4.65 GB | 22min 58s | mp4 | 1920X1080 | 16:9
Genre:eLearning |Language:English

Files Included :
002 Introduction to Deep Learning From Historical Context to Modern Applications.mp4 (34.64 MB)
002 How Neural Networks Learn Gradient Descent and Backpropagation Explained.mp4 (8.1 MB)
003 Understanding Neurons The Building Blocks of Artificial Neural Networks.mp4 (56.61 MB)
004 Understanding Activation Functions in Neural Networks Sigmoid, ReLU, and More.mp4 (31.51 MB)
005 How Do Neural Networks Work Step-by-Step Guide to Property Valuation Example.mp4 (29.68 MB)
006 How Do Neural Networks Learn Understanding Backpropagation and Cost Functions.mp4 (49.23 MB)
007 Mastering Gradient Descent Key to Efficient Neural Network Training.mp4 (34.24 MB)
008 How to Use Stochastic Gradient Descent for Deep Learning Optimization.mp4 (33.18 MB)
009 Understanding Backpropagation Algorithm Key to Optimizing Deep Learning Models.mp4 (20.35 MB)
002 Step 1 - Data Preprocessing for Deep Learning Preparing Neural Network Dataset.mp4 (36.33 MB)
004 Step 2 - Data Preprocessing for Neural Networks Essential Steps and Techniques.mp4 (69.12 MB)
005 Step 3 - Constructing an Artificial Neural Network Adding Input --& Hidden Layers.mp4 (54.85 MB)
006 Step 4 - Compile and Train Neural Network Optimizers, Loss Functions --& Metrics.mp4 (45.41 MB)
007 Step 5 - How to Make Predictions and Evaluate Neural Network Model in Python.mp4 (61.64 MB)
002 Understanding CNN Architecture From Convolution to Fully Connected Layers.mp4 (10.68 MB)
003 How Do Convolutional Neural Networks Work Understanding CNN Architecture.mp4 (54.97 MB)
004 How to Apply Convolution Filters in Neural Networks Feature Detection Explained.mp4 (44.4 MB)
005 Rectified Linear Units --(ReLU--) in Deep Learning Optimizing CNN Performance.mp4 (25.18 MB)
006 Understanding Spatial Invariance in CNNs Max Pooling Explained for Beginners.mp4 (55.67 MB)
007 How to Flatten Pooled Feature Maps in Convolutional Neural Networks --(CNNs--).mp4 (6.14 MB)
008 How Do Fully Connected Layers Work in Convolutional Neural Networks --(CNNs--).mp4 (52.78 MB)
009 CNN Building Blocks Feature Maps, ReLU, Pooling, and Fully Connected Layers.mp4 (16.4 MB)
010 Understanding Softmax Activation and Cross-Entropy Loss in Deep Learning.mp4 (67.3 MB)
002 Step 1 - Convolutional Neural Networks Explained Image Classification Tutorial.mp4 (27.8 MB)
003 Step 2 - Deep Learning Preprocessing Scaling --& Transforming Images for CNNs.mp4 (67.42 MB)
004 Step 3 - Building CNN Architecture Convolutional Layers --& Max Pooling Explained.mp4 (68.02 MB)
005 Step 4 - Train CNN for Image Classification Optimize with Keras --& TensorFlow.mp4 (27.99 MB)
006 Step 5 - Deploying a CNN for Real-World Image Recognition.mp4 (56.64 MB)
007 Develop an Image Recognition System Using Convolutional Neural Networks.mp4 (87.16 MB)
002 How Do Recurrent Neural Networks --(RNNs--) Work Deep Learning Explained.mp4 (6.88 MB)
003 What is a Recurrent Neural Network --(RNN--) Deep Learning for Sequential Data.mp4 (43.59 MB)
004 Understanding the Vanishing Gradient Problem in Recurrent Neural Networks --(RNNs--).mp4 (54.89 MB)
005 Understanding Long Short-Term Memory --(LSTM--) Architecture for Deep Learning.mp4 (75.06 MB)
006 How LSTMs Work in Practice Visualizing Neural Network Predictions.mp4 (64.06 MB)
007 LSTM Variations Peepholes, Combined Gates, and GRUs in Deep Learning.mp4 (13.76 MB)
002 Step 1 - Building a Robust LSTM Neural Network for Stock Price Trend Prediction.mp4 (24.65 MB)
003 Step 2 - Importing Training Data for LSTM Stock Price Prediction Model.mp4 (26.81 MB)
004 Step 3 - Applying Min-Max Normalization for Time Series Data in Neural Networks.mp4 (22.62 MB)
005 Step 4 - Building X train and y train Arrays for LSTM Time Series Forecasting.mp4 (57.77 MB)
006 Step 5 - Preparing Time Series Data for LSTM Neural Network in Stock Forecasting.mp4 (41.4 MB)
007 Step 6 - Create RNN Architecture Sequential Layers vs Computational Graphs.mp4 (10.81 MB)
008 Step 7 - Adding First LSTM Layer Key Components for Stock Market Prediction.mp4 (33.05 MB)
009 Step 8 - Implementing Dropout Regularization in LSTM Networks for Forecasting.mp4 (20.26 MB)
010 Step 9 - Finalizing RNN Architecture Dense Layer for Stock Price Forecasting.mp4 (12.68 MB)
011 Step 10 - Compile RNN with Adam Optimizer for Stock Price Prediction in Python.mp4 (16.56 MB)
012 Step 11 - Optimizing Epochs and Batch Size for LSTM Stock Price Forecasting.mp4 (41.48 MB)
013 Step 12 - Visualizing LSTM Predictions Real vs Forecasted Google Stock Prices.mp4 (21.26 MB)
014 Step 13 - Preparing Historical Stock Data for LSTM Model Scaling and Reshaping.mp4 (64.1 MB)
015 Step 14 - Creating 3D Input Structure for LSTM Stock Price Prediction in Python.mp4 (31.64 MB)
016 Step 15 - Visualizing LSTM Predictions Plotting Real vs Predicted Stock Prices.mp4 (34.33 MB)
001 How Do Self-Organizing Maps Work Understanding SOM in Deep Learning.mp4 (9.5 MB)
002 Self-Organizing Maps --(SOM--) Unsupervised Deep Learning for Dimensionality Reduct.mp4 (32.54 MB)
003 Why K-Means Clustering is Essential for Understanding Self-Organizing Maps.mp4 (7.14 MB)
004 Self-Organizing Maps Tutorial Dimensionality Reduction in Machine Learning.mp4 (53.79 MB)
005 How Self-Organizing Maps --(SOMs--) Learn Unsupervised Deep Learning Explained.mp4 (38.91 MB)
006 How to Create a Self-Organizing Map --(SOM--) in DL Step-by-Step Tutorial.mp4 (25.35 MB)
007 Interpreting SOM Clusters Unsupervised Learning Techniques for Data Analysis.mp4 (16.99 MB)
008 Understanding K-Means Clustering Intuitive Explanation with Visual Examples.mp4 (54.69 MB)
009 K-Means Clustering Avoiding the Random Initialization Trap in Machine Learning.mp4 (29.66 MB)
010 How to Find the Optimal Number of Clusters in K-Means WCSS and Elbow Method.mp4 (43.17 MB)
002 Step 1 - Implementing Self-Organizing Maps --(SOMs--) for Fraud Detection in Python.mp4 (52 MB)
003 Step 2 - SOM Weight Initialization and Training Tutorial for Anomaly Detection.mp4 (36.64 MB)
004 Step 3 - SOM Visualization Techniques Colorbar --& Markers for Outlier Detection.mp4 (64.29 MB)
005 Step 4 - Catching Cheaters with SOMs Mapping Winning Nodes to Customer Data.mp4 (44.87 MB)
002 Step 1 - Building a Hybrid Deep Learning Model for Credit Card Fraud Detection.mp4 (10.74 MB)
003 Step 2 - Developing a Fraud Detection System Using Self-Organizing Maps.mp4 (17.78 MB)
004 Step 3 - Building a Hybrid Model From Unsupervised to Supervised Deep Learning.mp4 (55.65 MB)
005 Step 4 - Implementing Fraud Detection with SOM A Deep Learning Approach.mp4 (35.35 MB)
001 Understanding Boltzmann Machines Deep Learning Fundamentals for AI Enthusiasts.mp4 (6.5 MB)
002 Boltzmann Machines vs Neural Networks Key Differences in Deep Learning.mp4 (54.47 MB)
003 Deep Learning Fundamentals Energy-Based Models --& Their Role in Neural Networks.mp4 (40.46 MB)
004 How to Edit Wikipedia Adding Boltzmann Distribution in Deep Learning.mp4 (13.32 MB)
005 How Restricted Boltzmann Machines Work Deep Learning for Recommender Systems.mp4 (47.56 MB)
006 How Energy-Based Models Work Deep Dive into Contrastive Divergence Algorithm.mp4 (59.23 MB)
007 Deep Belief Networks Understanding RBM Stacking in Deep Learning Models.mp4 (20.47 MB)
008 Deep Boltzmann Machines vs Deep Belief Networks Key Differences Explained.mp4 (11.2 MB)
002 Step 0 - Building a Movie Recommender System with RBMs Data Preprocessing Guide.mp4 (34.79 MB)
004 Step 1 - Importing Movie Datasets for RBM-Based Recommender Systems in Python.mp4 (35.1 MB)
005 Step 2 - Preparing Training and Test Sets for Restricted Boltzmann Machine.mp4 (36.67 MB)
006 Step 3 - Preparing Data for RBM Calculating Total Users and Movies in Python.mp4 (31.89 MB)
007 Step 4 - Convert Training --& Test Sets to RBM-Ready Arrays in Python.mp4 (79.37 MB)
008 Step 5 - Converting NumPy Arrays to PyTorch Tensors for Deep Learning Models.mp4 (19.37 MB)
009 Step 6 - RBM Data Preprocessing Transforming Movie Ratings for Neural Networks.mp4 (29.11 MB)
010 Step 7 - Implementing Restricted Boltzmann Machine Class Structure in PyTorch.mp4 (38.87 MB)
011 Step 8 - RBM Hidden Layer Sampling Bernoulli Distribution in PyTorch Tutorial.mp4 (48.35 MB)
012 Step 9 - RBM Visible Node Sampling Bernoulli Distribution in Deep Learning.mp4 (23.88 MB)
013 Step 10 - RBM Training Function Updating Weights and Biases with Gibbs Sampling.mp4 (44.32 MB)
014 Step 11 - How to Set Up an RBM Model Choosing NV, NH, and Batch Size Parameters.mp4 (27.04 MB)
015 Step 12 - RBM Training Loop Epoch Setup and Loss Function Implementation.mp4 (51.02 MB)
016 Step 13 - RBM Training Updating Weights and Biases with Contrastive Divergence.mp4 (73.76 MB)
017 Step 14 - Optimizing RBM Models From Training to Test Set Performance Analysis.mp4 (65.13 MB)
001 Deep Learning Autoencoders Types, Architecture, and Training Explained.mp4 (8.34 MB)
002 Autoencoders in Machine Learning Applications and Architecture Overview.mp4 (25.64 MB)
003 Autoencoder Bias in Deep Learning Improving Neural Network Performance.mp4 (4.8 MB)
004 How to Train an Autoencoder Step-by-Step Guide for Deep Learning Beginners.mp4 (23.48 MB)
005 How to Use Overcomplete Hidden Layers in Autoencoders for Feature Extraction.mp4 (14.77 MB)
006 Sparse Autoencoders in Deep Learning Preventing Overfitting in Neural Networks.mp4 (23.69 MB)
007 Denoising Autoencoders Deep Learning Regularization Technique Explained.mp4 (9.65 MB)
008 What are Contractive Autoencoders Deep Learning Regularization Techniques.mp4 (9.08 MB)
009 What are Stacked Autoencoders in Deep Learning Architecture and Applications.mp4 (7.21 MB)
010 Deep Autoencoders vs Stacked Autoencoders Key Differences in Neural Networks.mp4 (7.07 MB)
003 Step 1 - Building a Movie Recommendation System with AutoEncoders Data Import.mp4 (41.61 MB)
004 Step 2 - Preparing Training and Test Sets for Autoencoder Recommendation System.mp4 (40.63 MB)
005 Step 3 - Preparing Data for Recommendation Systems User --& Movie Count in Python.mp4 (28.89 MB)
007 Step 4 - Prepare Data for Autoencoder Creating User-Movie Rating Matrices.mp4 (72.17 MB)
008 Step 5 - Convert Training and Test Sets to PyTorch Tensors for Deep Learning.mp4 (17.56 MB)
009 Step 6 - Building Autoencoder Architecture Class Creation for Neural Networks.mp4 (58.44 MB)
010 Step 7 - Python Autoencoder Tutorial Implementing Activation Functions --& Layers.mp4 (47.82 MB)
011 Step 8 - PyTorch Techniques for Efficient Autoencoder Training on Large Datasets.mp4 (51.87 MB)
012 Step 9 - Implementing Stochastic Gradient Descent in Autoencoder Architecture.mp4 (46.64 MB)
013 Step 10 - Machine Learning Metrics Interpreting Loss in Autoencoder Training.mp4 (15.25 MB)
014 Step 11 - How to Evaluate Recommender System Performance Using Test Set Loss.mp4 (40.13 MB)
015 THANK YOU Video.mp4 (9.18 MB)
002 Simple Linear Regression Understanding Y = B0 + B1X in Machine Learning.mp4 (16.29 MB)
003 Linear Regression Explained Finding the Best Fitting Line for Data Analysis.mp4 (10.86 MB)
004 Multiple Linear Regression - Understanding Dependent --& Independent Variables.mp4 (3.33 MB)
005 Understanding Logistic Regression Intuition and Probability in Classification.mp4 (58.02 MB)
002 How to Scale Features in Machine Learning Normalization vs Standardization.mp4 (5.29 MB)
003 Machine Learning Basics Using Train-Test Split to Evaluate Model Performance.mp4 (7.02 MB)
004 Machine Learning Workflow Data Splitting, Feature Scaling, and Model Training.mp4 (16.54 MB)
001 Step 1 - Data Preprocessing in Python Essential Tools for ML Models.mp4 (18.4 MB)
002 Step 2 - How to Handle Missing Data in Python Data Preprocessing Techniques.mp4 (18.44 MB)
003 Step 1 - Importing Essential Python Libraries for Data Preprocessing --& Analysis.mp4 (12.25 MB)
004 Step 1 - Creating a DataFrame from CSV Python Data Preprocessing Basics.mp4 (17.97 MB)
005 Step 2 - Pandas DataFrame Indexing Building Feature Matrix X with iloc Method.mp4 (16.19 MB)
006 Step 3 - Preprocessing Data Extracting Features and Target Variables in Python.mp4 (19.83 MB)
008 Step 1 - Handling Missing Data in Python SimpleImputer for Data Preprocessing.mp4 (20.39 MB)
009 Step 2 - Preprocessing Datasets Fit and Transform to Handle Missing Values.mp4 (20.54 MB)
010 Step 1 - Preprocessing Categorical Variables One-Hot Encoding in Python.mp4 (15.17 MB)
011 Step 2 - Using fit transform Method for Efficient Data Preprocessing in Python.mp4 (20.27 MB)
012 Step 3 - Preprocessing Categorical Data One-Hot and Label Encoding Techniques.mp4 (16.03 MB)
013 Step 1 - Machine Learning Data Prep Splitting Dataset Before Feature Scaling.mp4 (13.46 MB)
014 Step 2 - Split Data into Train --& Test Sets with Scikit-learn--'s train test split.mp4 (20.58 MB)
015 Step 3 - Preparing Data for ML Splitting Datasets with Python and Scikit-learn.mp4 (13.33 MB)
016 Step 1 - How to Apply Feature Scaling for Preprocessing Machine Learning Data.mp4 (20.45 MB)
017 Step 2 - Feature Scaling in Machine Learning When to Apply StandardScaler.mp4 (16.34 MB)
018 Step 3 - Normalizing Data with Fit and Transform Methods in Scikit-learn.mp4 (13.1 MB)
019 Step 4 - How to Apply Feature Scaling to Training --& Test Sets in ML.mp4 (20.17 MB)
001 Understanding the Logistic Regression Equation A Step-by-Step Guide.mp4 (16.91 MB)
002 How to Calculate Maximum Likelihood in Logistic Regression Step-by-Step Guide.mp4 (9.66 MB)
003 Step 1a - Machine Learning Classification Logistic Regression in Python.mp4 (19.65 MB)
004 Step 1b - Logistic Regression Analysis Importing Libraries and Splitting Data.mp4 (13.71 MB)
005 Step 2a - Data Preprocessing for Logistic Regression Importing and Splitting.mp4 (20.11 MB)
006 Step 2b - Data Preprocessing Feature Scaling for Machine Learning in Python.mp4 (20.46 MB)
007 Step 3a - Implementing Logistic Regression for Classification with Scikit-Learn.mp4 (13.67 MB)
008 Step 3b - Predicting Purchase Decisions with Logistic Regression in Python.mp4 (12.03 MB)
009 Step 4a - Using Classifier Objects to Make Predictions in Machine Learning.mp4 (20.56 MB)
010 Step 4b - Evaluating Logistic Regression Model Predicted vs Real Outcomes.mp4 (6.27 MB)
011 Step 5 - Evaluating Machine Learning Models Confusion Matrix and Accuracy.mp4 (20.42 MB)
012 Step 6a - Creating a Confusion Matrix for Machine Learning Model Evaluation.mp4 (20.2 MB)
013 Step 6b - Visualizing Machine Learning Results Training vs Test Set Comparison.mp4 (11.46 MB)
014 Step 7a - Visualizing Logistic Regression 2D Plots for Classification Models.mp4 (20.29 MB)
015 Step 7b - Visualizing Logistic Regression Interpreting Classification Results.mp4 (12.84 MB)
016 Step 7c - Visualizing Test Results Assessing Machine Learning Model Accuracy.mp4 (11.48 MB)]
Screenshot