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Category:
Language:
English
Total Size:
1.8 GB
Info Hash:
AD3F47E9AA6BF9084D2D7E77062D9A0DD0A4A4A7
Added By:
Added:
Aug. 25, 2024, 1:07 p.m.
Stats:
|
(Last updated: Nov. 29, 2025, 4:54 a.m.)
| File | Size |
|---|---|
| $10 ChatGPT for 1 Year & More.txt | 252 bytes |
| 2. What you should know.srt | 908 bytes |
| 3. Other RL algorithms.srt | 916 bytes |
| 1. Extending your deep learning education.srt | 1.0 KB |
| description.html | 1.0 KB |
| description.html | 1.1 KB |
| 5. Challenge Manually tune hyperparameters.srt | 1.1 KB |
| description.html | 1.1 KB |
| description.html | 1.1 KB |
| 1. Next steps.srt | 1.2 KB |
| 6. Challenge Build a neural network.srt | 1.2 KB |
| 1. Next steps.srt | 1.2 KB |
| description.html | 1.2 KB |
| 3. Building the RCA model.srt | 1.2 KB |
| description.html | 1.2 KB |
| description.html | 1.3 KB |
| 1. Neural networks 101 Your path to AI brilliance.srt | 1.3 KB |
| 1. Explore the capabilities of PyTorch.srt | 1.4 KB |
| 5. Challenge Resize a picture.srt | 1.4 KB |
| 5. Challenge Removing color.srt | 1.4 KB |
| 5. Monte Carlo control.srt | 1.4 KB |
| 3. Using the exercise files.srt | 1.4 KB |
| 6. Solution Removing color.srt | 1.5 KB |
| 1. Reinforcement learning in a nutshell.srt | 1.5 KB |
| 4. Predicting root causes with deep learning.srt | 1.5 KB |
| 1. Getting started with deep learning.srt | 1.5 KB |
| 2. Preprocessing RCA data.srt | 1.5 KB |
| 1. Introduction.srt | 1.5 KB |
| 2. What you should know.srt | 1.6 KB |
| 1. Installing Anaconda and OpenCV.srt | 1.7 KB |
| 2. Multi-agent reinforcement learning.srt | 1.7 KB |
| 7. Solution Convolution filters.srt | 1.7 KB |
| 4. Challenge Stitch two pictures together.srt | 1.7 KB |
| 3. Inverse reinforcement learning.srt | 1.8 KB |
| 1. Next steps.srt | 1.8 KB |
| 6. Solution Resize a picture.srt | 1.8 KB |
| 2. Temporal difference methods.srt | 1.8 KB |
| 1. The setting.srt | 1.8 KB |
| 1. Continuing your PyTorch learning process.srt | 1.9 KB |
| 5. Solution Stitch two pictures together.srt | 1.9 KB |
| 2. Torchvision for video and image understanding.srt | 1.9 KB |
| 2. Weighted grayscale.srt | 1.9 KB |
| 6. Challenge Convolution filters.srt | 1.9 KB |
| 5. Saving and loading models.srt | 1.9 KB |
| 5. Solution Help a robot.srt | 2.0 KB |
| 3. Building a spam model.srt | 2.0 KB |
| 3. How to use the challenge exercise files.srt | 2.1 KB |
| 1. Computer vision under the hood.srt | 2.1 KB |
| 1. The setting.srt | 2.1 KB |
| 2. Forward propagation.srt | 2.1 KB |
| 2. What you should know.srt | 2.1 KB |
| 1. Deep reinforcement learning.srt | 2.2 KB |
| 1. The Iris classification problem.srt | 2.2 KB |
| 4. Predictions for text.srt | 2.2 KB |
| 5. Gradient descent.srt | 2.4 KB |
| 6. Predictions with deep learning models.srt | 2.4 KB |
| 6. Solution Manually tune hyperparameters.srt | 2.5 KB |
| 3. Artificial neural networks.srt | 2.5 KB |
| 4. Expected SARSA.srt | 2.5 KB |
| 7. Validation and testing.srt | 2.6 KB |
| 4. The perceptron.srt | 2.6 KB |
| 1. Next steps.srt | 2.6 KB |
| 3. Monte Carlo prediction.srt | 2.7 KB |
| 4. First visit and every visit MC prediction.srt | 2.7 KB |
| 1. What is deep learning.srt | 2.7 KB |
| 5. The output layer.srt | 2.7 KB |
| 3. Image upscaling methods.srt | 2.8 KB |
| 3. Open and close.srt | 2.8 KB |
| 1. Your reinforcement learning journey.srt | 2.8 KB |
| 4. Data preprocessing.srt | 2.8 KB |
| 2. Hidden layers.srt | 2.8 KB |
| 1. Spam classification problem.srt | 2.8 KB |
| 5. Rotations and flips.srt | 2.9 KB |
| 4. Gaussian filters.srt | 2.9 KB |
| 8. An ANN model.srt | 3.0 KB |
| 2. Creating text representations.srt | 3.0 KB |
| 5. Advanced PyTorch autograd.srt | 3.1 KB |
| 3. Orthogonal matrix.srt | 3.2 KB |
| 3. SARSAMAX (Q-learning).srt | 3.2 KB |
| 1. Matrices changing basis.srt | 3.2 KB |
| 1. Image downscaling methods.srt | 3.3 KB |
| 1. Welcome.srt | 3.3 KB |
| 1. Defining linear algebra.srt | 3.5 KB |
| 4. Challenge Help a robot.srt | 3.5 KB |
| 2. Biological neural networks.srt | 3.5 KB |
| 2. Exploration and exploitation.srt | 3.5 KB |
| 4. Activation functions.srt | 3.5 KB |
| 4. A basic RL solution.srt | 3.5 KB |
| 3. PyTorch use case description.srt | 3.6 KB |
| 3. Setting up the environment.srt | 3.6 KB |
| 2. Transforming to the new basis.srt | 3.6 KB |
| 6. Challenge Manipulate some pictures.srt | 3.7 KB |
| 10. Using available open-source models.srt | 3.7 KB |
| 1. Terms in reinforcement learning.srt | 3.7 KB |
| 3. Data checks and data preparation.srt | 3.7 KB |
| 7. Solution Manipulate some pictures.srt | 3.7 KB |
| 3. Measuring accuracy and error.srt | 3.8 KB |
| 2. Understand PyTorch basic operations.srt | 3.8 KB |
| 1. Exercise problem statement.srt | 3.8 KB |
| 4. Back propagation.srt | 3.8 KB |
| 1. Matrices introduction.srt | 3.9 KB |
| 6. Batches and epochs.srt | 3.9 KB |
| 9. Reusing existing network architectures.srt | 3.9 KB |
| 3. Inverse and determinant.srt | 3.9 KB |
| 4. Gram–Schmidt process.srt | 4.0 KB |
| 1. Introduction to eigenvalues and eigenvectors.srt | 4.0 KB |
| 4. Basis, linear independence, and span.srt | 4.0 KB |
| 3. Creating a deep learning model.srt | 4.0 KB |
| 2. Layers Input, hidden, and output.srt | 4.0 KB |
| 6. Training an ANN.srt | 4.0 KB |
| 3. Converting grayscale to black and white.srt | 4.1 KB |
| 4. Understand PyTorch autograd.srt | 4.1 KB |
| 2. Prerequisites for the course.srt | 4.1 KB |
| 2. Linear regression.srt | 4.2 KB |
| 6. Additional modifications.srt | 4.2 KB |
| 2. Average filters.srt | 4.2 KB |
| 2. Input preprocessing.srt | 4.2 KB |
| 3. Coordinate system.srt | 4.2 KB |
| 2. Color encoding.srt | 4.2 KB |
| 3. Weights and biases.srt | 4.2 KB |
| 2. Types of matrices.srt | 4.3 KB |
| 4. Composition or combination of matrix transformations.srt | 4.3 KB |
| 5. Artificial neural networks.srt | 4.3 KB |
| 4. Training and evaluation.srt | 4.3 KB |
| 3. Types of matrix transformation.srt | 4.3 KB |
| 2. Calculating eigenvalues and eigenvectors.srt | 4.4 KB |
| 2. Gaussian elimination and finding the inverse matrix.srt | 4.4 KB |
| 3. An analogy for deep learning.srt | 4.4 KB |
| 1. Dot product of vectors.srt | 4.4 KB |
| 2. Hyperparameters and neural networks.srt | 4.5 KB |
| 4. Resolution.srt | 4.5 KB |
| 1. The input layer.srt | 4.6 KB |
| 2. Downscaling example.srt | 4.6 KB |
| 1. Monte Carlo method.srt | 4.7 KB |
| 3. Cuts in panoramic photography.srt | 4.8 KB |
| 1. Torchaudio introduction.srt | 4.8 KB |
| 1. Setup and initialization.srt | 4.8 KB |
| 3. Understand PyTorch NumPy Bridge.srt | 4.8 KB |
| 2. Scalar and vector projection.srt | 4.9 KB |
| 3. Transfer and activation functions.srt | 5.0 KB |
| 4. Upscaling example.srt | 5.0 KB |
| 1. Understand PyTorch tensors.srt | 5.0 KB |
| 1. Torchtext introduction.srt | 5.0 KB |
| 1. Average grayscale.srt | 5.0 KB |
| 3. Self-supervised learning.srt | 5.2 KB |
| 4. Single-layer perceptron.srt | 5.3 KB |
| 2. Erosion and dilation.srt | 5.3 KB |
| 2. Torchaudio for audio understanding.srt | 5.4 KB |
| 4. PyTorch data exploration.srt | 5.5 KB |
| 2. PyTorch environment setup.srt | 5.5 KB |
| 7. Solution Build a neural network.srt | 5.5 KB |
| 2. Torchtext for translation.srt | 5.5 KB |
| 2. Testing your environment.srt | 5.5 KB |
| 2. Vector arithmetic.srt | 5.5 KB |
| 2. Foundation models.srt | 5.5 KB |
| 3. Transformer architecture.srt | 5.6 KB |
| 3. Changing to the eigenbasis.srt | 5.7 KB |
| 3. How do you improve model performance.srt | 5.7 KB |
| 1. Generative AI.srt | 5.8 KB |
| 1. PyTorch overview.srt | 5.8 KB |
| 1. Image cuts.srt | 5.8 KB |
| 1. Image representation.srt | 5.8 KB |
| 1. Multilayer perceptron.srt | 5.8 KB |
| 1. The Keras Sequential model.srt | 5.9 KB |
| 5. Edge detection filters.srt | 6.0 KB |
| 4. Google PageRank algorithm.srt | 6.0 KB |
| 1. Machine learning and neural networks.srt | 6.0 KB |
| 1. Solving linear equations using Gaussian elimination.srt | 6.1 KB |
| 1. Convolution filters.srt | 6.3 KB |
| 3. Changing basis of vectors.srt | 6.4 KB |
| 2. A basic RL problem.srt | 6.7 KB |
| 2. SARSA.srt | 6.8 KB |
| 4. How neural networks learn.srt | 6.8 KB |
| 1. Introduction to vectors.srt | 6.9 KB |
| 3. Median filters.srt | 6.9 KB |
| 3. The Internet of Things.srt | 6.9 KB |
| 3. Markov decision process.srt | 7.0 KB |
| 4. Adaptive thresholding.srt | 7.2 KB |
| 2. Use case and determine evaluation metric.srt | 7.2 KB |
| 1. Overfitting and underfitting Two common ANN problems.srt | 7.4 KB |
| 1. Why modify objects.srt | 7.4 KB |
| 4. Backpropagation.srt | 7.6 KB |
| 1. Big data.srt | 7.6 KB |
| 2. Applications of linear algebra in ML.srt | 7.6 KB |
| 2. Artificial neural networks.srt | 7.9 KB |
| 2. The history of AI.srt | 7.9 KB |
| 2. Data science.srt | 8.0 KB |
| 2. Data vs. reasoning.srt | 8.1 KB |
| 1. Robotics.srt | 8.1 KB |
| 3. Unsupervised learning.srt | 8.1 KB |
| 1. Match patterns.srt | 8.1 KB |
| 2. Natural language processing.srt | 8.2 KB |
| 1. Pitfalls.srt | 8.2 KB |
| 3. Strong vs. weak AI.srt | 8.3 KB |
| 1. Machine learning.srt | 8.3 KB |
| 5. Train the neural network using Keras.srt | 8.4 KB |
| 3. Image file management.srt | 8.4 KB |
| 4. Plan AI.srt | 8.4 KB |
| 1. Define general intelligence.srt | 8.4 KB |
| 3. Perceptrons.srt | 8.5 KB |
| 5. Regression.srt | 8.9 KB |
| 2. Recurrent neural networks (RNN).srt | 9.8 KB |
| 2. Stitching two images together.srt | 9.9 KB |
| 4. Regularization techniques to improve overfitting models.srt | 11.3 KB |
| 1. Torchvision introduction.srt | 12.0 KB |
| 1. Convolutional neural networks (CNN).srt | 12.2 KB |
| Ex_Files_ML_Foundations_Linear_Algebra.zip | 33.3 KB |
| Ex_Files_Deep_Learning_Getting_Started.zip | 103.0 KB |
| 5. Challenge Manually tune hyperparameters.mp4 | 1.1 MB |
| 6. Challenge Build a neural network.mp4 | 1.3 MB |
| 5. Monte Carlo control.mp4 | 1.4 MB |
| 1. Extending your deep learning education.mp4 | 1.5 MB |
| 2. What you should know.mp4 | 1.6 MB |
| 1. Continuing your PyTorch learning process.mp4 | 1.7 MB |
| 2. Multi-agent reinforcement learning.mp4 | 1.8 MB |
| 3. Using the exercise files.mp4 | 1.8 MB |
| 1. Next steps.mp4 | 1.8 MB |
| 1. Installing Anaconda and OpenCV.mp4 | 1.9 MB |
| 3. Inverse reinforcement learning.mp4 | 2.2 MB |
| 2. Temporal difference methods.mp4 | 2.3 MB |
| 3. Monte Carlo prediction.mp4 | 2.4 MB |
| 1. Next steps.mp4 | 2.5 MB |
| 1. Explore the capabilities of PyTorch.mp4 | 2.5 MB |
| 4. The perceptron.mp4 | 2.6 MB |
| 1. Next steps.mp4 | 2.6 MB |
| 1. What is deep learning.mp4 | 2.6 MB |
| 4. Predicting root causes with deep learning.mp4 | 2.7 MB |
| 2. What you should know.mp4 | 2.7 MB |
| 2. Forward propagation.mp4 | 2.8 MB |
| 3. Artificial neural networks.mp4 | 2.9 MB |
| 5. Challenge Removing color.mp4 | 2.9 MB |
| 5. Challenge Resize a picture.mp4 | 2.9 MB |
| 5. Gradient descent.mp4 | 3.0 MB |
| 5. Saving and loading models.mp4 | 3.0 MB |
| 3. Other RL algorithms.mp4 | 3.2 MB |
| 7. Validation and testing.mp4 | 3.2 MB |
| 1. The setting.mp4 | 3.2 MB |
| 5. The output layer.mp4 | 3.4 MB |
| 3. Image upscaling methods.mp4 | 3.5 MB |
| 8. An ANN model.mp4 | 3.5 MB |
| 3. PyTorch use case description.mp4 | 3.6 MB |
| 4. Challenge Stitch two pictures together.mp4 | 3.6 MB |
| 3. Building the RCA model.mp4 | 3.6 MB |
| 1. Reinforcement learning in a nutshell.mp4 | 3.7 MB |
| 4. Data preprocessing.mp4 | 3.7 MB |
| 1. Spam classification problem.mp4 | 3.7 MB |
| 3. How to use the challenge exercise files.mp4 | 3.7 MB |
| 4. Activation functions.mp4 | 3.9 MB |
| 1. Getting started with deep learning.mp4 | 4.0 MB |
| 2. Preprocessing RCA data.mp4 | 4.0 MB |
| 4. Predictions for text.mp4 | 4.1 MB |
| 6. Solution Removing color.mp4 | 4.2 MB |
| 1. Image downscaling methods.mp4 | 4.2 MB |
| 9. Reusing existing network architectures.mp4 | 4.2 MB |
| 1. Next steps.mp4 | 4.3 MB |
| 1. Deep reinforcement learning.mp4 | 4.3 MB |
| 10. Using available open-source models.mp4 | 4.3 MB |
| 1. Neural networks 101 Your path to AI brilliance.mp4 | 4.4 MB |
| 2. Torchvision for video and image understanding.mp4 | 4.5 MB |
| 3. An analogy for deep learning.mp4 | 4.5 MB |
| 6. Additional modifications.mp4 | 4.5 MB |
| 2. Layers Input, hidden, and output.mp4 | 4.5 MB |
| 6. Predictions with deep learning models.mp4 | 4.6 MB |
| 2. Hidden layers.mp4 | 4.6 MB |
| 3. Data checks and data preparation.mp4 | 4.7 MB |
| 1. The Iris classification problem.mp4 | 4.7 MB |
| 3. Measuring accuracy and error.mp4 | 4.7 MB |
| 6. Challenge Convolution filters.mp4 | 4.8 MB |
| 4. Back propagation.mp4 | 4.8 MB |
| 6. Batches and epochs.mp4 | 4.8 MB |
| 2. Prerequisites for the course.mp4 | 4.9 MB |
| 5. Advanced PyTorch autograd.mp4 | 5.0 MB |
| 2. Biological neural networks.mp4 | 5.0 MB |
| 1. The setting.mp4 | 5.2 MB |
| 6. Training an ANN.mp4 | 5.2 MB |
| 3. Building a spam model.mp4 | 5.2 MB |
| 2. What you should know.mp4 | 5.4 MB |
| 4. Understand PyTorch autograd.mp4 | 5.4 MB |
| 2. Linear regression.mp4 | 5.6 MB |
| 3. Weights and biases.mp4 | 5.6 MB |
| 3. Transfer and activation functions.mp4 | 5.7 MB |
| 1. Setup and initialization.mp4 | 5.7 MB |
| 5. Artificial neural networks.mp4 | 5.8 MB |
| 1. Exercise problem statement.mp4 | 5.8 MB |
| 1. The input layer.mp4 | 5.9 MB |
| 2. Hyperparameters and neural networks.mp4 | 6.0 MB |
| 3. Setting up the environment.mp4 | 6.0 MB |
| 6. Solution Manually tune hyperparameters.mp4 | 6.1 MB |
| 6. Solution Resize a picture.mp4 | 6.1 MB |
| 5. Rotations and flips.mp4 | 6.1 MB |
| 1. Your reinforcement learning journey.mp4 | 6.2 MB |
| 2. Weighted grayscale.mp4 | 6.2 MB |
| 7. Solution Convolution filters.mp4 | 6.2 MB |
| 3. How do you improve model performance.mp4 | 6.2 MB |
| 4. Single-layer perceptron.mp4 | 6.4 MB |
| 5. Solution Stitch two pictures together.mp4 | 6.4 MB |
| 1. The Keras Sequential model.mp4 | 6.5 MB |
| 3. Orthogonal matrix.mp4 | 6.6 MB |
| 1. Torchaudio introduction.mp4 | 6.6 MB |
| 1. Multilayer perceptron.mp4 | 6.7 MB |
| 4. First visit and every visit MC prediction.mp4 | 6.8 MB |
| Ex_Files_Hands_On_PyTorch_ML.zip | 6.8 MB |
| 1. Overfitting and underfitting Two common ANN problems.mp4 | 6.9 MB |
| 1. Understand PyTorch tensors.mp4 | 7.0 MB |
| 4. Expected SARSA.mp4 | 7.1 MB |
| 1. Welcome.mp4 | 7.1 MB |
| 3. Open and close.mp4 | 7.1 MB |
| 2. Creating text representations.mp4 | 7.1 MB |
| 6. Challenge Manipulate some pictures.mp4 | 7.2 MB |
| 1. Computer vision under the hood.mp4 | 7.4 MB |
| 1. Matrices changing basis.mp4 | 7.4 MB |
| 2. Understand PyTorch basic operations.mp4 | 7.5 MB |
| 2. Exploration and exploitation.mp4 | 7.7 MB |
| 1. PyTorch overview.mp4 | 7.7 MB |
| 3. Transformer architecture.mp4 | 7.8 MB |
| 1. Torchtext introduction.mp4 | 7.9 MB |
| 5. Solution Help a robot.mp4 | 7.9 MB |
| 2. Color encoding.mp4 | 7.9 MB |
| 3. Creating a deep learning model.mp4 | 8.1 MB |
| 3. Understand PyTorch NumPy Bridge.mp4 | 8.1 MB |
| 4. Gaussian filters.mp4 | 8.2 MB |
| 1. Matrices introduction.mp4 | 8.4 MB |
| 3. Inverse and determinant.mp4 | 8.4 MB |
| 1. Convolution filters.mp4 | 8.5 MB |
| 4. A basic RL solution.mp4 | 8.6 MB |
| 1. Introduction.mp4 | 8.6 MB |
| 4. Resolution.mp4 | 8.8 MB |
| 1. Machine learning and neural networks.mp4 | 8.8 MB |
| 3. Types of matrix transformation.mp4 | 8.9 MB |
| 4. How neural networks learn.mp4 | 8.9 MB |
| 4. Challenge Help a robot.mp4 | 9.0 MB |
| 3. SARSAMAX (Q-learning).mp4 | 9.1 MB |
| 2. Input preprocessing.mp4 | 9.4 MB |
| 4. Training and evaluation.mp4 | 9.4 MB |
| 7. Solution Manipulate some pictures.mp4 | 9.5 MB |
| 2. Types of matrices.mp4 | 9.6 MB |
| 2. Gaussian elimination and finding the inverse matrix.mp4 | 9.7 MB |
| 3. Coordinate system.mp4 | 9.8 MB |
| 2. Use case and determine evaluation metric.mp4 | 9.9 MB |
| 1. Terms in reinforcement learning.mp4 | 10.2 MB |
| 5. Train the neural network using Keras.mp4 | 10.3 MB |
| 1. Introduction to eigenvalues and eigenvectors.mp4 | 10.4 MB |
| 2. The history of AI.mp4 | 10.4 MB |
| 3. Converting grayscale to black and white.mp4 | 10.5 MB |
| 2. Testing your environment.mp4 | 10.6 MB |
| 7. Solution Build a neural network.mp4 | 10.8 MB |
| 1. Average grayscale.mp4 | 10.9 MB |
| 4. Gram–Schmidt process.mp4 | 11.1 MB |
| 1. Defining linear algebra.mp4 | 11.2 MB |
| 2. Average filters.mp4 | 11.4 MB |
| 2. Data vs. reasoning.mp4 | 11.4 MB |
| 2. Downscaling example.mp4 | 11.4 MB |
| 2. Erosion and dilation.mp4 | 11.4 MB |
| 3. Self-supervised learning.mp4 | 11.4 MB |
| 2. Calculating eigenvalues and eigenvectors.mp4 | 11.5 MB |
| 4. Upscaling example.mp4 | 11.7 MB |
| 1. Generative AI.mp4 | 11.7 MB |
| 3. The Internet of Things.mp4 | 11.7 MB |
| 4. Composition or combination of matrix transformations.mp4 | 11.8 MB |
| 4. Regularization techniques to improve overfitting models.mp4 | 11.8 MB |
| 1. Define general intelligence.mp4 | 11.9 MB |
| 4. Basis, linear independence, and span.mp4 | 12.0 MB |
| 1. Image representation.mp4 | 12.1 MB |
| 4. PyTorch data exploration.mp4 | 12.1 MB |
| 1. Monte Carlo method.mp4 | 12.2 MB |
| 1. Pitfalls.mp4 | 12.3 MB |
| 1. Dot product of vectors.mp4 | 12.4 MB |
| 2. Vector arithmetic.mp4 | 12.4 MB |
| 4. Google PageRank algorithm.mp4 | 12.4 MB |
| 3. Cuts in panoramic photography.mp4 | 12.5 MB |
| 2. Foundation models.mp4 | 12.6 MB |
| 1. Big data.mp4 | 12.7 MB |
| 2. Recurrent neural networks (RNN).mp4 | 12.8 MB |
| 4. Backpropagation.mp4 | 13.0 MB |
| 3. Strong vs. weak AI.mp4 | 13.0 MB |
| 2. PyTorch environment setup.mp4 | 13.0 MB |
| 2. Data science.mp4 | 13.1 MB |
| 2. Artificial neural networks.mp4 | 13.1 MB |
| 3. Changing to the eigenbasis.mp4 | 13.2 MB |
| 2. Torchaudio for audio understanding.mp4 | 13.2 MB |
| 5. Regression.mp4 | 13.5 MB |
| 3. Unsupervised learning.mp4 | 13.6 MB |
| 1. Torchvision introduction.mp4 | 13.7 MB |
| 1. Image cuts.mp4 | 13.7 MB |
| 2. Scalar and vector projection.mp4 | 13.8 MB |
| 1. Machine learning.mp4 | 13.8 MB |
| 1. Why modify objects.mp4 | 13.8 MB |
| 4. Plan AI.mp4 | 13.9 MB |
| 3. Perceptrons.mp4 | 14.1 MB |
| 5. Edge detection filters.mp4 | 14.2 MB |
| 1. Robotics.mp4 | 14.2 MB |
| 2. Torchtext for translation.mp4 | 14.3 MB |
| 2. Transforming to the new basis.mp4 | 14.4 MB |
| 2. Natural language processing.mp4 | 14.5 MB |
| 2. A basic RL problem.mp4 | 15.1 MB |
| 2. SARSA.mp4 | 15.2 MB |
| 1. Convolutional neural networks (CNN).mp4 | 15.6 MB |
| 1. Match patterns.mp4 | 15.6 MB |
| 1. Solving linear equations using Gaussian elimination.mp4 | 17.1 MB |
| 3. Changing basis of vectors.mp4 | 17.1 MB |
| 3. Markov decision process.mp4 | 17.4 MB |
| 3. Image file management.mp4 | 19.1 MB |
| 4. Adaptive thresholding.mp4 | 20.9 MB |
| 2. Applications of linear algebra in ML.mp4 | 22.8 MB |
| 3. Median filters.mp4 | 25.4 MB |
| 1. Introduction to vectors.mp4 | 29.9 MB |
| 2. Stitching two images together.mp4 | 44.1 MB |
| Ex_Files_Computer_Vision_Deep_Dive_in_Python.zip | 145.8 MB |
Name
DL
Uploader
Size
S/L
Added
-
947.1 MB
[28
/
9]
2024-08-27
| Uploaded by SunRiseZone | Size 947.1 MB | Health [ 28 /9 ] | Added 2024-08-27 |
-
2.8 GB
[67
/
10]
2024-05-23
| Uploaded by SunRiseZone | Size 2.8 GB | Health [ 67 /10 ] | Added 2024-05-23 |
-
3.4 GB
[49
/
12]
2024-05-20
| Uploaded by SunRiseZone | Size 3.4 GB | Health [ 49 /12 ] | Added 2024-05-20 |
NOTE
SOURCE: LinkedIn Learning Getting Started with AI and Machine Learning
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COVER
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MEDIAINFO
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