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