 10.3 kB
  • 26 - Logistic Regression/006 Step 2b - Data Preprocessing Feature Scaling for Machine Learning in Python.srt 10.3 kB
  • 25 - Data Preprocessing in Python/016 Step 1 - How to Apply Feature Scaling for Preprocessing Machine Learning Data.srt 10.3 kB
  • 25 - Data Preprocessing in Python/019 Step 4 - How to Apply Feature Scaling to Training --& Test Sets in ML.srt 10.3 kB
  • 25 - Data Preprocessing in Python/006 Step 3 - Preprocessing Data Extracting Features and Target Variables in Python.srt 10.3 kB
  • 26 - Logistic Regression/005 Step 2a - Data Preprocessing for Logistic Regression Importing and Splitting.srt 10.2 kB
  • 26 - Logistic Regression/012 Step 6a - Creating a Confusion Matrix for Machine Learning Model Evaluation.srt 10.2 kB
  • 25 - Data Preprocessing in Python/014 Step 2 - Split Data into Train --& Test Sets with Scikit-learn--'s train_test_split.srt 10.1 kB
  • 25 - Data Preprocessing in Python/008 Step 1 - Handling Missing Data in Python SimpleImputer for Data Preprocessing.srt 10.0 kB
  • 23 - Regression & Classification Intuition/002 Simple Linear Regression Understanding Y = B0 + B1X in Machine Learning.srt 9.8 kB
  • 25 - Data Preprocessing in Python/009 Step 2 - Preprocessing Datasets Fit and Transform to Handle Missing Values.srt 9.7 kB
  • 10 - Building a RNN/004 Step 3 - Applying Min-Max Normalization for Time Series Data in Neural Networks.srt 9.7 kB
  • 26 - Logistic Regression/009 Step 4a - Using Classifier Objects to Make Predictions in Machine Learning.srt 9.4 kB
  • 26 - Logistic Regression/014 Step 7a - Visualizing Logistic Regression 2D Plots for Classification Models.srt 9.4 kB
  • 26 - Logistic Regression/003 Step 1a - Machine Learning Classification Logistic Regression in Python.srt 9.3 kB
  • 25 - Data Preprocessing in Python/001 Step 1 - Data Preprocessing in Python Essential Tools for ML Models.srt 9.2 kB
  • 21 - Building an AutoEncoder/008 Step 5 - Convert Training and Test Sets to PyTorch Tensors for Deep Learning.srt 9.1 kB
  • 18 - Building a Boltzmann Machine/008 Step 5 - Converting NumPy Arrays to PyTorch Tensors for Deep Learning Models.srt 9.1 kB
  • 25 - Data Preprocessing in Python/004 Step 1 - Creating a DataFrame from CSV Python Data Preprocessing Basics.srt 9.0 kB
  • 17 - Boltzmann Machine Intuition/007 Deep Belief Networks Understanding RBM Stacking in Deep Learning Models.srt 9.0 kB
  • 03 - ANN Intuition/009 Understanding Backpropagation Algorithm Key to Optimizing Deep Learning Models.srt 8.8 kB
  • 10 - Building a RNN/009 Step 8 - Implementing Dropout Regularization in LSTM Networks for Forecasting.srt 8.7 kB
  • 10 - Building a RNN/013 Step 12 - Visualizing LSTM Predictions Real vs Forecasted Google Stock Prices.srt 8.6 kB
  • 26 - Logistic Regression/001 Understanding the Logistic Regression Equation A Step-by-Step Guide.srt 8.3 kB
  • 21 - Building an AutoEncoder/013 Step 10 - Machine Learning Metrics Interpreting Loss in Autoencoder Training.srt 8.3 kB
  • 25 - Data Preprocessing in Python/005 Step 2 - Pandas DataFrame Indexing Building Feature Matrix X with iloc Method.srt 8.2 kB
  • 25 - Data Preprocessing in Python/017 Step 2 - Feature Scaling in Machine Learning When to Apply StandardScaler.srt 8.1 kB
  • 25 - Data Preprocessing in Python/012 Step 3 - Preprocessing Categorical Data One-Hot and Label Encoding Techniques.srt 7.9 kB
  • 13 - SOMs Intuition/007 Interpreting SOM Clusters Unsupervised Learning Techniques for Data Analysis.srt 7.8 kB
  • 15 - Mega Case Study/003 Step 2 - Developing a Fraud Detection System Using Self-Organizing Maps.srt 7.6 kB
  • 26 - Logistic Regression/004 Step 1b - Logistic Regression Analysis Importing Libraries and Splitting Data.srt 7.2 kB
  • 25 - Data Preprocessing in Python/010 Step 1 - Preprocessing Categorical Variables One-Hot Encoding in Python.srt 7.2 kB
  • 10 - Building a RNN/011 Step 10 - Compile RNN with Adam Optimizer for Stock Price Prediction in Python.srt 7.2 kB
  • 27 - Congratulations!! Don't forget your Prize )/001 Huge Congrats for completing the challenge!.html 7.2 kB
  • 06 - CNN Intuition/009 CNN Building Blocks Feature Maps, ReLU, Pooling, and Fully Connected Layers.srt 7.0 kB
  • 26 - Logistic Regression/007 Step 3a - Implementing Logistic Regression for Classification with Scikit-Learn.srt 6.8 kB
  • 20 - AutoEncoders Intuition/005 How to Use Overcomplete Hidden Layers in Autoencoders for Feature Extraction.srt 6.7 kB
  • 17 - Boltzmann Machine Intuition/004 How to Edit Wikipedia Adding Boltzmann Distribution in Deep Learning.srt 6.6 kB
  • 25 - Data Preprocessing in Python/018 Step 3 - Normalizing Data with Fit and Transform Methods in Scikit-learn.srt 6.4 kB
  • 25 - Data Preprocessing in Python/013 Step 1 - Machine Learning Data Prep Splitting Dataset Before Feature Scaling.srt 6.4 kB
  • 25 - Data Preprocessing in Python/003 Step 1 - Importing Essential Python Libraries for Data Preprocessing --& Analysis.srt 6.4 kB
  • 18 - Building a Boltzmann Machine/018 Evaluating the Boltzmann Machine.html 6.2 kB
  • 26 - Logistic Regression/002 How to Calculate Maximum Likelihood in Logistic Regression Step-by-Step Guide.srt 6.2 kB
  • 06 - CNN Intuition/002 Understanding CNN Architecture From Convolution to Fully Connected Layers.srt 6.2 kB
  • 26 - Logistic Regression/015 Step 7b - Visualizing Logistic Regression Interpreting Classification Results.srt 6.2 kB
  • 25 - Data Preprocessing in Python/015 Step 3 - Preparing Data for ML Splitting Datasets with Python and Scikit-learn.srt 6.2 kB
  • 09 - RNN Intuition/007 LSTM Variations Peepholes, Combined Gates, and GRUs in Deep Learning.srt 6.0 kB
  • 01 - Welcome to the course!/001 Welcome Challenge!.html 5.9 kB
  • 26 - Logistic Regression/013 Step 6b - Visualizing Machine Learning Results Training vs Test Set Comparison.srt 5.8 kB
  • 26 - Logistic Regression/008 Step 3b - Predicting Purchase Decisions with Logistic Regression in Python.srt 5.7 kB
  • 10 - Building a RNN/010 Step 9 - Finalizing RNN Architecture Dense Layer for Stock Price Forecasting.srt 5.5 kB
  • 26 - Logistic Regression/016 Step 7c - Visualizing Test Results Assessing Machine Learning Model Accuracy.srt 5.5 kB
  • 13 - SOMs Intuition/001 How Do Self-Organizing Maps Work Understanding SOM in Deep Learning.srt 5.4 kB
  • 15 - Mega Case Study/002 Step 1 - Building a Hybrid Deep Learning Model for Credit Card Fraud Detection.srt 5.3 kB
  • 23 - Regression & Classification Intuition/003 Linear Regression Explained Finding the Best Fitting Line for Data Analysis.srt 5.1 kB
  • 17 - Boltzmann Machine Intuition/008 Deep Boltzmann Machines vs Deep Belief Networks Key Differences Explained.srt 5.1 kB
  • 18 - Building a Boltzmann Machine/001 Get the code and dataset ready.html 4.9 kB
  • 21 - Building an AutoEncoder/001 Get the code and dataset ready.html 4.9 kB
  • 10 - Building a RNN/007 Step 6 - Create RNN Architecture Sequential Layers vs Computational Graphs.srt 4.8 kB
  • 17 - Boltzmann Machine Intuition/001 Understanding Boltzmann Machines Deep Learning Fundamentals for AI Enthusiasts.srt 4.7 kB
  • 03 - ANN Intuition/002 How Neural Networks Learn Gradient Descent and Backpropagation Explained.srt 4.6 kB
  • 10 - Building a RNN/001 Get the code and dataset ready.html 4.6 kB
  • 15 - Mega Case Study/001 Get the code and dataset ready.html 4.5 kB
  • 14 - Building a SOM/001 Get the code and dataset ready.html 4.5 kB
  • 20 - AutoEncoders Intuition/007 Denoising Autoencoders Deep Learning Regularization Technique Explained.srt 4.4 kB
  • 04 - Building an ANN/001 Get the code and dataset ready.html 4.1 kB
  • 27 - Congratulations!! Don't forget your Prize )/002 Bonus How To UNLOCK Top Salaries (Live Training).html 4.1 kB
  • 09 - RNN Intuition/002 How Do Recurrent Neural Networks --(RNNs--) Work Deep Learning Explained.srt 4.1 kB
  • 11 - Evaluating and Improving the RNN/001 Evaluating the RNN.html 4.1 kB
  • 20 - AutoEncoders Intuition/008 What are Contractive Autoencoders Deep Learning Regularization Techniques.srt 4.0 kB
  • 13 - SOMs Intuition/003 Why K-Means Clustering is Essential for Understanding Self-Organizing Maps.srt 4.0 kB
  • 21 - Building an AutoEncoder/006 Homework Challenge - Coding Exercise.html 3.9 kB
  • 07 - Building a CNN/001 Get the code and dataset ready.html 3.9 kB
  • 20 - AutoEncoders Intuition/001 Deep Learning Autoencoders Types, Architecture, and Training Explained.srt 3.9 kB
  • 25 - Data Preprocessing in Python/007 For Python learners, summary of Object-oriented programming classes & objects.html 3.8 kB
  • 16 - ------------------------- Part 5 - Boltzmann Machines -------------------------/001 Welcome to Part 5 - Boltzmann Machines.html 3.7 kB
  • 11 - Evaluating and Improving the RNN/002 Improving the RNN.html 3.6 kB
  • 24 - Data Preprocessing/003 Machine Learning Basics Using Train-Test Split to Evaluate Model Performance.srt 3.4 kB
  • 01 - Welcome to the course!/004 EXTRA Use ChatGPT to Boost your Deep Learning Skills.html 3.3 kB
  • 06 - CNN Intuition/007 How to Flatten Pooled Feature Maps in Convolutional Neural Networks --(CNNs--).srt 3.3 kB
  • 19 - ---------------------------- Part 6 - AutoEncoders ----------------------------/001 Welcome to Part 6 - AutoEncoders.html 3.2 kB
  • 22 - ------------------- Annex - Get the Machine Learning Basics -------------------/001 Annex - Get the Machine Learning Basics.html 3.2 kB
  • 26 - Logistic Regression/010 Step 4b - Evaluating Logistic Regression Model Predicted vs Real Outcomes.srt 3.2 kB
  • 26 - Logistic Regression/018 Machine Learning Regression and Classification EXTRA.html 3.1 kB
  • 20 - AutoEncoders Intuition/010 Deep Autoencoders vs Stacked Autoencoders Key Differences in Neural Networks.srt 3.1 kB
  • 26 - Logistic Regression/017 Logistic Regression in Python - Step 7 (Colour-blind friendly image).html 3.0 kB
  • 21 - Building an AutoEncoder/015 THANK YOU Video.srt 2.9 kB
  • 26 - Logistic Regression/019 EXTRA CONTENT Logistic Regression Practical Case Study.html 2.9 kB
  • 20 - AutoEncoders Intuition/009 What are Stacked Autoencoders in Deep Learning Architecture and Applications.srt 2.9 kB
  • 04 - Building an ANN/003 Check out our free course on ANN for Regression.html 2.8 kB
  • 01 - Welcome to the course!/003 Get the codes, datasets and slides here.html 2.8 kB
  • 24 - Data Preprocessing/002 How to Scale Features in Machine Learning Normalization vs Standardization.srt 2.8 kB
  • 08 - ---------------------- Part 3 - Recurrent Neural Networks ----------------------/001 Welcome to Part 3 - Recurrent Neural Networks.html 2.8 kB
  • 24 - Data Preprocessing/001 Data Preprocessing.html 2.8 kB
  • 18 - Building a Boltzmann Machine/003 Same Data Preprocessing in Parts 5 and 6.html 2.7 kB
  • 12 - ------------------------ Part 4 - Self Organizing Maps ------------------------/001 Welcome to Part 4 - Self Organizing Maps.html 2.7 kB
  • 21 - Building an AutoEncoder/002 Same Data Preprocessing in Parts 5 and 6.html 2.6 kB
  • 23 - Regression & Classification Intuition/001 What You Need for Regression & Classification.html 2.6 kB
  • 06 - CNN Intuition/001 What You'll Need for CNN.html 2.6 kB
  • 03 - ANN Intuition/001 What You'll Need for ANN.html 2.6 kB
  • 09 - RNN Intuition/001 What You'll Need for RNN.html 2.6 kB
  • 02 - --------------------- Part 1 - Artificial Neural Networks ---------------------/001 Welcome to Part 1 - Artificial Neural Networks.html 2.6 kB
  • 05 - -------------------- Part 2 - Convolutional Neural Networks --------------------/001 Welcome to Part 2 - Convolutional Neural Networks.html 2.6 kB
  • 20 - AutoEncoders Intuition/003 Autoencoder Bias in Deep Learning Improving Neural Network Performance.srt 2.4 kB
  • 23 - Regression & Classification Intuition/004 Multiple Linear Regression - Understanding Dependent --& Independent Variables.srt 1.7 kB

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