| 01 - Welcome to the course! | |||
| 001 Welcome Challenge!.html | 5.8 KB | ||
| 002 Introduction to Deep Learning From Historical Context to Modern Applications.mp4 | 34.64 MB | ||
| 002 Introduction to Deep Learning From Historical Context to Modern Applications.srt | 21.27 KB | ||
| 003 Get the codes, datasets and slides here.html | 2.75 KB | ||
| 004 EXTRA Use ChatGPT to Boost your Deep Learning Skills.html | 3.26 KB | ||
| Course updated jan 2025.txt | 10 B | ||
| Direct Download.txt | 13 B | ||
| 02 - --------------------- Part 1 - Artificial Neural Networks --------------------- | |||
| 001 Welcome to Part 1 - Artificial Neural Networks.html | 2.53 KB | ||
| 03 - ANN Intuition | |||
| 001 What You'll Need for ANN.html | 2.55 KB | ||
| 002 How Neural Networks Learn Gradient Descent and Backpropagation Explained.mp4 | 8.1 MB | ||
| 002 How Neural Networks Learn Gradient Descent and Backpropagation Explained.srt | 4.51 KB | ||
| 003 Understanding Neurons The Building Blocks of Artificial Neural Networks.mp4 | 56.61 MB | ||
| 003 Understanding Neurons The Building Blocks of Artificial Neural Networks.srt | 29.5 KB | ||
| 004 Understanding Activation Functions in Neural Networks Sigmoid, ReLU, and More.mp4 | 31.51 MB | ||
| 004 Understanding Activation Functions in Neural Networks Sigmoid, ReLU, and More.srt | 14.14 KB | ||
| 005 How Do Neural Networks Work Step-by-Step Guide to Property Valuation Example.mp4 | 29.68 MB | ||
| 005 How Do Neural Networks Work Step-by-Step Guide to Property Valuation Example.srt | 22.97 KB | ||
| 006 How Do Neural Networks Learn Understanding Backpropagation and Cost Functions.mp4 | 49.23 MB | ||
| 006 How Do Neural Networks Learn Understanding Backpropagation and Cost Functions.srt | 21.74 KB | ||
| 007 Mastering Gradient Descent Key to Efficient Neural Network Training.mp4 | 34.24 MB | ||
| 007 Mastering Gradient Descent Key to Efficient Neural Network Training.srt | 17.55 KB | ||
| 008 How to Use Stochastic Gradient Descent for Deep Learning Optimization.mp4 | 33.18 MB | ||
| 008 How to Use Stochastic Gradient Descent for Deep Learning Optimization.srt | 14.87 KB | ||
| 009 Understanding Backpropagation Algorithm Key to Optimizing Deep Learning Models.mp4 | 20.35 MB | ||
| 009 Understanding Backpropagation Algorithm Key to Optimizing Deep Learning Models.srt | 8.6 KB | ||
| 04 - Building an ANN | |||
| 001 Get the code and dataset ready.html | 4.01 KB | ||
| 002 Step 1 - Data Preprocessing for Deep Learning Preparing Neural Network Dataset.mp4 | 36.33 MB | ||
| 002 Step 1 - Data Preprocessing for Deep Learning Preparing Neural Network Dataset.srt | 18.73 KB | ||
| 003 Check out our free course on ANN for Regression.html | 2.78 KB | ||
| 004 Step 2 - Data Preprocessing for Neural Networks Essential Steps and Techniques.mp4 | 69.12 MB | ||
| 004 Step 2 - Data Preprocessing for Neural Networks Essential Steps and Techniques.srt | 30.87 KB | ||
| 005 Step 3 - Constructing an Artificial Neural Network Adding Input --& Hidden Layers.mp4 | 54.85 MB | ||
| 005 Step 3 - Constructing an Artificial Neural Network Adding Input --& Hidden Layers.srt | 24.56 KB | ||
| 006 Step 4 - Compile and Train Neural Network Optimizers, Loss Functions --& Metrics.mp4 | 45.41 MB | ||
| 006 Step 4 - Compile and Train Neural Network Optimizers, Loss Functions --& Metrics.srt | 20.31 KB | ||
| 007 Step 5 - How to Make Predictions and Evaluate Neural Network Model in Python.mp4 | 61.64 MB | ||
| 007 Step 5 - How to Make Predictions and Evaluate Neural Network Model in Python.srt | 26.57 KB | ||
| 05 - -------------------- Part 2 - Convolutional Neural Networks -------------------- | |||
| 001 Welcome to Part 2 - Convolutional Neural Networks.html | 2.51 KB | ||
| 06 - CNN Intuition | |||
| 001 What You'll Need for CNN.html | 2.56 KB | ||
| 002 Understanding CNN Architecture From Convolution to Fully Connected Layers.mp4 | 10.68 MB | ||
| 002 Understanding CNN Architecture From Convolution to Fully Connected Layers.srt | 6.03 KB | ||
| 003 How Do Convolutional Neural Networks Work Understanding CNN Architecture.mp4 | 54.97 MB | ||
| 003 How Do Convolutional Neural Networks Work Understanding CNN Architecture.srt | 26.34 KB | ||
| 004 How to Apply Convolution Filters in Neural Networks Feature Detection Explained.mp4 | 44.4 MB | ||
| 004 How to Apply Convolution Filters in Neural Networks Feature Detection Explained.srt | 28.06 KB | ||
| 005 Rectified Linear Units --(ReLU--) in Deep Learning Optimizing CNN Performance.mp4 | 25.18 MB | ||
| 005 Rectified Linear Units --(ReLU--) in Deep Learning Optimizing CNN Performance.srt | 10.99 KB | ||
| 006 Understanding Spatial Invariance in CNNs Max Pooling Explained for Beginners.mp4 | 55.67 MB | ||
| 006 Understanding Spatial Invariance in CNNs Max Pooling Explained for Beginners.srt | 25.27 KB | ||
| 007 How to Flatten Pooled Feature Maps in Convolutional Neural Networks --(CNNs--).mp4 | 6.14 MB | ||
| 007 How to Flatten Pooled Feature Maps in Convolutional Neural Networks --(CNNs--).srt | 3.23 KB | ||
| 008 How Do Fully Connected Layers Work in Convolutional Neural Networks --(CNNs--).mp4 | 52.78 MB | ||
| 008 How Do Fully Connected Layers Work in Convolutional Neural Networks --(CNNs--).srt | 37.5 KB | ||
| 009 CNN Building Blocks Feature Maps, ReLU, Pooling, and Fully Connected Layers.mp4 | 16.4 MB | ||
| 009 CNN Building Blocks Feature Maps, ReLU, Pooling, and Fully Connected Layers.srt | 6.83 KB | ||
| 010 Understanding Softmax Activation and Cross-Entropy Loss in Deep Learning.mp4 | 67.3 MB | ||
| 010 Understanding Softmax Activation and Cross-Entropy Loss in Deep Learning.srt | 32.01 KB | ||
| 07 - Building a CNN | |||
| 001 Get the code and dataset ready.html | 3.79 KB | ||
| 002 Step 1 - Convolutional Neural Networks Explained Image Classification Tutorial.mp4 | 27.8 MB | ||
| 002 Step 1 - Convolutional Neural Networks Explained Image Classification Tutorial.srt | 13.6 KB | ||
| 003 Step 2 - Deep Learning Preprocessing Scaling --& Transforming Images for CNNs.mp4 | 67.42 MB | ||
| 003 Step 2 - Deep Learning Preprocessing Scaling --& Transforming Images for CNNs.srt | 30.55 KB | ||
| 004 Step 3 - Building CNN Architecture Convolutional Layers --& Max Pooling Explained.mp4 | 68.02 MB | ||
| 004 Step 3 - Building CNN Architecture Convolutional Layers --& Max Pooling Explained.srt | 36.66 KB | ||
| 005 Step 4 - Train CNN for Image Classification Optimize with Keras --& TensorFlow.mp4 | 27.99 MB | ||
| 005 Step 4 - Train CNN for Image Classification Optimize with Keras --& TensorFlow.srt | 12.28 KB | ||
| 006 Step 5 - Deploying a CNN for Real-World Image Recognition.mp4 | 56.64 MB | ||
| 006 Step 5 - Deploying a CNN for Real-World Image Recognition.srt | 29.46 KB | ||
| 007 Develop an Image Recognition System Using Convolutional Neural Networks.mp4 | 87.16 MB | ||
| 007 Develop an Image Recognition System Using Convolutional Neural Networks.srt | 36.53 KB | ||
| Course updated jan 2025.txt | 10 B | ||
| Direct Download.txt | 13 B | ||
| 08 - ---------------------- Part 3 - Recurrent Neural Networks ---------------------- | |||
| 001 Welcome to Part 3 - Recurrent Neural Networks.html | 2.71 KB | ||
| 09 - RNN Intuition | |||
| 001 What You'll Need for RNN.html | 2.55 KB | ||
| 002 How Do Recurrent Neural Networks --(RNNs--) Work Deep Learning Explained.mp4 | 6.88 MB | ||
| 002 How Do Recurrent Neural Networks --(RNNs--) Work Deep Learning Explained.srt | 4 KB | ||
| 003 What is a Recurrent Neural Network --(RNN--) Deep Learning for Sequential Data.mp4 | 43.59 MB | ||
| 003 What is a Recurrent Neural Network --(RNN--) Deep Learning for Sequential Data.srt | 27.88 KB | ||
| 004 Understanding the Vanishing Gradient Problem in Recurrent Neural Networks --(RNNs--).mp4 | 54.89 MB | ||
| 004 Understanding the Vanishing Gradient Problem in Recurrent Neural Networks --(RNNs--).srt | 26.54 KB | ||
| 005 Understanding Long Short-Term Memory --(LSTM--) Architecture for Deep Learning.mp4 | 75.06 MB | ||
| 005 Understanding Long Short-Term Memory --(LSTM--) Architecture for Deep Learning.srt | 33.6 KB | ||
| 006 How LSTMs Work in Practice Visualizing Neural Network Predictions.mp4 | 64.06 MB | ||
| 006 How LSTMs Work in Practice Visualizing Neural Network Predictions.srt | 24.86 KB | ||
| 007 LSTM Variations Peepholes, Combined Gates, and GRUs in Deep Learning.mp4 | 13.76 MB | ||
| 007 LSTM Variations Peepholes, Combined Gates, and GRUs in Deep Learning.srt | 5.87 KB | ||
| 10 - Building a RNN | |||
| 001 Get the code and dataset ready.html | 4.51 KB | ||
| 002 Step 1 - Building a Robust LSTM Neural Network for Stock Price Trend Prediction.mp4 | 24.65 MB | ||
| 002 Step 1 - Building a Robust LSTM Neural Network for Stock Price Trend Prediction.srt | 12.82 KB | ||
| 003 Step 2 - Importing Training Data for LSTM Stock Price Prediction Model.mp4 | 26.81 MB | ||
| 003 Step 2 - Importing Training Data for LSTM Stock Price Prediction Model.srt | 11.25 KB | ||
| 004 Step 3 - Applying Min-Max Normalization for Time Series Data in Neural Networks.mp4 | 22.62 MB | ||
| 004 Step 3 - Applying Min-Max Normalization for Time Series Data in Neural Networks.srt | 9.45 KB | ||
| 005 Step 4 - Building X_train and y_train Arrays for LSTM Time Series Forecasting.mp4 | 57.77 MB | ||
| 005 Step 4 - Building X_train and y_train Arrays for LSTM Time Series Forecasting.srt | 24.32 KB | ||
| 006 Step 5 - Preparing Time Series Data for LSTM Neural Network in Stock Forecasting.mp4 | 41.4 MB | ||
| 006 Step 5 - Preparing Time Series Data for LSTM Neural Network in Stock Forecasting.srt | 19.96 KB | ||
| 007 Step 6 - Create RNN Architecture Sequential Layers vs Computational Graphs.mp4 | 10.81 MB | ||
| 007 Step 6 - Create RNN Architecture Sequential Layers vs Computational Graphs.srt | 4.71 KB | ||
| 008 Step 7 - Adding First LSTM Layer Key Components for Stock Market Prediction.mp4 | 33.05 MB | ||
| 008 Step 7 - Adding First LSTM Layer Key Components for Stock Market Prediction.srt | 14.13 KB | ||
| 009 Step 8 - Implementing Dropout Regularization in LSTM Networks for Forecasting.mp4 | 20.26 MB | ||
| 009 Step 8 - Implementing Dropout Regularization in LSTM Networks for Forecasting.srt | 8.53 KB | ||
| 010 Step 9 - Finalizing RNN Architecture Dense Layer for Stock Price Forecasting.mp4 | 12.68 MB | ||
| 010 Step 9 - Finalizing RNN Architecture Dense Layer for Stock Price Forecasting.srt | 5.39 KB | ||
| 011 Step 10 - Compile RNN with Adam Optimizer for Stock Price Prediction in Python.mp4 | 16.56 MB | ||
| 011 Step 10 - Compile RNN with Adam Optimizer for Stock Price Prediction in Python.srt | 7.02 KB | ||
| 012 Step 11 - Optimizing Epochs and Batch Size for LSTM Stock Price Forecasting.mp4 | 41.48 MB | ||
| 012 Step 11 - Optimizing Epochs and Batch Size for LSTM Stock Price Forecasting.srt | 14.28 KB | ||
| 013 Step 12 - Visualizing LSTM Predictions Real vs Forecasted Google Stock Prices.mp4 | 21.26 MB | ||
| 013 Step 12 - Visualizing LSTM Predictions Real vs Forecasted Google Stock Prices.srt | 8.36 KB | ||
| 014 Step 13 - Preparing Historical Stock Data for LSTM Model Scaling and Reshaping.mp4 | 64.1 MB | ||
| 014 Step 13 - Preparing Historical Stock Data for LSTM Model Scaling and Reshaping.srt | 25.71 KB | ||
| 015 Step 14 - Creating 3D Input Structure for LSTM Stock Price Prediction in Python.mp4 | 31.64 MB | ||
| 015 Step 14 - Creating 3D Input Structure for LSTM Stock Price Prediction in Python.srt | 12.69 KB | ||
| 016 Step 15 - Visualizing LSTM Predictions Plotting Real vs Predicted Stock Prices.mp4 | 34.33 MB | ||
| 016 Step 15 - Visualizing LSTM Predictions Plotting Real vs Predicted Stock Prices.srt | 15.23 KB | ||
| 11 - Evaluating and Improving the RNN | |||
| 001 Evaluating the RNN.html | 3.97 KB | ||
| 002 Improving the RNN.html | 3.48 KB | ||
| 12 - ------------------------ Part 4 - Self Organizing Maps ------------------------ | |||
| 001 Welcome to Part 4 - Self Organizing Maps.html | 2.59 KB | ||
| 13 - SOMs Intuition | |||
| 001 How Do Self-Organizing Maps Work Understanding SOM in Deep Learning.mp4 | 9.5 MB | ||
| 001 How Do Self-Organizing Maps Work Understanding SOM in Deep Learning.srt | 5.31 KB | ||
| 002 Self-Organizing Maps --(SOM--) Unsupervised Deep Learning for Dimensionality Reduct.mp4 | 32.54 MB | ||
| 002 Self-Organizing Maps --(SOM--) Unsupervised Deep Learning for Dimensionality Reduct.srt | 14.81 KB | ||
| 003 Why K-Means Clustering is Essential for Understanding Self-Organizing Maps.mp4 | 7.14 MB | ||
| 003 Why K-Means Clustering is Essential for Understanding Self-Organizing Maps.srt | 3.9 KB | ||
| 004 Self-Organizing Maps Tutorial Dimensionality Reduction in Machine Learning.mp4 | 53.79 MB | ||
| 004 Self-Organizing Maps Tutorial Dimensionality Reduction in Machine Learning.srt | 26.03 KB | ||
| 005 How Self-Organizing Maps --(SOMs--) Learn Unsupervised Deep Learning Explained.mp4 | 38.91 MB | ||
| 005 How Self-Organizing Maps --(SOMs--) Learn Unsupervised Deep Learning Explained.srt | 24.78 KB | ||
| 006 How to Create a Self-Organizing Map --(SOM--) in DL Step-by-Step Tutorial.mp4 | 25.35 MB | ||
| 006 How to Create a Self-Organizing Map --(SOM--) in DL Step-by-Step Tutorial.srt | 16.38 KB | ||
| 007 Interpreting SOM Clusters Unsupervised Learning Techniques for Data Analysis.mp4 | 16.99 MB | ||
| 007 Interpreting SOM Clusters Unsupervised Learning Techniques for Data Analysis.srt | 7.57 KB | ||
| 008 Understanding K-Means Clustering Intuitive Explanation with Visual Examples.mp4 | 54.69 MB | ||
| 008 Understanding K-Means Clustering Intuitive Explanation with Visual Examples.srt | 24.42 KB | ||
| 009 K-Means Clustering Avoiding the Random Initialization Trap in Machine Learning.mp4 | 29.66 MB | ||
| 009 K-Means Clustering Avoiding the Random Initialization Trap in Machine Learning.srt | 14.04 KB | ||
| 010 How to Find the Optimal Number of Clusters in K-Means WCSS and Elbow Method.mp4 | 43.17 MB | ||
| 010 How to Find the Optimal Number of Clusters in K-Means WCSS and Elbow Method.srt | 20.4 KB | ||
| 14 - Building a SOM | |||
| 001 Get the code and dataset ready.html | 4.4 KB | ||
| 002 Step 1 - Implementing Self-Organizing Maps --(SOMs--) for Fraud Detection in Python.mp4 | 52 MB | ||
| 002 Step 1 - Implementing Self-Organizing Maps --(SOMs--) for Fraud Detection in Python.srt | 27.72 KB | ||
| 003 Step 2 - SOM Weight Initialization and Training Tutorial for Anomaly Detection.mp4 | 36.64 MB | ||
| 003 Step 2 - SOM Weight Initialization and Training Tutorial for Anomaly Detection.srt | 16.01 KB | ||
| 004 Step 3 - SOM Visualization Techniques Colorbar --& Markers for Outlier Detection.mp4 | 64.29 MB | ||
| 004 Step 3 - SOM Visualization Techniques Colorbar --& Markers for Outlier Detection.srt | 29.15 KB | ||
| 005 Step 4 - Catching Cheaters with SOMs Mapping Winning Nodes to Customer Data.mp4 | 44.87 MB | ||
| 005 Step 4 - Catching Cheaters with SOMs Mapping Winning Nodes to Customer Data.srt | 21.78 KB | ||
| Course updated jan 2025.txt | 10 B | ||
| Direct Download.txt | 13 B | ||
| 15 - Mega Case Study | |||
| 001 Get the code and dataset ready.html | 4.41 KB | ||
| 002 Step 1 - Building a Hybrid Deep Learning Model for Credit Card Fraud Detection.mp4 | 10.74 MB | ||
| 002 Step 1 - Building a Hybrid Deep Learning Model for Credit Card Fraud Detection.srt | 5.18 KB | ||
| 003 Step 2 - Developing a Fraud Detection System Using Self-Organizing Maps.mp4 | 17.78 MB | ||
| 003 Step 2 - Developing a Fraud Detection System Using Self-Organizing Maps.srt | 7.4 KB | ||
| 004 Step 3 - Building a Hybrid Model From Unsupervised to Supervised Deep Learning.mp4 | 55.65 MB | ||
| 004 Step 3 - Building a Hybrid Model From Unsupervised to Supervised Deep Learning.srt | 30.19 KB | ||
| 005 Step 4 - Implementing Fraud Detection with SOM A Deep Learning Approach.mp4 | 35.35 MB | ||
| 005 Step 4 - Implementing Fraud Detection with SOM A Deep Learning Approach.srt | 18.9 KB | ||
| 16 - ------------------------- Part 5 - Boltzmann Machines ------------------------- | |||
| 001 Welcome to Part 5 - Boltzmann Machines.html | 3.62 KB | ||
| 17 - Boltzmann Machine Intuition | |||
| 001 Understanding Boltzmann Machines Deep Learning Fundamentals for AI Enthusiasts.mp4 | 6.5 MB | ||
| 001 Understanding Boltzmann Machines Deep Learning Fundamentals for AI Enthusiasts.srt | 4.6 KB | ||
| 002 Boltzmann Machines vs. Neural Networks Key Differences in Deep Learning.mp4 | 54.47 MB | ||
| 002 Boltzmann Machines vs. Neural Networks Key Differences in Deep Learning.srt | 24.33 KB | ||
| 003 Deep Learning Fundamentals Energy-Based Models --& Their Role in Neural Networks.mp4 | 40.46 MB | ||
| 003 Deep Learning Fundamentals Energy-Based Models --& Their Role in Neural Networks.srt | 17.87 KB | ||
| 004 How to Edit Wikipedia Adding Boltzmann Distribution in Deep Learning.mp4 | 13.32 MB | ||
| 004 How to Edit Wikipedia Adding Boltzmann Distribution in Deep Learning.srt | 6.43 KB | ||
| 005 How Restricted Boltzmann Machines Work Deep Learning for Recommender Systems.mp4 | 47.56 MB | ||
| 005 How Restricted Boltzmann Machines Work Deep Learning for Recommender Systems.srt | 31.55 KB | ||
| 006 How Energy-Based Models Work Deep Dive into Contrastive Divergence Algorithm.mp4 | 59.23 MB | ||
| 006 How Energy-Based Models Work Deep Dive into Contrastive Divergence Algorithm.srt | 28.88 KB | ||
| 007 Deep Belief Networks Understanding RBM Stacking in Deep Learning Models.mp4 | 20.47 MB | ||
| 007 Deep Belief Networks Understanding RBM Stacking in Deep Learning Models.srt | 8.77 KB | ||
| 008 Deep Boltzmann Machines vs Deep Belief Networks Key Differences Explained.mp4 | 11.2 MB | ||
| 008 Deep Boltzmann Machines vs Deep Belief Networks Key Differences Explained.srt | 4.94 KB | ||
| Course updated jan 2025.txt | 10 B | ||
| Direct Download.txt | 13 B | ||
| 18 - Building a Boltzmann Machine | |||
| 001 Get the code and dataset ready.html | 4.83 KB | ||
| 002 Step 0 - Building a Movie Recommender System with RBMs Data Preprocessing Guide.mp4 | 34.79 MB | ||
| 002 Step 0 - Building a Movie Recommender System with RBMs Data Preprocessing Guide.srt | 16.74 KB | ||
| 003 Same Data Preprocessing in Parts 5 and 6.html | 2.59 KB | ||
| 004 Step 1 - Importing Movie Datasets for RBM-Based Recommender Systems in Python.mp4 | 35.1 MB | ||
| 004 Step 1 - Importing Movie Datasets for RBM-Based Recommender Systems in Python.srt | 15.92 KB | ||
| 005 Step 2 - Preparing Training and Test Sets for Restricted Boltzmann Machine.mp4 | 36.67 MB | ||
| 005 Step 2 - Preparing Training and Test Sets for Restricted Boltzmann Machine.srt | 16.23 KB | ||
| 006 Step 3 - Preparing Data for RBM Calculating Total Users and Movies in Python.mp4 | 31.89 MB | ||
| 006 Step 3 - Preparing Data for RBM Calculating Total Users and Movies in Python.srt | 16.44 KB | ||
| 007 Step 4 - Convert Training --& Test Sets to RBM-Ready Arrays in Python.mp4 | 79.37 MB | ||
| 007 Step 4 - Convert Training --& Test Sets to RBM-Ready Arrays in Python.srt | 35.14 KB | ||
| 008 Step 5 - Converting NumPy Arrays to PyTorch Tensors for Deep Learning Models.mp4 | 19.37 MB | ||
| 008 Step 5 - Converting NumPy Arrays to PyTorch Tensors for Deep Learning Models.srt | 8.85 KB | ||
| 009 Step 6 - RBM Data Preprocessing Transforming Movie Ratings for Neural Networks.mp4 | 29.11 MB | ||
| 009 Step 6 - RBM Data Preprocessing Transforming Movie Ratings for Neural Networks.srt | 13 KB | ||
| 010 Step 7 - Implementing Restricted Boltzmann Machine Class Structure in PyTorch.mp4 | 38.87 MB | ||
| 010 Step 7 - Implementing Restricted Boltzmann Machine Class Structure in PyTorch.srt | 17.63 KB | ||
| 011 Step 8 - RBM Hidden Layer Sampling Bernoulli Distribution in PyTorch Tutorial.mp4 | 48.35 MB | ||
| 011 Step 8 - RBM Hidden Layer Sampling Bernoulli Distribution in PyTorch Tutorial.srt | 24.1 KB | ||
| 012 Step 9 - RBM Visible Node Sampling Bernoulli Distribution in Deep Learning.mp4 | 23.88 MB | ||
| 012 Step 9 - RBM Visible Node Sampling Bernoulli Distribution in Deep Learning.srt | 10.6 KB | ||
| 013 Step 10 - RBM Training Function Updating Weights and Biases with Gibbs Sampling.mp4 | 44.32 MB | ||
| 013 Step 10 - RBM Training Function Updating Weights and Biases with Gibbs Sampling.srt | 18.91 KB | ||
| 014 Step 11 - How to Set Up an RBM Model Choosing NV, NH, and Batch Size Parameters.mp4 | 27.04 MB | ||
| 014 Step 11 - How to Set Up an RBM Model Choosing NV, NH, and Batch Size Parameters.srt | 11.63 KB | ||
| 015 Step 12 - RBM Training Loop Epoch Setup and Loss Function Implementation.mp4 | 51.02 MB | ||
| 015 Step 12 - RBM Training Loop Epoch Setup and Loss Function Implementation.srt | 21.33 KB | ||
| 016 Step 13 - RBM Training Updating Weights and Biases with Contrastive Divergence.mp4 | 73.76 MB | ||
| 016 Step 13 - RBM Training Updating Weights and Biases with Contrastive Divergence.srt | 29.86 KB | ||
| 017 Step 14 - Optimizing RBM Models From Training to Test Set Performance Analysis.mp4 | 65.13 MB | ||
| 017 Step 14 - Optimizing RBM Models From Training to Test Set Performance Analysis.srt | 29.49 KB | ||
| 018 Evaluating the Boltzmann Machine.html | 6.06 KB | ||
| 19 - ---------------------------- Part 6 - AutoEncoders ---------------------------- | |||
| 001 Welcome to Part 6 - AutoEncoders.html | 3.17 KB | ||
| Course updated jan 2025.txt | 10 B | ||
| Direct Download.txt | 13 B | ||
| 20 - AutoEncoders Intuition | |||
| 001 Deep Learning Autoencoders Types, Architecture, and Training Explained.mp4 | 8.34 MB | ||
| 001 Deep Learning Autoencoders Types, Architecture, and Training Explained.srt | 3.77 KB | ||
| 002 Autoencoders in Machine Learning Applications and Architecture Overview.mp4 | 25.64 MB | ||
| 002 Autoencoders in Machine Learning Applications and Architecture Overview.srt | 19.46 KB | ||
| 003 Autoencoder Bias in Deep Learning Improving Neural Network Performance.mp4 | 4.8 MB | ||
| 003 Autoencoder Bias in Deep Learning Improving Neural Network Performance.srt | 2.33 KB | ||
| 004 How to Train an Autoencoder Step-by-Step Guide for Deep Learning Beginners.mp4 | 23.48 MB | ||
| 004 How to Train an Autoencoder Step-by-Step Guide for Deep Learning Beginners.srt | 11.23 KB | ||
| 005 How to Use Overcomplete Hidden Layers in Autoencoders for Feature Extraction.mp4 | 14.77 MB | ||
| 005 How to Use Overcomplete Hidden Layers in Autoencoders for Feature Extraction.srt | 6.53 KB | ||
| 006 Sparse Autoencoders in Deep Learning Preventing Overfitting in Neural Networks.mp4 | 23.69 MB | ||
| 006 Sparse Autoencoders in Deep Learning Preventing Overfitting in Neural Networks.srt | 10.19 KB | ||
| 007 Denoising Autoencoders Deep Learning Regularization Technique Explained.mp4 | 9.65 MB | ||
| 007 Denoising Autoencoders Deep Learning Regularization Technique Explained.srt | 4.25 KB | ||
| 008 What are Contractive Autoencoders Deep Learning Regularization Techniques.mp4 | 9.08 MB | ||
| 008 What are Contractive Autoencoders Deep Learning Regularization Techniques.srt | 3.95 KB | ||
| 009 What are Stacked Autoencoders in Deep Learning Architecture and Applications.mp4 | 7.21 MB | ||
| 009 What are Stacked Autoencoders in Deep Learning Architecture and Applications.srt | 2.85 KB | ||
| 010 Deep Autoencoders vs Stacked Autoencoders Key Differences in Neural Networks.mp4 | 7.07 MB | ||
| 010 Deep Autoencoders vs Stacked Autoencoders Key Differences in Neural Networks.srt | 3.04 KB | ||
| 21 - Building an AutoEncoder | |||
| 001 Get the code and dataset ready.html | 4.83 KB | ||
| 002 Same Data Preprocessing in Parts 5 and 6.html | 2.58 KB | ||
| 003 Step 1 - Building a Movie Recommendation System with AutoEncoders Data Import.mp4 | 41.61 MB | ||
| 003 Step 1 - Building a Movie Recommendation System with AutoEncoders Data Import.srt | 20.91 KB | ||
| 004 Step 2 - Preparing Training and Test Sets for Autoencoder Recommendation System.mp4 | 40.63 MB | ||
| 004 Step 2 - Preparing Training and Test Sets for Autoencoder Recommendation System.srt | 19.99 KB | ||
| 005 Step 3 - Preparing Data for Recommendation Systems User --& Movie Count in Python.mp4 | 28.89 MB | ||
| 005 Step 3 - Preparing Data for Recommendation Systems User --& Movie Count in Python.srt | 16.44 KB | ||
| 006 Homework Challenge - Coding Exercise.html | 3.85 KB | ||
| 007 Step 4 - Prepare Data for Autoencoder Creating User-Movie Rating Matrices.mp4 | 72.17 MB | ||
| 007 Step 4 - Prepare Data for Autoencoder Creating User-Movie Rating Matrices.srt | 35.07 KB | ||
| 008 Step 5 - Convert Training and Test Sets to PyTorch Tensors for Deep Learning.mp4 | 17.56 MB | ||
| 008 Step 5 - Convert Training and Test Sets to PyTorch Tensors for Deep Learning.srt | 8.88 KB | ||
| 009 Step 6 - Building Autoencoder Architecture Class Creation for Neural Networks.mp4 | 58.44 MB | ||
| 009 Step 6 - Building Autoencoder Architecture Class Creation for Neural Networks.srt | 29.39 KB | ||
| 010 Step 7 - Python Autoencoder Tutorial Implementing Activation Functions --& Layers.mp4 | 47.82 MB | ||
| 010 Step 7 - Python Autoencoder Tutorial Implementing Activation Functions --& Layers.srt | 27.41 KB | ||
| 011 Step 8 - PyTorch Techniques for Efficient Autoencoder Training on Large Datasets.mp4 | 51.87 MB | ||
| 011 Step 8 - PyTorch Techniques for Efficient Autoencoder Training on Large Datasets.srt | 32.05 KB | ||
| 012 Step 9 - Implementing Stochastic Gradient Descent in Autoencoder Architecture.mp4 | 46.64 MB | ||
| 012 Step 9 - Implementing Stochastic Gradient Descent in Autoencoder Architecture.srt | 27.67 KB | ||
| 013 Step 10 - Machine Learning Metrics Interpreting Loss in Autoencoder Training.mp4 | 15.25 MB | ||
| 013 Step 10 - Machine Learning Metrics Interpreting Loss in Autoencoder Training.srt | 8.09 KB | ||
| 014 Step 11 - How to Evaluate Recommender System Performance Using Test Set Loss.mp4 | 40.13 MB | ||
| 014 Step 11 - How to Evaluate Recommender System Performance Using Test Set Loss.srt | 20.32 KB | ||
| 015 THANK YOU Video.mp4 | 9.18 MB | ||
| 015 THANK YOU Video.srt | 2.86 KB | ||
| 22 - ------------------- Annex - Get the Machine Learning Basics ------------------- | |||
| 001 Annex - Get the Machine Learning Basics.html | 3.1 KB | ||
| 23 - Regression & Classification Intuition | |||
| 001 What You Need for Regression & Classification.html | 2.57 KB | ||
| 002 Simple Linear Regression Understanding Y = B0 + B1X in Machine Learning.mp4 | 16.29 MB | ||
| 002 Simple Linear Regression Understanding Y = B0 + B1X in Machine Learning.srt | 9.57 KB | ||
| 003 Linear Regression Explained Finding the Best Fitting Line for Data Analysis.mp4 | 10.86 MB | ||
| 003 Linear Regression Explained Finding the Best Fitting Line for Data Analysis.srt | 4.95 KB | ||
| 004 Multiple Linear Regression - Understanding Dependent --& Independent Variables.mp4 | 3.33 MB | ||
| 004 Multiple Linear Regression - Understanding Dependent --& Independent Variables.srt | 1.7 KB | ||
| 005 Understanding Logistic Regression Intuition and Probability in Classification.mp4 | 58.02 MB | ||
| 005 Understanding Logistic Regression Intuition and Probability in Classification.srt | 28.27 KB | ||
| 24 - Data Preprocessing | |||
| 001 Data Preprocessing.html | 2.69 KB | ||
| 002 How to Scale Features in Machine Learning Normalization vs Standardization.mp4 | 5.29 MB | ||
| 002 How to Scale Features in Machine Learning Normalization vs Standardization.srt | 2.74 KB | ||
| 003 Machine Learning Basics Using Train-Test Split to Evaluate Model Performance.mp4 | 7.02 MB | ||
| 003 Machine Learning Basics Using Train-Test Split to Evaluate Model Performance.srt | 3.3 KB | ||
| 004 Machine Learning Workflow Data Splitting, Feature Scaling, and Model Training.mp4 | 16.54 MB | ||
| 004 Machine Learning Workflow Data Splitting, Feature Scaling, and Model Training.srt | 10.73 KB | ||
| 25 - Data Preprocessing in Python | |||
| 001 Step 1 - Data Preprocessing in Python Essential Tools for ML Models.mp4 | 18.4 MB | ||
| 001 Step 1 - Data Preprocessing in Python Essential Tools for ML Models.srt | 8.98 KB | ||
| 002 Step 2 - How to Handle Missing Data in Python Data Preprocessing Techniques.mp4 | 18.44 MB | ||
| 002 Step 2 - How to Handle Missing Data in Python Data Preprocessing Techniques.srt | 11.38 KB | ||
| 003 Step 1 - Importing Essential Python Libraries for Data Preprocessing --& Analysis.mp4 | 12.25 MB | ||
| 003 Step 1 - Importing Essential Python Libraries for Data Preprocessing --& Analysis.srt | 6.2 KB | ||
| 004 Step 1 - Creating a DataFrame from CSV Python Data Preprocessing Basics.mp4 | 17.97 MB | ||
| 004 Step 1 - Creating a DataFrame from CSV Python Data Preprocessing Basics.srt | 8.81 KB | ||
| 005 Step 2 - Pandas DataFrame Indexing Building Feature Matrix X with iloc Method.mp4 | 16.19 MB | ||
| 005 Step 2 - Pandas DataFrame Indexing Building Feature Matrix X with iloc Method.srt | 8.01 KB | ||
| 006 Step 3 - Preprocessing Data Extracting Features and Target Variables in Python.mp4 | 19.83 MB | ||
| 006 Step 3 - Preprocessing Data Extracting Features and Target Variables in Python.srt | 10.01 KB | ||
| 007 For Python learners, summary of Object-oriented programming classes & objects.html | 3.71 KB | ||
| 008 Step 1 - Handling Missing Data in Python SimpleImputer for Data Preprocessing.mp4 | 20.39 MB | ||
| 008 Step 1 - Handling Missing Data in Python SimpleImputer for Data Preprocessing.srt | 9.8 KB | ||
| 009 Step 2 - Preprocessing Datasets Fit and Transform to Handle Missing Values.mp4 | 20.54 MB | ||
| 009 Step 2 - Preprocessing Datasets Fit and Transform to Handle Missing Values.srt | 9.5 KB | ||
| 010 Step 1 - Preprocessing Categorical Variables One-Hot Encoding in Python.mp4 | 15.17 MB | ||
| 010 Step 1 - Preprocessing Categorical Variables One-Hot Encoding in Python.srt | 7.04 KB | ||
| 011 Step 2 - Using fit_transform Method for Efficient Data Preprocessing in Python.mp4 | 20.27 MB | ||
| 011 Step 2 - Using fit_transform Method for Efficient Data Preprocessing in Python.srt | 10.1 KB | ||
| 012 Step 3 - Preprocessing Categorical Data One-Hot and Label Encoding Techniques.mp4 | 16.03 MB | ||
| 012 Step 3 - Preprocessing Categorical Data One-Hot and Label Encoding Techniques.srt | 7.7 KB | ||
| 013 Step 1 - Machine Learning Data Prep Splitting Dataset Before Feature Scaling.mp4 | 13.46 MB | ||
| 013 Step 1 - Machine Learning Data Prep Splitting Dataset Before Feature Scaling.srt | 6.25 KB | ||
| 014 Step 2 - Split Data into Train --& Test Sets with Scikit-learn--'s train_test_split.mp4 | 20.58 MB | ||
| 014 Step 2 - Split Data into Train --& Test Sets with Scikit-learn--'s train_test_split.srt | 9.9 KB | ||
| 015 Step 3 - Preparing Data for ML Splitting Datasets with Python and Scikit-learn.mp4 | 13.33 MB | ||
| 015 Step 3 - Preparing Data for ML Splitting Datasets with Python and Scikit-learn.srt | 6.01 KB | ||
| 016 Step 1 - How to Apply Feature Scaling for Preprocessing Machine Learning Data.mp4 | 20.45 MB | ||
| 016 Step 1 - How to Apply Feature Scaling for Preprocessing Machine Learning Data.srt | 10.06 KB | ||
| 017 Step 2 - Feature Scaling in Machine Learning When to Apply StandardScaler.mp4 | 16.34 MB | ||
| 017 Step 2 - Feature Scaling in Machine Learning When to Apply StandardScaler.srt | 7.9 KB | ||
| 018 Step 3 - Normalizing Data with Fit and Transform Methods in Scikit-learn.mp4 | 13.1 MB | ||
| 018 Step 3 - Normalizing Data with Fit and Transform Methods in Scikit-learn.srt | 6.3 KB | ||
| 019 Step 4 - How to Apply Feature Scaling to Training --& Test Sets in ML.mp4 | 20.17 MB | ||
| 019 Step 4 - How to Apply Feature Scaling to Training --& Test Sets in ML.srt | 10.03 KB | ||
| 26 - Logistic Regression | |||
| 001 Understanding the Logistic Regression Equation A Step-by-Step Guide.mp4 | 16.91 MB | ||
| 001 Understanding the Logistic Regression Equation A Step-by-Step Guide.srt | 8.15 KB | ||
| 002 How to Calculate Maximum Likelihood in Logistic Regression Step-by-Step Guide.mp4 | 9.66 MB | ||
| 002 How to Calculate Maximum Likelihood in Logistic Regression Step-by-Step Guide.srt | 6.04 KB | ||
| 003 Step 1a - Machine Learning Classification Logistic Regression in Python.mp4 | 19.65 MB | ||
| 003 Step 1a - Machine Learning Classification Logistic Regression in Python.srt | 9.08 KB | ||
| 004 Step 1b - Logistic Regression Analysis Importing Libraries and Splitting Data.mp4 | 13.71 MB | ||
| 004 Step 1b - Logistic Regression Analysis Importing Libraries and Splitting Data.srt | 7.07 KB | ||
| 005 Step 2a - Data Preprocessing for Logistic Regression Importing and Splitting.mp4 | 20.11 MB | ||
| 005 Step 2a - Data Preprocessing for Logistic Regression Importing and Splitting.srt | 9.98 KB | ||
| 006 Step 2b - Data Preprocessing Feature Scaling for Machine Learning in Python.mp4 | 20.46 MB | ||
| 006 Step 2b - Data Preprocessing Feature Scaling for Machine Learning in Python.srt | 10.08 KB | ||
| 007 Step 3a - Implementing Logistic Regression for Classification with Scikit-Learn.mp4 | 13.67 MB | ||
| 007 Step 3a - Implementing Logistic Regression for Classification with Scikit-Learn.srt | 6.62 KB | ||
| 008 Step 3b - Predicting Purchase Decisions with Logistic Regression in Python.mp4 | 12.03 MB | ||
| 008 Step 3b - Predicting Purchase Decisions with Logistic Regression in Python.srt | 5.61 KB | ||
| 009 Step 4a - Using Classifier Objects to Make Predictions in Machine Learning.mp4 | 20.56 MB | ||
| 009 Step 4a - Using Classifier Objects to Make Predictions in Machine Learning.srt | 9.21 KB | ||
| 010 Step 4b - Evaluating Logistic Regression Model Predicted vs Real Outcomes.mp4 | 6.27 MB | ||
| 010 Step 4b - Evaluating Logistic Regression Model Predicted vs Real Outcomes.srt | 3.09 KB | ||
| 011 Step 5 - Evaluating Machine Learning Models Confusion Matrix and Accuracy.mp4 | 20.42 MB | ||
| 011 Step 5 - Evaluating Machine Learning Models Confusion Matrix and Accuracy.srt | 12.19 KB | ||
| 012 Step 6a - Creating a Confusion Matrix for Machine Learning Model Evaluation.mp4 | 20.2 MB | ||
| 012 Step 6a - Creating a Confusion Matrix for Machine Learning Model Evaluation.srt | 9.91 KB | ||
| 013 Step 6b - Visualizing Machine Learning Results Training vs Test Set Comparison.mp4 | 11.46 MB | ||
| 013 Step 6b - Visualizing Machine Learning Results Training vs Test Set Comparison.srt | 5.65 KB | ||
| 014 Step 7a - Visualizing Logistic Regression 2D Plots for Classification Models.mp4 | 20.29 MB | ||
| 014 Step 7a - Visualizing Logistic Regression 2D Plots for Classification Models.srt | 9.17 KB | ||
| 015 Step 7b - Visualizing Logistic Regression Interpreting Classification Results.mp4 | 12.84 MB | ||
| 015 Step 7b - Visualizing Logistic Regression Interpreting Classification Results.srt | 6.01 KB | ||
| 016 Step 7c - Visualizing Test Results Assessing Machine Learning Model Accuracy.mp4 | 11.48 MB | ||
| 016 Step 7c - Visualizing Test Results Assessing Machine Learning Model Accuracy.srt | 5.38 KB | ||
| 017 Logistic Regression in Python - Step 7 (Colour-blind friendly image).html | 2.95 KB | ||
| 018 Machine Learning Regression and Classification EXTRA.html | 3.04 KB | ||
| 019 EXTRA CONTENT Logistic Regression Practical Case Study.html | 2.85 KB | ||
| Course updated jan 2025.txt | 10 B | ||
| Direct Download.txt | 13 B | ||
| 27 - Congratulations!! Don't forget your Prize ) | |||
| 001 Huge Congrats for completing the challenge!.html | 6.99 KB | ||
| 002 Bonus How To UNLOCK Top Salaries (Live Training).html | 4 KB | ||
| Course updated jan 2025.txt | 10 B | ||
| Direct Download.txt | 13 B | ||
| Course updated jan 2025.txt | 10 B | ||
| Direct Download.txt | 13 B | ||
| ▲ 359 total files | |||
Deep Learning A-Z 2025: Neural Networks, AI & ChatGPT Prize
Learn to create Deep Learning models in Python from two Machine Learning, Data Science experts. Code templates included.
Updated Jan 2025
Part 1 - Artificial Neural Networks
Part 2 - Convolutional Neural Networks
Part 3 - Recurrent Neural Networks
Part 4 - Self Organizing Maps
Part 5 - Boltzmann Machines
Part 6 - Auto Encoders
Annex - Get the Machine Learning Basics
Enjoy
| torrent name | size | uploader | age | seed | leech |
|---|---|---|---|---|---|
| 533.4 MB | freecoursewb | 1 week | 27 | 2 | |
| 4 GB | freecoursewb | 4 months | 12 | 10 | |
| 498 MB | freecoursewb | 4 months | 4 | 0 | |
| 4 GB | freecoursewb | 6 months | 24 | 10 | |
| 2.5 GB | freecoursewb | 9 months | 14 | 3 |
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