[FreeCourseSite.com] Udemy - Deep Learning A-Z 2025 Neural Networks, AI & ChatGPT Prize

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[FreeCourseSite.com] Udemy - Deep Learning A-Z 2025 Neural Networks, AI & ChatGPT Prize

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文件列表

  • 06. CNN Intuition/8. How Do Fully Connected Layers Work in Convolutional Neural Networks (CNNs).mp4 192.2 MB
  • 07. Building a CNN/7. Develop an Image Recognition System Using Convolutional Neural Networks.mp4 161.6 MB
  • 09. RNN Intuition/6. How LSTMs Work in Practice Visualizing Neural Network Predictions.mp4 159.4 MB
  • 18. Building a Boltzmann Machine/16. Step 13 - RBM Training Updating Weights and Biases with Contrastive Divergence.mp4 151.1 MB
  • 17. Boltzmann Machine Intuition/5. How Restricted Boltzmann Machines Work Deep Learning for Recommender Systems.mp4 149.4 MB
  • 13. SOMs Intuition/8. Understanding K-Means Clustering Intuitive Explanation with Visual Examples.mp4 132.4 MB
  • 10. Building a RNN/14. Step 13 - Preparing Historical Stock Data for LSTM Model Scaling and Reshaping.mp4 132.0 MB
  • 07. Building a CNN/3. Step 2 - Deep Learning Preprocessing Scaling & Transforming Images for CNNs.mp4 115.2 MB
  • 09. RNN Intuition/3. What is a Recurrent Neural Network (RNN) Deep Learning for Sequential Data.mp4 113.5 MB
  • 21. Building an AutoEncoder/7. Step 4 - Prepare Data for Autoencoder Creating User-Movie Rating Matrices.mp4 109.1 MB
  • 10. Building a RNN/5. Step 4 - Building X_train and y_train Arrays for LSTM Time Series Forecasting.mp4 108.8 MB
  • 18. Building a Boltzmann Machine/7. Step 4 - Convert Training & Test Sets to RBM-Ready Arrays in Python.mp4 108.3 MB
  • 18. Building a Boltzmann Machine/13. Step 10 - RBM Training Function Updating Weights and Biases with Gibbs Sampling.mp4 104.1 MB
  • 21. Building an AutoEncoder/3. Step 1 - Building a Movie Recommendation System with AutoEncoders Data Import.mp4 103.9 MB
  • 13. SOMs Intuition/5. How Self-Organizing Maps (SOMs) Learn Unsupervised Deep Learning Explained.mp4 103.8 MB
  • 06. CNN Intuition/6. Understanding Spatial Invariance in CNNs Max Pooling Explained for Beginners.mp4 94.5 MB
  • 17. Boltzmann Machine Intuition/2. Boltzmann Machines vs. Neural Networks Key Differences in Deep Learning.mp4 91.6 MB
  • 06. CNN Intuition/4. How to Apply Convolution Filters in Neural Networks Feature Detection Explained.mp4 89.6 MB
  • 20. AutoEncoders Intuition/2. Autoencoders in Machine Learning Applications and Architecture Overview.mp4 88.3 MB
  • 10. Building a RNN/6. Step 5 - Preparing Time Series Data for LSTM Neural Network in Stock Forecasting.mp4 84.3 MB
  • 04. Building an ANN/7. Step 5 - How to Make Predictions and Evaluate Neural Network Model in Python.mp4 84.0 MB
  • 18. Building a Boltzmann Machine/4. Step 1 - Importing Movie Datasets for RBM-Based Recommender Systems in Python.mp4 83.9 MB
  • 07. Building a CNN/6. Step 5 - Deploying a CNN for Real-World Image Recognition.mp4 80.5 MB
  • 15. Mega Case Study/4. Step 3 - Building a Hybrid Model From Unsupervised to Supervised Deep Learning.mp4 79.7 MB
  • 18. Building a Boltzmann Machine/17. Step 14 - Optimizing RBM Models From Training to Test Set Performance Analysis.mp4 78.6 MB
  • 06. CNN Intuition/3. How Do Convolutional Neural Networks Work Understanding CNN Architecture.mp4 77.9 MB
  • 21. Building an AutoEncoder/9. Step 6 - Building Autoencoder Architecture Class Creation for Neural Networks.mp4 76.1 MB
  • 21. Building an AutoEncoder/11. Step 8 - PyTorch Techniques for Efficient Autoencoder Training on Large Datasets.mp4 75.6 MB
  • 03. ANN Intuition/5. How Do Neural Networks Work Step-by-Step Guide to Property Valuation Example.mp4 74.7 MB
  • 17. Boltzmann Machine Intuition/6. How Energy-Based Models Work Deep Dive into Contrastive Divergence Algorithm.mp4 72.3 MB
  • 07. Building a CNN/4. Step 3 - Building CNN Architecture Convolutional Layers & Max Pooling Explained.mp4 70.1 MB
  • 09. RNN Intuition/5. Understanding Long Short-Term Memory (LSTM) Architecture for Deep Learning.mp4 70.1 MB
  • 04. Building an ANN/4. Step 2 - Data Preprocessing for Neural Networks Essential Steps and Techniques.mp4 68.5 MB
  • 10. Building a RNN/15. Step 14 - Creating 3D Input Structure for LSTM Stock Price Prediction in Python.mp4 67.2 MB
  • 18. Building a Boltzmann Machine/9. Step 6 - RBM Data Preprocessing Transforming Movie Ratings for Neural Networks.mp4 65.6 MB
  • 21. Building an AutoEncoder/10. Step 7 - Python Autoencoder Tutorial Implementing Activation Functions & Layers.mp4 63.0 MB
  • 10. Building a RNN/16. Step 15 - Visualizing LSTM Predictions Plotting Real vs Predicted Stock Prices.mp4 62.4 MB
  • 04. Building an ANN/2. Step 1 - Data Preprocessing for Deep Learning Preparing Neural Network Dataset.mp4 61.6 MB
  • 14. Building a SOM/3. Step 2 - SOM Weight Initialization and Training Tutorial for Anomaly Detection.mp4 61.1 MB
  • 14. Building a SOM/4. Step 3 - SOM Visualization Techniques Colorbar & Markers for Outlier Detection.mp4 60.7 MB
  • 18. Building a Boltzmann Machine/15. Step 12 - RBM Training Loop Epoch Setup and Loss Function Implementation.mp4 60.5 MB
  • 13. SOMs Intuition/6. How to Create a Self-Organizing Map (SOM) in DL Step-by-Step Tutorial.mp4 60.4 MB
  • 14. Building a SOM/5. Step 4 - Catching Cheaters with SOMs Mapping Winning Nodes to Customer Data.mp4 59.4 MB
  • 18. Building a Boltzmann Machine/11. Step 8 - RBM Hidden Layer Sampling Bernoulli Distribution in PyTorch Tutorial.mp4 56.6 MB
  • 10. Building a RNN/13. Step 12 - Visualizing LSTM Predictions Real vs Forecasted Google Stock Prices.mp4 56.4 MB
  • 21. Building an AutoEncoder/15. THANK YOU Video.mp4 56.2 MB
  • 06. CNN Intuition/10. Understanding Softmax Activation and Cross-Entropy Loss in Deep Learning.mp4 55.9 MB
  • 03. ANN Intuition/6. How Do Neural Networks Learn Understanding Backpropagation and Cost Functions.mp4 55.4 MB
  • 21. Building an AutoEncoder/14. Step 11 - How to Evaluate Recommender System Performance Using Test Set Loss.mp4 52.6 MB
  • 14. Building a SOM/2. Step 1 - Implementing Self-Organizing Maps (SOMs) for Fraud Detection in Python.mp4 51.4 MB
  • 21. Building an AutoEncoder/4. Step 2 - Preparing Training and Test Sets for Autoencoder Recommendation System.mp4 51.1 MB
  • 03. ANN Intuition/7. Mastering Gradient Descent Key to Efficient Neural Network Training.mp4 50.5 MB
  • 09. RNN Intuition/4. Understanding the Vanishing Gradient Problem in Recurrent Neural Networks (RNNs).mp4 49.4 MB
  • 10. Building a RNN/12. Step 11 - Optimizing Epochs and Batch Size for LSTM Stock Price Forecasting.mp4 48.3 MB
  • 04. Building an ANN/5. Step 3 - Constructing an Artificial Neural Network Adding Input & Hidden Layers.mp4 48.1 MB
  • 03. ANN Intuition/3. Understanding Neurons The Building Blocks of Artificial Neural Networks.mp4 47.4 MB
  • 21. Building an AutoEncoder/12. Step 9 - Implementing Stochastic Gradient Descent in Autoencoder Architecture.mp4 46.4 MB
  • 18. Building a Boltzmann Machine/10. Step 7 - Implementing Restricted Boltzmann Machine Class Structure in PyTorch.mp4 45.0 MB
  • 15. Mega Case Study/5. Step 4 - Implementing Fraud Detection with SOM A Deep Learning Approach.mp4 43.5 MB
  • 17. Boltzmann Machine Intuition/4. How to Edit Wikipedia Adding Boltzmann Distribution in Deep Learning.mp4 43.4 MB
  • 18. Building a Boltzmann Machine/5. Step 2 - Preparing Training and Test Sets for Restricted Boltzmann Machine.mp4 41.7 MB
  • 13. SOMs Intuition/7. Interpreting SOM Clusters Unsupervised Learning Techniques for Data Analysis.mp4 40.7 MB
  • 10. Building a RNN/3. Step 2 - Importing Training Data for LSTM Stock Price Prediction Model.mp4 40.6 MB
  • 10. Building a RNN/8. Step 7 - Adding First LSTM Layer Key Components for Stock Market Prediction.mp4 40.3 MB
  • 13. SOMs Intuition/2. Self-Organizing Maps (SOM) Unsupervised Deep Learning for Dimensionality Reduct.mp4 40.0 MB
  • 25. Data Preprocessing in Python/2. Step 2 - How to Handle Missing Data in Python Data Preprocessing Techniques.mp4 36.8 MB
  • 10. Building a RNN/4. Step 3 - Applying Min-Max Normalization for Time Series Data in Neural Networks.mp4 35.5 MB
  • 04. Building an ANN/6. Step 4 - Compile and Train Neural Network Optimizers, Loss Functions & Metrics.mp4 34.7 MB
  • 18. Building a Boltzmann Machine/2. Step 0 - Building a Movie Recommender System with RBMs Data Preprocessing Guide.mp4 34.6 MB
  • 26. Logistic Regression/6. Step 2b - Data Preprocessing Feature Scaling for Machine Learning in Python.mp4 34.4 MB
  • 15. Mega Case Study/3. Step 2 - Developing a Fraud Detection System Using Self-Organizing Maps.mp4 34.1 MB
  • 23. Regression & Classification Intuition/5. Understanding Logistic Regression Intuition and Probability in Classification.mp4 34.0 MB
  • 10. Building a RNN/2. Step 1 - Building a Robust LSTM Neural Network for Stock Price Trend Prediction.mp4 34.0 MB
  • 18. Building a Boltzmann Machine/14. Step 11 - How to Set Up an RBM Model Choosing NV, NH, and Batch Size Parameters.mp4 32.3 MB
  • 01. Welcome to the course!/1. Introduction to Deep Learning From Historical Context to Modern Applications.mp4 31.0 MB
  • 25. Data Preprocessing in Python/9. Step 2 - Preprocessing Datasets Fit and Transform to Handle Missing Values.mp4 30.8 MB
  • 26. Logistic Regression/5. Step 2a - Data Preprocessing for Logistic Regression Importing and Splitting.mp4 30.5 MB
  • 18. Building a Boltzmann Machine/12. Step 9 - RBM Visible Node Sampling Bernoulli Distribution in Deep Learning.mp4 29.7 MB
  • 13. SOMs Intuition/4. Self-Organizing Maps Tutorial Dimensionality Reduction in Machine Learning.mp4 29.0 MB
  • 03. ANN Intuition/8. How to Use Stochastic Gradient Descent for Deep Learning Optimization.mp4 29.0 MB
  • 21. Building an AutoEncoder/5. Step 3 - Preparing Data for Recommendation Systems User & Movie Count in Python.mp4 27.6 MB
  • 18. Building a Boltzmann Machine/6. Step 3 - Preparing Data for RBM Calculating Total Users and Movies in Python.mp4 27.6 MB
  • 13. SOMs Intuition/10. How to Find the Optimal Number of Clusters in K-Means WCSS and Elbow Method.mp4 27.3 MB
  • 07. Building a CNN/2. Step 1 - Convolutional Neural Networks Explained Image Classification Tutorial.mp4 27.2 MB
  • 26. Logistic Regression/15. Step 7b - Visualizing Logistic Regression Interpreting Classification Results.mp4 25.2 MB
  • 10. Building a RNN/9. Step 8 - Implementing Dropout Regularization in LSTM Networks for Forecasting.mp4 25.0 MB
  • 07. Building a CNN/5. Step 4 - Train CNN for Image Classification Optimize with Keras & TensorFlow.mp4 24.9 MB
  • 21. Building an AutoEncoder/8. Step 5 - Convert Training and Test Sets to PyTorch Tensors for Deep Learning.mp4 22.7 MB
  • 18. Building a Boltzmann Machine/8. Step 5 - Converting NumPy Arrays to PyTorch Tensors for Deep Learning Models.mp4 22.6 MB
  • 06. CNN Intuition/5. Rectified Linear Units (ReLU) in Deep Learning Optimizing CNN Performance.mp4 22.2 MB
  • 26. Logistic Regression/9. Step 4a - Using Classifier Objects to Make Predictions in Machine Learning.mp4 21.8 MB
  • 26. Logistic Regression/12. Step 6a - Creating a Confusion Matrix for Machine Learning Model Evaluation.mp4 21.7 MB
  • 10. Building a RNN/11. Step 10 - Compile RNN with Adam Optimizer for Stock Price Prediction in Python.mp4 21.5 MB
  • 26. Logistic Regression/14. Step 7a - Visualizing Logistic Regression 2D Plots for Classification Models.mp4 21.5 MB
  • 26. Logistic Regression/16. Step 7c - Visualizing Test Results Assessing Machine Learning Model Accuracy.mp4 21.1 MB
  • 17. Boltzmann Machine Intuition/3. Deep Learning Fundamentals Energy-Based Models & Their Role in Neural Networks.mp4 21.1 MB
  • 25. Data Preprocessing in Python/11. Step 2 - Using fit_transform Method for Efficient Data Preprocessing in Python.mp4 20.7 MB
  • 17. Boltzmann Machine Intuition/7. Deep Belief Networks Understanding RBM Stacking in Deep Learning Models.mp4 20.3 MB
  • 21. Building an AutoEncoder/13. Step 10 - Machine Learning Metrics Interpreting Loss in Autoencoder Training.mp4 19.9 MB
  • 26. Logistic Regression/11. Step 5 - Evaluating Machine Learning Models Confusion Matrix and Accuracy.mp4 19.1 MB
  • 03. ANN Intuition/9. Understanding Backpropagation Algorithm Key to Optimizing Deep Learning Models.mp4 18.9 MB
  • 03. ANN Intuition/4. Understanding Activation Functions in Neural Networks Sigmoid, ReLU, and More.mp4 18.7 MB
  • 20. AutoEncoders Intuition/6. Sparse Autoencoders in Deep Learning Preventing Overfitting in Neural Networks.mp4 18.7 MB
  • 25. Data Preprocessing in Python/19. Step 4 - How to Apply Feature Scaling to Training & Test Sets in ML.mp4 17.7 MB
  • 25. Data Preprocessing in Python/8. Step 1 - Handling Missing Data in Python SimpleImputer for Data Preprocessing.mp4 16.9 MB
  • 20. AutoEncoders Intuition/4. How to Train an Autoencoder Step-by-Step Guide for Deep Learning Beginners.mp4 16.8 MB
  • 13. SOMs Intuition/9. K-Means Clustering Avoiding the Random Initialization Trap in Machine Learning.mp4 16.4 MB
  • 10. Building a RNN/10. Step 9 - Finalizing RNN Architecture Dense Layer for Stock Price Forecasting.mp4 15.6 MB
  • 25. Data Preprocessing in Python/17. Step 2 - Feature Scaling in Machine Learning When to Apply StandardScaler.mp4 14.9 MB
  • 25. Data Preprocessing in Python/12. Step 3 - Preprocessing Categorical Data One-Hot and Label Encoding Techniques.mp4 14.8 MB
  • 24. Data Preprocessing/4. Machine Learning Workflow Data Splitting, Feature Scaling, and Model Training.mp4 14.7 MB
  • 26. Logistic Regression/7. Step 3a - Implementing Logistic Regression for Classification with Scikit-Learn.mp4 14.4 MB
  • 25. Data Preprocessing in Python/14. Step 2 - Split Data into Train & Test Sets with Scikit-learn's train_test_split.mp4 14.3 MB
  • 25. Data Preprocessing in Python/16. Step 1 - How to Apply Feature Scaling for Preprocessing Machine Learning Data.mp4 13.7 MB
  • 25. Data Preprocessing in Python/4. Step 1 - Creating a DataFrame from CSV Python Data Preprocessing Basics.mp4 13.1 MB
  • 10. Building a RNN/7. Step 6 - Create RNN Architecture Sequential Layers vs Computational Graphs.mp4 12.9 MB
  • 26. Logistic Regression/13. Step 6b - Visualizing Machine Learning Results Training vs Test Set Comparison.mp4 12.7 MB
  • 26. Logistic Regression/3. Step 1a - Machine Learning Classification Logistic Regression in Python.mp4 12.5 MB
  • 25. Data Preprocessing in Python/10. Step 1 - Preprocessing Categorical Variables One-Hot Encoding in Python.mp4 12.3 MB
  • 25. Data Preprocessing in Python/15. Step 3 - Preparing Data for ML Splitting Datasets with Python and Scikit-learn.mp4 12.2 MB
  • 25. Data Preprocessing in Python/6. Step 3 - Preprocessing Data Extracting Features and Target Variables in Python.mp4 12.1 MB
  • 26. Logistic Regression/1. Understanding the Logistic Regression Equation A Step-by-Step Guide.mp4 11.9 MB
  • 25. Data Preprocessing in Python/18. Step 3 - Normalizing Data with Fit and Transform Methods in Scikit-learn.mp4 11.8 MB
  • 06. CNN Intuition/9. CNN Building Blocks Feature Maps, ReLU, Pooling, and Fully Connected Layers.mp4 11.7 MB
  • 25. Data Preprocessing in Python/1. Step 1 - Data Preprocessing in Python Essential Tools for ML Models.mp4 11.3 MB
  • 25. Data Preprocessing in Python/13. Step 1 - Machine Learning Data Prep Splitting Dataset Before Feature Scaling.mp4 10.8 MB
  • 25. Data Preprocessing in Python/5. Step 2 - Pandas DataFrame Indexing Building Feature Matrix X with iloc Method.mp4 10.3 MB
  • 23. Regression & Classification Intuition/2. Simple Linear Regression Understanding Y = B0 + B1X in Machine Learning.mp4 10.2 MB
  • 20. AutoEncoders Intuition/5. How to Use Overcomplete Hidden Layers in Autoencoders for Feature Extraction.mp4 9.8 MB
  • 26. Logistic Regression/4. Step 1b - Logistic Regression Analysis Importing Libraries and Splitting Data.mp4 9.7 MB
  • 09. RNN Intuition/7. LSTM Variations Peepholes, Combined Gates, and GRUs in Deep Learning.mp4 9.2 MB
  • 20. AutoEncoders Intuition/10. Deep Autoencoders vs Stacked Autoencoders Key Differences in Neural Networks.mp4 8.2 MB
  • 26. Logistic Regression/8. Step 3b - Predicting Purchase Decisions with Logistic Regression in Python.mp4 8.1 MB
  • 25. Data Preprocessing in Python/3. Step 1 - Importing Essential Python Libraries for Data Preprocessing & Analysis.mp4 8.1 MB
  • 20. AutoEncoders Intuition/7. Denoising Autoencoders Deep Learning Regularization Technique Explained.mp4 7.8 MB
  • 26. Logistic Regression/2. How to Calculate Maximum Likelihood in Logistic Regression Step-by-Step Guide.mp4 7.5 MB
  • 17. Boltzmann Machine Intuition/8. Deep Boltzmann Machines vs Deep Belief Networks Key Differences Explained.mp4 7.1 MB
  • 20. AutoEncoders Intuition/9. What are Stacked Autoencoders in Deep Learning Architecture and Applications.mp4 7.0 MB
  • 20. AutoEncoders Intuition/8. What are Contractive Autoencoders Deep Learning Regularization Techniques.mp4 6.9 MB
  • 06. CNN Intuition/2. Understanding CNN Architecture From Convolution to Fully Connected Layers.mp4 6.8 MB
  • 23. Regression & Classification Intuition/3. Linear Regression Explained Finding the Best Fitting Line for Data Analysis.mp4 6.6 MB
  • 15. Mega Case Study/2. Step 1 - Building a Hybrid Deep Learning Model for Credit Card Fraud Detection.mp4 6.5 MB
  • 13. SOMs Intuition/1. How Do Self-Organizing Maps Work Understanding SOM in Deep Learning.mp4 6.3 MB
  • 24. Data Preprocessing/3. Machine Learning Basics Using Train-Test Split to Evaluate Model Performance.mp4 5.6 MB
  • 13. SOMs Intuition/3. Why K-Means Clustering is Essential for Understanding Self-Organizing Maps.mp4 5.4 MB
  • 20. AutoEncoders Intuition/1. Deep Learning Autoencoders Types, Architecture, and Training Explained.mp4 4.8 MB
  • 26. Logistic Regression/10. Step 4b - Evaluating Logistic Regression Model Predicted vs Real Outcomes.mp4 4.7 MB
  • 09. RNN Intuition/2. How Do Recurrent Neural Networks (RNNs) Work Deep Learning Explained.mp4 4.6 MB
  • 17. Boltzmann Machine Intuition/1. Understanding Boltzmann Machines Deep Learning Fundamentals for AI Enthusiasts.mp4 4.3 MB
  • 24. Data Preprocessing/2. How to Scale Features in Machine Learning Normalization vs Standardization.mp4 3.8 MB
  • 06. CNN Intuition/7. How to Flatten Pooled Feature Maps in Convolutional Neural Networks (CNNs).mp4 3.4 MB
  • 20. AutoEncoders Intuition/3. Autoencoder Bias in Deep Learning Improving Neural Network Performance.mp4 3.3 MB
  • 03. ANN Intuition/2. How Neural Networks Learn Gradient Descent and Backpropagation Explained.mp4 2.5 MB
  • 23. Regression & Classification Intuition/4. Multiple Linear Regression - Understanding Dependent & Independent Variables.mp4 2.1 MB
  • 27. Congratulations!! Don't forget your Prize )/2. 2025-01-13_07-39-00-d4903ef226a918fe48f699f14bb6e25e.png 190.3 kB
  • 18. Building a Boltzmann Machine/7. Step 4 - Convert Training & Test Sets to RBM-Ready Arrays in Python.vtt 39.1 kB
  • 21. Building an AutoEncoder/7. Step 4 - Prepare Data for Autoencoder Creating User-Movie Rating Matrices.vtt 37.9 kB
  • 09. RNN Intuition/5. Understanding Long Short-Term Memory (LSTM) Architecture for Deep Learning.vtt 36.2 kB
  • 06. CNN Intuition/8. How Do Fully Connected Layers Work in Convolutional Neural Networks (CNNs).vtt 35.7 kB
  • 17. Boltzmann Machine Intuition/5. How Restricted Boltzmann Machines Work Deep Learning for Recommender Systems.vtt 34.9 kB
  • 18. Building a Boltzmann Machine/16. Step 13 - RBM Training Updating Weights and Biases with Contrastive Divergence.vtt 33.1 kB
  • 03. ANN Intuition/3. Understanding Neurons The Building Blocks of Artificial Neural Networks.vtt 32.9 kB
  • 14. Building a SOM/4. Step 3 - SOM Visualization Techniques Colorbar & Markers for Outlier Detection.vtt 32.5 kB
  • 21. Building an AutoEncoder/9. Step 6 - Building Autoencoder Architecture Class Creation for Neural Networks.vtt 32.1 kB
  • 06. CNN Intuition/10. Understanding Softmax Activation and Cross-Entropy Loss in Deep Learning.vtt 32.0 kB
  • 18. Building a Boltzmann Machine/17. Step 14 - Optimizing RBM Models From Training to Test Set Performance Analysis.vtt 31.6 kB
  • 07. Building a CNN/7. Develop an Image Recognition System Using Convolutional Neural Networks.vtt 30.9 kB
  • 23. Regression & Classification Intuition/5. Understanding Logistic Regression Intuition and Probability in Classification.vtt 30.1 kB
  • 06. CNN Intuition/4. How to Apply Convolution Filters in Neural Networks Feature Detection Explained.vtt 29.8 kB
  • 09. RNN Intuition/3. What is a Recurrent Neural Network (RNN) Deep Learning for Sequential Data.vtt 29.3 kB
  • 17. Boltzmann Machine Intuition/6. How Energy-Based Models Work Deep Dive into Contrastive Divergence Algorithm.vtt 28.9 kB
  • 21. Building an AutoEncoder/11. Step 8 - PyTorch Techniques for Efficient Autoencoder Training on Large Datasets.vtt 28.7 kB
  • 06. CNN Intuition/3. How Do Convolutional Neural Networks Work Understanding CNN Architecture.vtt 28.7 kB
  • 07. Building a CNN/3. Step 2 - Deep Learning Preprocessing Scaling & Transforming Images for CNNs.vtt 28.7 kB
  • 13. SOMs Intuition/4. Self-Organizing Maps Tutorial Dimensionality Reduction in Machine Learning.vtt 28.6 kB
  • 10. Building a RNN/14. Step 13 - Preparing Historical Stock Data for LSTM Model Scaling and Reshaping.vtt 28.6 kB
  • 17. Boltzmann Machine Intuition/2. Boltzmann Machines vs. Neural Networks Key Differences in Deep Learning.vtt 27.9 kB
  • 09. RNN Intuition/4. Understanding the Vanishing Gradient Problem in Recurrent Neural Networks (RNNs).vtt 27.4 kB
  • 13. SOMs Intuition/5. How Self-Organizing Maps (SOMs) Learn Unsupervised Deep Learning Explained.vtt 27.0 kB
  • 07. Building a CNN/4. Step 3 - Building CNN Architecture Convolutional Layers & Max Pooling Explained.vtt 26.9 kB
  • 13. SOMs Intuition/8. Understanding K-Means Clustering Intuitive Explanation with Visual Examples.vtt 26.4 kB
  • 06. CNN Intuition/6. Understanding Spatial Invariance in CNNs Max Pooling Explained for Beginners.vtt 26.4 kB
  • 09. RNN Intuition/6. How LSTMs Work in Practice Visualizing Neural Network Predictions.vtt 25.9 kB
  • 15. Mega Case Study/4. Step 3 - Building a Hybrid Model From Unsupervised to Supervised Deep Learning.vtt 25.7 kB
  • 04. Building an ANN/4. Step 2 - Data Preprocessing for Neural Networks Essential Steps and Techniques.vtt 25.6 kB
  • 21. Building an AutoEncoder/10. Step 7 - Python Autoencoder Tutorial Implementing Activation Functions & Layers.vtt 25.6 kB
  • 03. ANN Intuition/6. How Do Neural Networks Learn Understanding Backpropagation and Cost Functions.vtt 25.2 kB
  • 21. Building an AutoEncoder/12. Step 9 - Implementing Stochastic Gradient Descent in Autoencoder Architecture.vtt 25.1 kB
  • 04. Building an ANN/7. Step 5 - How to Make Predictions and Evaluate Neural Network Model in Python.vtt 24.7 kB
  • 14. Building a SOM/2. Step 1 - Implementing Self-Organizing Maps (SOMs) for Fraud Detection in Python.vtt 24.3 kB
  • 10. Building a RNN/5. Step 4 - Building X_train and y_train Arrays for LSTM Time Series Forecasting.vtt 23.5 kB
  • 13. SOMs Intuition/10. How to Find the Optimal Number of Clusters in K-Means WCSS and Elbow Method.vtt 23.1 kB
  • 18. Building a Boltzmann Machine/11. Step 8 - RBM Hidden Layer Sampling Bernoulli Distribution in PyTorch Tutorial.vtt 22.9 kB
  • 18. Building a Boltzmann Machine/15. Step 12 - RBM Training Loop Epoch Setup and Loss Function Implementation.vtt 22.8 kB
  • 04. Building an ANN/5. Step 3 - Constructing an Artificial Neural Network Adding Input & Hidden Layers.vtt 22.6 kB
  • 21. Building an AutoEncoder/4. Step 2 - Preparing Training and Test Sets for Autoencoder Recommendation System.vtt 22.2 kB
  • 03. ANN Intuition/5. How Do Neural Networks Work Step-by-Step Guide to Property Valuation Example.vtt 22.2 kB
  • 21. Building an AutoEncoder/14. Step 11 - How to Evaluate Recommender System Performance Using Test Set Loss.vtt 22.1 kB
  • 07. Building a CNN/6. Step 5 - Deploying a CNN for Real-World Image Recognition.vtt 21.6 kB
  • 21. Building an AutoEncoder/3. Step 1 - Building a Movie Recommendation System with AutoEncoders Data Import.vtt 21.4 kB
  • 18. Building a Boltzmann Machine/13. Step 10 - RBM Training Function Updating Weights and Biases with Gibbs Sampling.vtt 20.9 kB
  • 01. Welcome to the course!/1. Introduction to Deep Learning From Historical Context to Modern Applications.vtt 20.2 kB
  • 17. Boltzmann Machine Intuition/3. Deep Learning Fundamentals Energy-Based Models & Their Role in Neural Networks.vtt 20.2 kB
  • 20. AutoEncoders Intuition/2. Autoencoders in Machine Learning Applications and Architecture Overview.vtt 19.9 kB
  • 14. Building a SOM/5. Step 4 - Catching Cheaters with SOMs Mapping Winning Nodes to Customer Data.vtt 19.8 kB
  • 15. Mega Case Study/5. Step 4 - Implementing Fraud Detection with SOM A Deep Learning Approach.vtt 19.8 kB
  • 18. Building a Boltzmann Machine/10. Step 7 - Implementing Restricted Boltzmann Machine Class Structure in PyTorch.vtt 19.3 kB
  • 18. Building a Boltzmann Machine/2. Step 0 - Building a Movie Recommender System with RBMs Data Preprocessing Guide.vtt 19.0 kB
  • 04. Building an ANN/6. Step 4 - Compile and Train Neural Network Optimizers, Loss Functions & Metrics.vtt 18.5 kB
  • 10. Building a RNN/6. Step 5 - Preparing Time Series Data for LSTM Neural Network in Stock Forecasting.vtt 18.2 kB
  • 14. Building a SOM/3. Step 2 - SOM Weight Initialization and Training Tutorial for Anomaly Detection.vtt 17.7 kB
  • 03. ANN Intuition/7. Mastering Gradient Descent Key to Efficient Neural Network Training.vtt 17.7 kB
  • 10. Building a RNN/12. Step 11 - Optimizing Epochs and Batch Size for LSTM Stock Price Forecasting.vtt 17.1 kB
  • 13. SOMs Intuition/6. How to Create a Self-Organizing Map (SOM) in DL Step-by-Step Tutorial.vtt 17.1 kB
  • 18. Building a Boltzmann Machine/4. Step 1 - Importing Movie Datasets for RBM-Based Recommender Systems in Python.vtt 17.1 kB
  • 18. Building a Boltzmann Machine/5. Step 2 - Preparing Training and Test Sets for Restricted Boltzmann Machine.vtt 17.1 kB
  • 13. SOMs Intuition/2. Self-Organizing Maps (SOM) Unsupervised Deep Learning for Dimensionality Reduct.vtt 16.9 kB
  • 10. Building a RNN/16. Step 15 - Visualizing LSTM Predictions Plotting Real vs Predicted Stock Prices.vtt 16.4 kB
  • 03. ANN Intuition/8. How to Use Stochastic Gradient Descent for Deep Learning Optimization.vtt 16.2 kB
  • 13. SOMs Intuition/9. K-Means Clustering Avoiding the Random Initialization Trap in Machine Learning.vtt 15.8 kB
  • 03. ANN Intuition/4. Understanding Activation Functions in Neural Networks Sigmoid, ReLU, and More.vtt 15.6 kB
  • 18. Building a Boltzmann Machine/6. Step 3 - Preparing Data for RBM Calculating Total Users and Movies in Python.vtt 15.2 kB
  • 21. Building an AutoEncoder/5. Step 3 - Preparing Data for Recommendation Systems User & Movie Count in Python.vtt 15.0 kB
  • 18. Building a Boltzmann Machine/9. Step 6 - RBM Data Preprocessing Transforming Movie Ratings for Neural Networks.vtt 14.8 kB
  • 10. Building a RNN/8. Step 7 - Adding First LSTM Layer Key Components for Stock Market Prediction.vtt 14.8 kB
  • 04. Building an ANN/2. Step 1 - Data Preprocessing for Deep Learning Preparing Neural Network Dataset.vtt 13.6 kB
  • 10. Building a RNN/15. Step 14 - Creating 3D Input Structure for LSTM Stock Price Prediction in Python.vtt 13.2 kB
  • 26. Logistic Regression/17. 2023-10-27_09-33-51-79d68c341b6d6d2ca73c27f9e2697b29.png 13.0 kB
  • 18. Building a Boltzmann Machine/12. Step 9 - RBM Visible Node Sampling Bernoulli Distribution in Deep Learning.vtt 12.5 kB
  • 20. AutoEncoders Intuition/4. How to Train an Autoencoder Step-by-Step Guide for Deep Learning Beginners.vtt 12.4 kB
  • 18. Building a Boltzmann Machine/14. Step 11 - How to Set Up an RBM Model Choosing NV, NH, and Batch Size Parameters.vtt 11.9 kB
  • 10. Building a RNN/3. Step 2 - Importing Training Data for LSTM Stock Price Prediction Model.vtt 11.8 kB
  • 06. CNN Intuition/5. Rectified Linear Units (ReLU) in Deep Learning Optimizing CNN Performance.vtt 11.6 kB
  • 20. AutoEncoders Intuition/6. Sparse Autoencoders in Deep Learning Preventing Overfitting in Neural Networks.vtt 11.4 kB
  • 10. Building a RNN/2. Step 1 - Building a Robust LSTM Neural Network for Stock Price Trend Prediction.vtt 11.2 kB
  • 07. Building a CNN/5. Step 4 - Train CNN for Image Classification Optimize with Keras & TensorFlow.vtt 10.7 kB
  • 10. Building a RNN/9. Step 8 - Implementing Dropout Regularization in LSTM Networks for Forecasting.vtt 10.5 kB
  • 23. Regression & Classification Intuition/2. Simple Linear Regression Understanding Y = B0 + B1X in Machine Learning.vtt 10.5 kB
  • 24. Data Preprocessing/4. Machine Learning Workflow Data Splitting, Feature Scaling, and Model Training.vtt 10.2 kB
  • 07. Building a CNN/2. Step 1 - Convolutional Neural Networks Explained Image Classification Tutorial.vtt 10.0 kB
  • 25. Data Preprocessing in Python/16. Step 1 - How to Apply Feature Scaling for Preprocessing Machine Learning Data.vtt 9.9 kB
  • 17. Boltzmann Machine Intuition/7. Deep Belief Networks Understanding RBM Stacking in Deep Learning Models.vtt 9.8 kB
  • 10. Building a RNN/4. Step 3 - Applying Min-Max Normalization for Time Series Data in Neural Networks.vtt 9.8 kB
  • 26. Logistic Regression/17. 2023-10-27_09-33-51-7c6b56531ac00d135d9ae1b931c61936.png 9.8 kB
  • 26. Logistic Regression/12. Step 6a - Creating a Confusion Matrix for Machine Learning Model Evaluation.vtt 9.8 kB
  • 25. Data Preprocessing in Python/19. Step 4 - How to Apply Feature Scaling to Training & Test Sets in ML.vtt 9.7 kB
  • 18. Building a Boltzmann Machine/8. Step 5 - Converting NumPy Arrays to PyTorch Tensors for Deep Learning Models.vtt 9.7 kB
  • 25. Data Preprocessing in Python/14. Step 2 - Split Data into Train & Test Sets with Scikit-learn's train_test_split.vtt 9.4 kB
  • 25. Data Preprocessing in Python/8. Step 1 - Handling Missing Data in Python SimpleImputer for Data Preprocessing.vtt 9.3 kB
  • 03. ANN Intuition/9. Understanding Backpropagation Algorithm Key to Optimizing Deep Learning Models.vtt 9.3 kB
  • 21. Building an AutoEncoder/8. Step 5 - Convert Training and Test Sets to PyTorch Tensors for Deep Learning.vtt 9.3 kB
  • 25. Data Preprocessing in Python/11. Step 2 - Using fit_transform Method for Efficient Data Preprocessing in Python.vtt 9.3 kB
  • 25. Data Preprocessing in Python/6. Step 3 - Preprocessing Data Extracting Features and Target Variables in Python.vtt 9.1 kB
  • 26. Logistic Regression/6. Step 2b - Data Preprocessing Feature Scaling for Machine Learning in Python.vtt 9.1 kB
  • 26. Logistic Regression/11. Step 5 - Evaluating Machine Learning Models Confusion Matrix and Accuracy.vtt 9.0 kB
  • 26. Logistic Regression/9. Step 4a - Using Classifier Objects to Make Predictions in Machine Learning.vtt 9.0 kB
  • 25. Data Preprocessing in Python/9. Step 2 - Preprocessing Datasets Fit and Transform to Handle Missing Values.vtt 8.9 kB
  • 10. Building a RNN/13. Step 12 - Visualizing LSTM Predictions Real vs Forecasted Google Stock Prices.vtt 8.8 kB
  • 25. Data Preprocessing in Python/2. Step 2 - How to Handle Missing Data in Python Data Preprocessing Techniques.vtt 8.8 kB
  • 26. Logistic Regression/5. Step 2a - Data Preprocessing for Logistic Regression Importing and Splitting.vtt 8.8 kB
  • 26. Logistic Regression/14. Step 7a - Visualizing Logistic Regression 2D Plots for Classification Models.vtt 8.7 kB
  • 21. Building an AutoEncoder/13. Step 10 - Machine Learning Metrics Interpreting Loss in Autoencoder Training.vtt 8.7 kB
  • 10. Building a RNN/11. Step 10 - Compile RNN with Adam Optimizer for Stock Price Prediction in Python.vtt 8.6 kB
  • 25. Data Preprocessing in Python/1. Step 1 - Data Preprocessing in Python Essential Tools for ML Models.vtt 8.6 kB
  • 13. SOMs Intuition/7. Interpreting SOM Clusters Unsupervised Learning Techniques for Data Analysis.vtt 8.5 kB
  • 26. Logistic Regression/3. Step 1a - Machine Learning Classification Logistic Regression in Python.vtt 8.3 kB
  • 15. Mega Case Study/3. Step 2 - Developing a Fraud Detection System Using Self-Organizing Maps.vtt 8.2 kB
  • 25. Data Preprocessing in Python/4. Step 1 - Creating a DataFrame from CSV Python Data Preprocessing Basics.vtt 7.9 kB
  • 06. CNN Intuition/9. CNN Building Blocks Feature Maps, ReLU, Pooling, and Fully Connected Layers.vtt 7.9 kB
  • 26. Logistic Regression/1. Understanding the Logistic Regression Equation A Step-by-Step Guide.vtt 7.6 kB
  • 25. Data Preprocessing in Python/17. Step 2 - Feature Scaling in Machine Learning When to Apply StandardScaler.vtt 7.6 kB
  • 20. AutoEncoders Intuition/5. How to Use Overcomplete Hidden Layers in Autoencoders for Feature Extraction.vtt 7.5 kB
  • 25. Data Preprocessing in Python/5. Step 2 - Pandas DataFrame Indexing Building Feature Matrix X with iloc Method.vtt 7.4 kB
  • 06. CNN Intuition/2. Understanding CNN Architecture From Convolution to Fully Connected Layers.vtt 6.9 kB
  • 25. Data Preprocessing in Python/12. Step 3 - Preprocessing Categorical Data One-Hot and Label Encoding Techniques.vtt 6.8 kB
  • 26. Logistic Regression/4. Step 1b - Logistic Regression Analysis Importing Libraries and Splitting Data.vtt 6.5 kB
  • 09. RNN Intuition/7. LSTM Variations Peepholes, Combined Gates, and GRUs in Deep Learning.vtt 6.3 kB
  • 25. Data Preprocessing in Python/10. Step 1 - Preprocessing Categorical Variables One-Hot Encoding in Python.vtt 6.3 kB
  • 17. Boltzmann Machine Intuition/4. How to Edit Wikipedia Adding Boltzmann Distribution in Deep Learning.vtt 6.2 kB
  • 15. Mega Case Study/2. Step 1 - Building a Hybrid Deep Learning Model for Credit Card Fraud Detection.vtt 6.2 kB
  • 26. Logistic Regression/7. Step 3a - Implementing Logistic Regression for Classification with Scikit-Learn.vtt 6.2 kB
  • 25. Data Preprocessing in Python/13. Step 1 - Machine Learning Data Prep Splitting Dataset Before Feature Scaling.vtt 6.1 kB
  • 10. Building a RNN/10. Step 9 - Finalizing RNN Architecture Dense Layer for Stock Price Forecasting.vtt 6.1 kB
  • 25. Data Preprocessing in Python/18. Step 3 - Normalizing Data with Fit and Transform Methods in Scikit-learn.vtt 6.0 kB
  • 17. Boltzmann Machine Intuition/8. Deep Boltzmann Machines vs Deep Belief Networks Key Differences Explained.vtt 5.9 kB
  • 25. Data Preprocessing in Python/3. Step 1 - Importing Essential Python Libraries for Data Preprocessing & Analysis.vtt 5.8 kB
  • 26. Logistic Regression/2. How to Calculate Maximum Likelihood in Logistic Regression Step-by-Step Guide.vtt 5.8 kB
  • 10. Building a RNN/7. Step 6 - Create RNN Architecture Sequential Layers vs Computational Graphs.vtt 5.7 kB
  • 26. Logistic Regression/15. Step 7b - Visualizing Logistic Regression Interpreting Classification Results.vtt 5.6 kB
  • 25. Data Preprocessing in Python/15. Step 3 - Preparing Data for ML Splitting Datasets with Python and Scikit-learn.vtt 5.6 kB
  • 23. Regression & Classification Intuition/3. Linear Regression Explained Finding the Best Fitting Line for Data Analysis.vtt 5.6 kB
  • 26. Logistic Regression/8. Step 3b - Predicting Purchase Decisions with Logistic Regression in Python.vtt 5.3 kB
  • 03. ANN Intuition/2. How Neural Networks Learn Gradient Descent and Backpropagation Explained.vtt 5.2 kB
  • 26. Logistic Regression/16. Step 7c - Visualizing Test Results Assessing Machine Learning Model Accuracy.vtt 5.1 kB
  • 26. Logistic Regression/13. Step 6b - Visualizing Machine Learning Results Training vs Test Set Comparison.vtt 5.1 kB
  • 17. Boltzmann Machine Intuition/1. Understanding Boltzmann Machines Deep Learning Fundamentals for AI Enthusiasts.vtt 4.9 kB
  • 20. AutoEncoders Intuition/7. Denoising Autoencoders Deep Learning Regularization Technique Explained.vtt 4.9 kB
  • 20. AutoEncoders Intuition/8. What are Contractive Autoencoders Deep Learning Regularization Techniques.vtt 4.6 kB
  • 09. RNN Intuition/2. How Do Recurrent Neural Networks (RNNs) Work Deep Learning Explained.vtt 4.5 kB
  • 13. SOMs Intuition/3. Why K-Means Clustering is Essential for Understanding Self-Organizing Maps.vtt 4.5 kB
  • 20. AutoEncoders Intuition/1. Deep Learning Autoencoders Types, Architecture, and Training Explained.vtt 4.4 kB
  • 18. Building a Boltzmann Machine/18. Evaluating the Boltzmann Machine.html 3.9 kB
  • 20. AutoEncoders Intuition/10. Deep Autoencoders vs Stacked Autoencoders Key Differences in Neural Networks.vtt 3.6 kB
  • 06. CNN Intuition/7. How to Flatten Pooled Feature Maps in Convolutional Neural Networks (CNNs).vtt 3.5 kB
  • 20. AutoEncoders Intuition/9. What are Stacked Autoencoders in Deep Learning Architecture and Applications.vtt 3.1 kB
  • 24. Data Preprocessing/3. Machine Learning Basics Using Train-Test Split to Evaluate Model Performance.vtt 3.1 kB
  • 26. Logistic Regression/10. Step 4b - Evaluating Logistic Regression Model Predicted vs Real Outcomes.vtt 3.0 kB
  • 21. Building an AutoEncoder/15. THANK YOU Video.vtt 2.8 kB
  • 18. Building a Boltzmann Machine/1. Get the code and dataset ready.html 2.7 kB
  • 21. Building an AutoEncoder/1. Get the code and dataset ready.html 2.7 kB
  • 24. Data Preprocessing/2. How to Scale Features in Machine Learning Normalization vs Standardization.vtt 2.6 kB
  • 20. AutoEncoders Intuition/3. Autoencoder Bias in Deep Learning Improving Neural Network Performance.vtt 2.5 kB
  • 15. Mega Case Study/1. Get the code and dataset ready.html 2.3 kB
  • 23. Regression & Classification Intuition/4. Multiple Linear Regression - Understanding Dependent & Independent Variables.vtt 2.1 kB
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  • 21. Building an AutoEncoder/6. Homework Challenge - Coding Exercise.html 1.6 kB
  • 25. Data Preprocessing in Python/7. For Python learners, summary of Object-oriented programming classes & objects.html 1.5 kB
  • 16. ------------------------- Part 5 - Boltzmann Machines -------------------------/1. Welcome to Part 5 - Boltzmann Machines.html 1.5 kB
  • 19. ---------------------------- Part 6 - AutoEncoders ----------------------------/1. Welcome to Part 6 - AutoEncoders.html 1.0 kB
  • 22. ------------------- Annex - Get the Machine Learning Basics -------------------/1. Annex - Get the Machine Learning Basics.html 938 Bytes
  • 26. Logistic Regression/18. Machine Learning Regression and Classification EXTRA.html 858 Bytes
  • 26. Logistic Regression/17. Logistic Regression in Python - Step 7 (Colour-blind friendly image).html 765 Bytes
  • 26. Logistic Regression/19. EXTRA CONTENT Logistic Regression Practical Case Study.html 657 Bytes
  • 04. Building an ANN/3. Check out our free course on ANN for Regression.html 600 Bytes
  • 24. Data Preprocessing/1. Data Preprocessing.html 565 Bytes
  • 18. Building a Boltzmann Machine/3. Same Data Preprocessing in Parts 5 and 6.html 419 Bytes
  • 12. ------------------------ Part 4 - Self Organizing Maps ------------------------/1. Welcome to Part 4 - Self Organizing Maps.html 418 Bytes
  • 06. CNN Intuition/1. What You'll Need for CNN.html 414 Bytes
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  • 21. Building an AutoEncoder/2. Same Data Preprocessing in Parts 5 and 6.html 410 Bytes
  • 09. RNN Intuition/1. What You'll Need for RNN.html 405 Bytes
  • 23. Regression & Classification Intuition/1. What You Need for Regression & Classification.html 387 Bytes
  • 01. Welcome to the course!/3. Prizes $$ for Learning.html 383 Bytes
  • 01. Welcome to the course!/2. Get the Codes, Datasets and Slides Here.html 350 Bytes
  • 02. --------------------- Part 1 - Artificial Neural Networks ---------------------/1. Welcome to Part 1 - Artificial Neural Networks.html 348 Bytes
  • 05. -------------------- Part 2 - Convolutional Neural Networks --------------------/1. Welcome to Part 2 - Convolutional Neural Networks.html 319 Bytes
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  • 02. --------------------- Part 1 - Artificial Neural Networks ---------------------/0. Websites you may like/[FreeCourseSite.com].url 127 Bytes
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  • 03. ANN Intuition/0. Websites you may like/[FreeCourseSite.com].url 127 Bytes
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  • 05. -------------------- Part 2 - Convolutional Neural Networks --------------------/0. Websites you may like/[FreeCourseSite.com].url 127 Bytes
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  • 06. CNN Intuition/0. Websites you may like/[FreeCourseSite.com].url 127 Bytes
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  • 07. Building a CNN/0. Websites you may like/[FreeCourseSite.com].url 127 Bytes
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  • 08. ---------------------- Part 3 - Recurrent Neural Networks ----------------------/0. Websites you may like/[FreeCourseSite.com].url 127 Bytes
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  • 09. RNN Intuition/0. Websites you may like/[FreeCourseSite.com].url 127 Bytes
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  • 10. Building a RNN/0. Websites you may like/[FreeCourseSite.com].url 127 Bytes
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  • 11. Evaluating and Improving the RNN/0. Websites you may like/[FreeCourseSite.com].url 127 Bytes
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  • 12. ------------------------ Part 4 - Self Organizing Maps ------------------------/0. Websites you may like/[FreeCourseSite.com].url 127 Bytes
  • 12. ------------------------ Part 4 - Self Organizing Maps ------------------------/[FreeCourseSite.com].url 127 Bytes
  • 13. SOMs Intuition/0. Websites you may like/[FreeCourseSite.com].url 127 Bytes
  • 13. SOMs Intuition/[FreeCourseSite.com].url 127 Bytes
  • 14. Building a SOM/0. Websites you may like/[FreeCourseSite.com].url 127 Bytes
  • 14. Building a SOM/[FreeCourseSite.com].url 127 Bytes
  • 15. Mega Case Study/0. Websites you may like/[FreeCourseSite.com].url 127 Bytes
  • 15. Mega Case Study/[FreeCourseSite.com].url 127 Bytes
  • 16. ------------------------- Part 5 - Boltzmann Machines -------------------------/0. Websites you may like/[FreeCourseSite.com].url 127 Bytes
  • 16. ------------------------- Part 5 - Boltzmann Machines -------------------------/[FreeCourseSite.com].url 127 Bytes
  • 17. Boltzmann Machine Intuition/0. Websites you may like/[FreeCourseSite.com].url 127 Bytes
  • 17. Boltzmann Machine Intuition/[FreeCourseSite.com].url 127 Bytes
  • 18. Building a Boltzmann Machine/0. Websites you may like/[FreeCourseSite.com].url 127 Bytes
  • 18. Building a Boltzmann Machine/[FreeCourseSite.com].url 127 Bytes
  • 19. ---------------------------- Part 6 - AutoEncoders ----------------------------/0. Websites you may like/[FreeCourseSite.com].url 127 Bytes
  • 19. ---------------------------- Part 6 - AutoEncoders ----------------------------/[FreeCourseSite.com].url 127 Bytes
  • 20. AutoEncoders Intuition/0. Websites you may like/[FreeCourseSite.com].url 127 Bytes
  • 20. AutoEncoders Intuition/[FreeCourseSite.com].url 127 Bytes
  • 21. Building an AutoEncoder/0. Websites you may like/[FreeCourseSite.com].url 127 Bytes
  • 21. Building an AutoEncoder/[FreeCourseSite.com].url 127 Bytes
  • 22. ------------------- Annex - Get the Machine Learning Basics -------------------/0. Websites you may like/[FreeCourseSite.com].url 127 Bytes
  • 22. ------------------- Annex - Get the Machine Learning Basics -------------------/[FreeCourseSite.com].url 127 Bytes
  • 23. Regression & Classification Intuition/0. Websites you may like/[FreeCourseSite.com].url 127 Bytes
  • 23. Regression & Classification Intuition/[FreeCourseSite.com].url 127 Bytes
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  • 24. Data Preprocessing/[FreeCourseSite.com].url 127 Bytes
  • 25. Data Preprocessing in Python/0. Websites you may like/[FreeCourseSite.com].url 127 Bytes
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  • 26. Logistic Regression/0. Websites you may like/[FreeCourseSite.com].url 127 Bytes
  • 26. Logistic Regression/[FreeCourseSite.com].url 127 Bytes
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  • 01. Welcome to the course!/0. Websites you may like/[CourseClub.Me].url 122 Bytes
  • 01. Welcome to the course!/[CourseClub.Me].url 122 Bytes
  • 02. --------------------- Part 1 - Artificial Neural Networks ---------------------/0. Websites you may like/[CourseClub.Me].url 122 Bytes
  • 02. --------------------- Part 1 - Artificial Neural Networks ---------------------/[CourseClub.Me].url 122 Bytes
  • 03. ANN Intuition/0. Websites you may like/[CourseClub.Me].url 122 Bytes
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  • 04. Building an ANN/0. Websites you may like/[CourseClub.Me].url 122 Bytes
  • 04. Building an ANN/[CourseClub.Me].url 122 Bytes
  • 05. -------------------- Part 2 - Convolutional Neural Networks --------------------/0. Websites you may like/[CourseClub.Me].url 122 Bytes
  • 05. -------------------- Part 2 - Convolutional Neural Networks --------------------/[CourseClub.Me].url 122 Bytes
  • 06. CNN Intuition/0. Websites you may like/[CourseClub.Me].url 122 Bytes
  • 06. CNN Intuition/[CourseClub.Me].url 122 Bytes
  • 07. Building a CNN/0. Websites you may like/[CourseClub.Me].url 122 Bytes
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  • 08. ---------------------- Part 3 - Recurrent Neural Networks ----------------------/0. Websites you may like/[CourseClub.Me].url 122 Bytes
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  • 11. Evaluating and Improving the RNN/0. Websites you may like/[CourseClub.Me].url 122 Bytes
  • 11. Evaluating and Improving the RNN/[CourseClub.Me].url 122 Bytes
  • 12. ------------------------ Part 4 - Self Organizing Maps ------------------------/0. Websites you may like/[CourseClub.Me].url 122 Bytes
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  • 13. SOMs Intuition/0. Websites you may like/[CourseClub.Me].url 122 Bytes
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  • 14. Building a SOM/0. Websites you may like/[CourseClub.Me].url 122 Bytes
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  • 15. Mega Case Study/0. Websites you may like/[CourseClub.Me].url 122 Bytes
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  • 16. ------------------------- Part 5 - Boltzmann Machines -------------------------/0. Websites you may like/[CourseClub.Me].url 122 Bytes
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  • 18. Building a Boltzmann Machine/0. Websites you may like/[CourseClub.Me].url 122 Bytes
  • 18. Building a Boltzmann Machine/[CourseClub.Me].url 122 Bytes
  • 19. ---------------------------- Part 6 - AutoEncoders ----------------------------/0. Websites you may like/[CourseClub.Me].url 122 Bytes
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  • 22. ------------------- Annex - Get the Machine Learning Basics -------------------/0. Websites you may like/[CourseClub.Me].url 122 Bytes
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  • 23. Regression & Classification Intuition/0. Websites you may like/[CourseClub.Me].url 122 Bytes
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  • 26. Logistic Regression/0. Websites you may like/[CourseClub.Me].url 122 Bytes
  • 26. Logistic Regression/[CourseClub.Me].url 122 Bytes
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  • 04. Building an ANN/1. Get the code and dataset ready.html 0 Bytes
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  • 08. ---------------------- Part 3 - Recurrent Neural Networks ----------------------/1. Welcome to Part 3 - Recurrent Neural Networks.html 0 Bytes
  • 10. Building a RNN/1. Get the code and dataset ready.html 0 Bytes
  • 11. Evaluating and Improving the RNN/1. Evaluating the RNN.html 0 Bytes
  • 11. Evaluating and Improving the RNN/2. Improving the RNN.html 0 Bytes
  • 13. SOMs Intuition/1. How Do Self-Organizing Maps Work Understanding SOM in Deep Learning.vtt 0 Bytes
  • 14. Building a SOM/1. Get the code and dataset ready.html 0 Bytes

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