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


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Creat Time:   2025-07-01
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Last Active:   2025-07-01
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File List

  1. 06. CNN Intuition/8. How Do Fully Connected Layers Work in Convolutional Neural Networks (CNNs).mp4 183.32 MB
  2. 07. Building a CNN/7. Develop an Image Recognition System Using Convolutional Neural Networks.mp4 154.14 MB
  3. 09. RNN Intuition/6. How LSTMs Work in Practice Visualizing Neural Network Predictions.mp4 152.03 MB
  4. 18. Building a Boltzmann Machine/16. Step 13 - RBM Training Updating Weights and Biases with Contrastive Divergence.mp4 144.11 MB
  5. 17. Boltzmann Machine Intuition/5. How Restricted Boltzmann Machines Work Deep Learning for Recommender Systems.mp4 142.46 MB
  6. 13. SOMs Intuition/8. Understanding K-Means Clustering Intuitive Explanation with Visual Examples.mp4 126.26 MB
  7. 10. Building a RNN/14. Step 13 - Preparing Historical Stock Data for LSTM Model Scaling and Reshaping.mp4 125.89 MB
  8. 07. Building a CNN/3. Step 2 - Deep Learning Preprocessing Scaling & Transforming Images for CNNs.mp4 109.86 MB
  9. 09. RNN Intuition/3. What is a Recurrent Neural Network (RNN) Deep Learning for Sequential Data.mp4 108.21 MB
  10. 21. Building an AutoEncoder/7. Step 4 - Prepare Data for Autoencoder Creating User-Movie Rating Matrices.mp4 104.04 MB
  11. 10. Building a RNN/5. Step 4 - Building X_train and y_train Arrays for LSTM Time Series Forecasting.mp4 103.75 MB
  12. 18. Building a Boltzmann Machine/7. Step 4 - Convert Training & Test Sets to RBM-Ready Arrays in Python.mp4 103.32 MB
  13. 18. Building a Boltzmann Machine/13. Step 10 - RBM Training Function Updating Weights and Biases with Gibbs Sampling.mp4 99.28 MB
  14. 21. Building an AutoEncoder/3. Step 1 - Building a Movie Recommendation System with AutoEncoders Data Import.mp4 99.07 MB
  15. 13. SOMs Intuition/5. How Self-Organizing Maps (SOMs) Learn Unsupervised Deep Learning Explained.mp4 98.95 MB
  16. 06. CNN Intuition/6. Understanding Spatial Invariance in CNNs Max Pooling Explained for Beginners.mp4 90.10 MB
  17. 17. Boltzmann Machine Intuition/2. Boltzmann Machines vs. Neural Networks Key Differences in Deep Learning.mp4 87.33 MB
  18. 06. CNN Intuition/4. How to Apply Convolution Filters in Neural Networks Feature Detection Explained.mp4 85.48 MB
  19. 20. AutoEncoders Intuition/2. Autoencoders in Machine Learning Applications and Architecture Overview.mp4 84.20 MB
  20. 10. Building a RNN/6. Step 5 - Preparing Time Series Data for LSTM Neural Network in Stock Forecasting.mp4 80.38 MB
  21. 04. Building an ANN/7. Step 5 - How to Make Predictions and Evaluate Neural Network Model in Python.mp4 80.13 MB
  22. 18. Building a Boltzmann Machine/4. Step 1 - Importing Movie Datasets for RBM-Based Recommender Systems in Python.mp4 80.02 MB
  23. 07. Building a CNN/6. Step 5 - Deploying a CNN for Real-World Image Recognition.mp4 76.76 MB
  24. 15. Mega Case Study/4. Step 3 - Building a Hybrid Model From Unsupervised to Supervised Deep Learning.mp4 76.05 MB
  25. 18. Building a Boltzmann Machine/17. Step 14 - Optimizing RBM Models From Training to Test Set Performance Analysis.mp4 75.00 MB
  26. 06. CNN Intuition/3. How Do Convolutional Neural Networks Work Understanding CNN Architecture.mp4 74.24 MB
  27. 21. Building an AutoEncoder/9. Step 6 - Building Autoencoder Architecture Class Creation for Neural Networks.mp4 72.54 MB
  28. 21. Building an AutoEncoder/11. Step 8 - PyTorch Techniques for Efficient Autoencoder Training on Large Datasets.mp4 72.12 MB
  29. 03. ANN Intuition/5. How Do Neural Networks Work Step-by-Step Guide to Property Valuation Example.mp4 71.20 MB
  30. 17. Boltzmann Machine Intuition/6. How Energy-Based Models Work Deep Dive into Contrastive Divergence Algorithm.mp4 68.93 MB
  31. 07. Building a CNN/4. Step 3 - Building CNN Architecture Convolutional Layers & Max Pooling Explained.mp4 66.84 MB
  32. 09. RNN Intuition/5. Understanding Long Short-Term Memory (LSTM) Architecture for Deep Learning.mp4 66.83 MB
  33. 04. Building an ANN/4. Step 2 - Data Preprocessing for Neural Networks Essential Steps and Techniques.mp4 65.30 MB
  34. 10. Building a RNN/15. Step 14 - Creating 3D Input Structure for LSTM Stock Price Prediction in Python.mp4 64.12 MB
  35. 18. Building a Boltzmann Machine/9. Step 6 - RBM Data Preprocessing Transforming Movie Ratings for Neural Networks.mp4 62.55 MB
  36. 21. Building an AutoEncoder/10. Step 7 - Python Autoencoder Tutorial Implementing Activation Functions & Layers.mp4 60.06 MB
  37. 10. Building a RNN/16. Step 15 - Visualizing LSTM Predictions Plotting Real vs Predicted Stock Prices.mp4 59.53 MB
  38. 04. Building an ANN/2. Step 1 - Data Preprocessing for Deep Learning Preparing Neural Network Dataset.mp4 58.72 MB
  39. 14. Building a SOM/3. Step 2 - SOM Weight Initialization and Training Tutorial for Anomaly Detection.mp4 58.25 MB
  40. 14. Building a SOM/4. Step 3 - SOM Visualization Techniques Colorbar & Markers for Outlier Detection.mp4 57.93 MB
  41. 18. Building a Boltzmann Machine/15. Step 12 - RBM Training Loop Epoch Setup and Loss Function Implementation.mp4 57.69 MB
  42. 13. SOMs Intuition/6. How to Create a Self-Organizing Map (SOM) in DL Step-by-Step Tutorial.mp4 57.62 MB
  43. 14. Building a SOM/5. Step 4 - Catching Cheaters with SOMs Mapping Winning Nodes to Customer Data.mp4 56.62 MB
  44. 18. Building a Boltzmann Machine/11. Step 8 - RBM Hidden Layer Sampling Bernoulli Distribution in PyTorch Tutorial.mp4 53.99 MB
  45. 10. Building a RNN/13. Step 12 - Visualizing LSTM Predictions Real vs Forecasted Google Stock Prices.mp4 53.77 MB
  46. 21. Building an AutoEncoder/15. THANK YOU Video.mp4 53.58 MB
  47. 06. CNN Intuition/10. Understanding Softmax Activation and Cross-Entropy Loss in Deep Learning.mp4 53.35 MB
  48. 03. ANN Intuition/6. How Do Neural Networks Learn Understanding Backpropagation and Cost Functions.mp4 52.81 MB
  49. 21. Building an AutoEncoder/14. Step 11 - How to Evaluate Recommender System Performance Using Test Set Loss.mp4 50.20 MB
  50. 14. Building a SOM/2. Step 1 - Implementing Self-Organizing Maps (SOMs) for Fraud Detection in Python.mp4 49.05 MB

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