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LinkedIn Learning | Getting Started With AI And Ma...
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LinkedIn Learning | Getting Started With AI And Machine Learning - Complete 7 Courses Torrent content (File list)
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| $10 ChatGPT for 1 Year & More.txt 0.2 KB |
| Artificial Intelligence Foundations Neural Networks/0 - Introduction/2. What you should know.srt 0.9 KB |
| Reinforcement Learning Foundations/2 - Reinforcement Learning Algorithms/3. Other RL algorithms.srt 0.9 KB |
| Deep Learning Getting Started/7 - Conclusion/1. Extending your deep learning education.srt 1.0 KB |
| Reinforcement Learning Foundations/description.html 1.0 KB |
| Hands-On PyTorch Machine Learning/description.html 1.1 KB |
| Artificial Intelligence Foundations Neural Networks/5 - Best Practices for Optimizing a Neural Network/5. Challenge Manually tune hyperparameters.srt 1.1 KB |
| Machine Learning Foundations Linear Algebra/description.html 1.1 KB |
| Building Computer Vision Applications with Python/description.html 1.1 KB |
| Machine Learning Foundations Linear Algebra/8 - Conclusion/1. Next steps.srt 1.2 KB |
| Artificial Intelligence Foundations Neural Networks/4 - Build a Simple Neural Network Using Keras/6. Challenge Build a neural network.srt 1.2 KB |
| Building Computer Vision Applications with Python/8 - Conclusion/1. Next steps.srt 1.2 KB |
| Deep Learning Getting Started/description.html 1.2 KB |
| Deep Learning Getting Started/6 - Deep Learning Exercise/3. Building the RCA model.srt 1.2 KB |
| Artificial Intelligence Foundations Neural Networks/description.html 1.2 KB |
| Artificial Intelligence Foundations Thinking Machines/description.html 1.3 KB |
| Artificial Intelligence Foundations Neural Networks/0 - Introduction/1. Neural networks 101 Your path to AI brilliance.srt 1.3 KB |
| Hands-On PyTorch Machine Learning/0 - Introduction/1. Explore the capabilities of PyTorch.srt 1.4 KB |
| Building Computer Vision Applications with Python/5 - Image Scaling/5. Challenge Resize a picture.srt 1.4 KB |
| Building Computer Vision Applications with Python/3 - From Color to Black and White/5. Challenge Removing color.srt 1.4 KB |
| Reinforcement Learning Foundations/3 - Monte Carlo Method/5. Monte Carlo control.srt 1.4 KB |
| Building Computer Vision Applications with Python/0 - Introduction/3. Using the exercise files.srt 1.4 KB |
| Building Computer Vision Applications with Python/3 - From Color to Black and White/6. Solution Removing color.srt 1.5 KB |
| Reinforcement Learning Foundations/0 - Introduction/1. Reinforcement learning in a nutshell.srt 1.5 KB |
| Deep Learning Getting Started/6 - Deep Learning Exercise/4. Predicting root causes with deep learning.srt 1.5 KB |
| Deep Learning Getting Started/0 - Introduction/1. Getting started with deep learning.srt 1.5 KB |
| Deep Learning Getting Started/6 - Deep Learning Exercise/2. Preprocessing RCA data.srt 1.5 KB |
| Machine Learning Foundations Linear Algebra/0 - Introduction/1. Introduction.srt 1.5 KB |
| Machine Learning Foundations Linear Algebra/0 - Introduction/2. What you should know.srt 1.6 KB |
| Building Computer Vision Applications with Python/1 - Setting Up Your Environment/1. Installing Anaconda and OpenCV.srt 1.7 KB |
| Reinforcement Learning Foundations/5 - Modified Forms of Reinforcement/2. Multi-agent reinforcement learning.srt 1.7 KB |
| Building Computer Vision Applications with Python/4 - Filters/7. Solution Convolution filters.srt 1.7 KB |
| Building Computer Vision Applications with Python/6 - Fun with Cuts/4. Challenge Stitch two pictures together.srt 1.7 KB |
| Reinforcement Learning Foundations/5 - Modified Forms of Reinforcement/3. Inverse reinforcement learning.srt 1.8 KB |
| Artificial Intelligence Foundations Thinking Machines/8 - Conclusion/1. Next steps.srt 1.8 KB |
| Building Computer Vision Applications with Python/5 - Image Scaling/6. Solution Resize a picture.srt 1.8 KB |
| Reinforcement Learning Foundations/2 - Reinforcement Learning Algorithms/2. Temporal difference methods.srt 1.8 KB |
| Reinforcement Learning Foundations/4 - Temporal Difference Methods/1. The setting.srt 1.8 KB |
| Hands-On PyTorch Machine Learning/6 - Conclusion/1. Continuing your PyTorch learning process.srt 1.9 KB |
| Building Computer Vision Applications with Python/6 - Fun with Cuts/5. Solution Stitch two pictures together.srt 1.9 KB |
| Hands-On PyTorch Machine Learning/3 - Torchvision/2. Torchvision for video and image understanding.srt 1.9 KB |
| Building Computer Vision Applications with Python/3 - From Color to Black and White/2. Weighted grayscale.srt 1.9 KB |
| Building Computer Vision Applications with Python/4 - Filters/6. Challenge Convolution filters.srt 1.9 KB |
| Deep Learning Getting Started/4 - Deep Learning Example 1/5. Saving and loading models.srt 1.9 KB |
| Building Computer Vision Applications with Python/7 - Morphological Modifications/5. Solution Help a robot.srt 2.0 KB |
| Deep Learning Getting Started/5 - Deep Learning Example 2/3. Building a spam model.srt 2.0 KB |
| Artificial Intelligence Foundations Neural Networks/0 - Introduction/3. How to use the challenge exercise files.srt 2.1 KB |
| Building Computer Vision Applications with Python/0 - Introduction/1. Computer vision under the hood.srt 2.1 KB |
| Reinforcement Learning Foundations/3 - Monte Carlo Method/1. The setting.srt 2.1 KB |
| Deep Learning Getting Started/3 - Training a Neural Network/2. Forward propagation.srt 2.1 KB |
| Building Computer Vision Applications with Python/0 - Introduction/2. What you should know.srt 2.1 KB |
| Reinforcement Learning Foundations/5 - Modified Forms of Reinforcement/1. Deep reinforcement learning.srt 2.2 KB |
| Deep Learning Getting Started/4 - Deep Learning Example 1/1. The Iris classification problem.srt 2.2 KB |
| Deep Learning Getting Started/5 - Deep Learning Example 2/4. Predictions for text.srt 2.2 KB |
| Deep Learning Getting Started/3 - Training a Neural Network/5. Gradient descent.srt 2.4 KB |
| Deep Learning Getting Started/4 - Deep Learning Example 1/6. Predictions with deep learning models.srt 2.4 KB |
| Artificial Intelligence Foundations Neural Networks/5 - Best Practices for Optimizing a Neural Network/6. Solution Manually tune hyperparameters.srt 2.5 KB |
| Artificial Intelligence Foundations Neural Networks/1 - What Are Neural Networks/3. Artificial neural networks.srt 2.5 KB |
| Reinforcement Learning Foundations/4 - Temporal Difference Methods/4. Expected SARSA.srt 2.5 KB |
| Deep Learning Getting Started/3 - Training a Neural Network/7. Validation and testing.srt 2.6 KB |
| Deep Learning Getting Started/1 - Introduction to Deep Learning/4. The perceptron.srt 2.6 KB |
| Artificial Intelligence Foundations Neural Networks/6 - Conclusion/1. Next steps.srt 2.6 KB |
| Reinforcement Learning Foundations/3 - Monte Carlo Method/3. Monte Carlo prediction.srt 2.7 KB |
| Reinforcement Learning Foundations/3 - Monte Carlo Method/4. First visit and every visit MC prediction.srt 2.7 KB |
| Deep Learning Getting Started/1 - Introduction to Deep Learning/1. What is deep learning.srt 2.7 KB |
| Deep Learning Getting Started/2 - Neural Network Architecture/5. The output layer.srt 2.7 KB |
| Building Computer Vision Applications with Python/5 - Image Scaling/3. Image upscaling methods.srt 2.8 KB |
| Building Computer Vision Applications with Python/7 - Morphological Modifications/3. Open and close.srt 2.8 KB |
| Reinforcement Learning Foundations/6 - Conclusion/1. Your reinforcement learning journey.srt 2.8 KB |
| Artificial Intelligence Foundations Neural Networks/4 - Build a Simple Neural Network Using Keras/4. Data preprocessing.srt 2.8 KB |
| Deep Learning Getting Started/2 - Neural Network Architecture/2. Hidden layers.srt 2.8 KB |
| Deep Learning Getting Started/5 - Deep Learning Example 2/1. Spam classification problem.srt 2.8 KB |
| Building Computer Vision Applications with Python/2 - The Basics of Image Processing/5. Rotations and flips.srt 2.9 KB |
| Building Computer Vision Applications with Python/4 - Filters/4. Gaussian filters.srt 2.9 KB |
| Deep Learning Getting Started/3 - Training a Neural Network/8. An ANN model.srt 3.0 KB |
| Deep Learning Getting Started/5 - Deep Learning Example 2/2. Creating text representations.srt 3.0 KB |
| Hands-On PyTorch Machine Learning/2 - PyTorch Basics/5. Advanced PyTorch autograd.srt 3.1 KB |
| Machine Learning Foundations Linear Algebra/6 - Matrices from Orthogonality to Gram–Schmidt Process/3. Orthogonal matrix.srt 3.2 KB |
| Reinforcement Learning Foundations/4 - Temporal Difference Methods/3. SARSAMAX (Q-learning).srt 3.2 KB |
| Machine Learning Foundations Linear Algebra/6 - Matrices from Orthogonality to Gram–Schmidt Process/1. Matrices changing basis.srt 3.2 KB |
| Building Computer Vision Applications with Python/5 - Image Scaling/1. Image downscaling methods.srt 3.3 KB |
| Artificial Intelligence Foundations Thinking Machines/0 - Introduction/1. Welcome.srt 3.3 KB |
| Machine Learning Foundations Linear Algebra/1 - Introduction to Linear Algebra/1. Defining linear algebra.srt 3.5 KB |
| Building Computer Vision Applications with Python/7 - Morphological Modifications/4. Challenge Help a robot.srt 3.5 KB |
| Artificial Intelligence Foundations Neural Networks/1 - What Are Neural Networks/2. Biological neural networks.srt 3.5 KB |
| Reinforcement Learning Foundations/3 - Monte Carlo Method/2. Exploration and exploitation.srt 3.5 KB |
| Deep Learning Getting Started/2 - Neural Network Architecture/4. Activation functions.srt 3.5 KB |
| Reinforcement Learning Foundations/1 - Getting Started with Reinforcement Learning/4. A basic RL solution.srt 3.5 KB |
| Hands-On PyTorch Machine Learning/1 - Preparation/3. PyTorch use case description.srt 3.6 KB |
| Deep Learning Getting Started/0 - Introduction/3. Setting up the environment.srt 3.6 KB |
| Machine Learning Foundations Linear Algebra/6 - Matrices from Orthogonality to Gram–Schmidt Process/2. Transforming to the new basis.srt 3.6 KB |
| Building Computer Vision Applications with Python/2 - The Basics of Image Processing/6. Challenge Manipulate some pictures.srt 3.7 KB |
| Deep Learning Getting Started/3 - Training a Neural Network/10. Using available open-source models.srt 3.7 KB |
| Reinforcement Learning Foundations/1 - Getting Started with Reinforcement Learning/1. Terms in reinforcement learning.srt 3.7 KB |
| Artificial Intelligence Foundations Neural Networks/4 - Build a Simple Neural Network Using Keras/3. Data checks and data preparation.srt 3.7 KB |
| Building Computer Vision Applications with Python/2 - The Basics of Image Processing/7. Solution Manipulate some pictures.srt 3.7 KB |
| Deep Learning Getting Started/3 - Training a Neural Network/3. Measuring accuracy and error.srt 3.8 KB |
| Hands-On PyTorch Machine Learning/2 - PyTorch Basics/2. Understand PyTorch basic operations.srt 3.8 KB |
| Deep Learning Getting Started/6 - Deep Learning Exercise/1. Exercise problem statement.srt 3.8 KB |
| Deep Learning Getting Started/3 - Training a Neural Network/4. Back propagation.srt 3.8 KB |
| Machine Learning Foundations Linear Algebra/4 - Introduction to Matrices/1. Matrices introduction.srt 3.9 KB |
| Deep Learning Getting Started/3 - Training a Neural Network/6. Batches and epochs.srt 3.9 KB |
| Deep Learning Getting Started/3 - Training a Neural Network/9. Reusing existing network architectures.srt 3.9 KB |
| Machine Learning Foundations Linear Algebra/5 - Gaussian Elimination/3. Inverse and determinant.srt 3.9 KB |
| Machine Learning Foundations Linear Algebra/6 - Matrices from Orthogonality to Gram–Schmidt Process/4. Gram–Schmidt process.srt 4.0 KB |
| Machine Learning Foundations Linear Algebra/7 - Eigenvalues and Eigenvectors/1. Introduction to eigenvalues and eigenvectors.srt 4.0 KB |
| Machine Learning Foundations Linear Algebra/3 - Vector Projections and Basis/4. Basis, linear independence, and span.srt 4.0 KB |
| Deep Learning Getting Started/4 - Deep Learning Example 1/3. Creating a deep learning model.srt 4.0 KB |
| Artificial Intelligence Foundations Neural Networks/2 - Key Components in Neural Network Architecture/2. Layers Input, hidden, and output.srt 4.0 KB |
| Deep Learning Getting Started/1 - Introduction to Deep Learning/6. Training an ANN.srt 4.0 KB |
| Building Computer Vision Applications with Python/3 - From Color to Black and White/3. Converting grayscale to black and white.srt 4.1 KB |
| Hands-On PyTorch Machine Learning/2 - PyTorch Basics/4. Understand PyTorch autograd.srt 4.1 KB |
| Deep Learning Getting Started/0 - Introduction/2. Prerequisites for the course.srt 4.1 KB |
| Deep Learning Getting Started/1 - Introduction to Deep Learning/2. Linear regression.srt 4.2 KB |
| Reinforcement Learning Foundations/3 - Monte Carlo Method/6. Additional modifications.srt 4.2 KB |
| Building Computer Vision Applications with Python/4 - Filters/2. Average filters.srt 4.2 KB |
| Deep Learning Getting Started/4 - Deep Learning Example 1/2. Input preprocessing.srt 4.2 KB |
| Machine Learning Foundations Linear Algebra/2 - Vectors Basics/3. Coordinate system.srt 4.2 KB |
| Building Computer Vision Applications with Python/2 - The Basics of Image Processing/2. Color encoding.srt 4.2 KB |
| Deep Learning Getting Started/2 - Neural Network Architecture/3. Weights and biases.srt 4.2 KB |
| Machine Learning Foundations Linear Algebra/4 - Introduction to Matrices/2. Types of matrices.srt 4.3 KB |
| Machine Learning Foundations Linear Algebra/4 - Introduction to Matrices/4. Composition or combination of matrix transformations.srt 4.3 KB |
| Deep Learning Getting Started/1 - Introduction to Deep Learning/5. Artificial neural networks.srt 4.3 KB |
| Deep Learning Getting Started/4 - Deep Learning Example 1/4. Training and evaluation.srt 4.3 KB |
| Machine Learning Foundations Linear Algebra/4 - Introduction to Matrices/3. Types of matrix transformation.srt 4.3 KB |
| Machine Learning Foundations Linear Algebra/7 - Eigenvalues and Eigenvectors/2. Calculating eigenvalues and eigenvectors.srt 4.4 KB |
| Machine Learning Foundations Linear Algebra/5 - Gaussian Elimination/2. Gaussian elimination and finding the inverse matrix.srt 4.4 KB |
| Deep Learning Getting Started/1 - Introduction to Deep Learning/3. An analogy for deep learning.srt 4.4 KB |
| Machine Learning Foundations Linear Algebra/3 - Vector Projections and Basis/1. Dot product of vectors.srt 4.4 KB |
| Artificial Intelligence Foundations Neural Networks/5 - Best Practices for Optimizing a Neural Network/2. Hyperparameters and neural networks.srt 4.5 KB |
| Building Computer Vision Applications with Python/2 - The Basics of Image Processing/4. Resolution.srt 4.5 KB |
| Deep Learning Getting Started/2 - Neural Network Architecture/1. The input layer.srt 4.6 KB |
| Building Computer Vision Applications with Python/5 - Image Scaling/2. Downscaling example.srt 4.6 KB |
| Reinforcement Learning Foundations/2 - Reinforcement Learning Algorithms/1. Monte Carlo method.srt 4.7 KB |
| Building Computer Vision Applications with Python/6 - Fun with Cuts/3. Cuts in panoramic photography.srt 4.8 KB |
| Hands-On PyTorch Machine Learning/4 - Torchaudio/1. Torchaudio introduction.srt 4.8 KB |
| Deep Learning Getting Started/3 - Training a Neural Network/1. Setup and initialization.srt 4.8 KB |
| Hands-On PyTorch Machine Learning/2 - PyTorch Basics/3. Understand PyTorch NumPy Bridge.srt 4.8 KB |
| Machine Learning Foundations Linear Algebra/3 - Vector Projections and Basis/2. Scalar and vector projection.srt 4.9 KB |
| Artificial Intelligence Foundations Neural Networks/2 - Key Components in Neural Network Architecture/3. Transfer and activation functions.srt 5.0 KB |
| Building Computer Vision Applications with Python/5 - Image Scaling/4. Upscaling example.srt 5.0 KB |
| Hands-On PyTorch Machine Learning/2 - PyTorch Basics/1. Understand PyTorch tensors.srt 5.0 KB |
| Hands-On PyTorch Machine Learning/5 - Torchtext/1. Torchtext introduction.srt 5.0 KB |
| Building Computer Vision Applications with Python/3 - From Color to Black and White/1. Average grayscale.srt 5.0 KB |
| Artificial Intelligence Foundations Thinking Machines/6 - What Has Changed/3. Self-supervised learning.srt 5.2 KB |
| Artificial Intelligence Foundations Neural Networks/1 - What Are Neural Networks/4. Single-layer perceptron.srt 5.3 KB |
| Building Computer Vision Applications with Python/7 - Morphological Modifications/2. Erosion and dilation.srt 5.3 KB |
| Hands-On PyTorch Machine Learning/4 - Torchaudio/2. Torchaudio for audio understanding.srt 5.4 KB |
| Hands-On PyTorch Machine Learning/1 - Preparation/4. PyTorch data exploration.srt 5.5 KB |
| Hands-On PyTorch Machine Learning/1 - Preparation/2. PyTorch environment setup.srt 5.5 KB |
| Artificial Intelligence Foundations Neural Networks/4 - Build a Simple Neural Network Using Keras/7. Solution Build a neural network.srt 5.5 KB |
| Hands-On PyTorch Machine Learning/5 - Torchtext/2. Torchtext for translation.srt 5.5 KB |
| Building Computer Vision Applications with Python/1 - Setting Up Your Environment/2. Testing your environment.srt 5.5 KB |
| Machine Learning Foundations Linear Algebra/2 - Vectors Basics/2. Vector arithmetic.srt 5.5 KB |
| Artificial Intelligence Foundations Thinking Machines/6 - What Has Changed/2. Foundation models.srt 5.5 KB |
| Artificial Intelligence Foundations Neural Networks/3 - Other Types of Neural Networks/3. Transformer architecture.srt 5.6 KB |
| Machine Learning Foundations Linear Algebra/7 - Eigenvalues and Eigenvectors/3. Changing to the eigenbasis.srt 5.7 KB |
| Artificial Intelligence Foundations Neural Networks/5 - Best Practices for Optimizing a Neural Network/3. How do you improve model performance.srt 5.7 KB |
| Artificial Intelligence Foundations Thinking Machines/6 - What Has Changed/1. Generative AI.srt 5.8 KB |
| Hands-On PyTorch Machine Learning/1 - Preparation/1. PyTorch overview.srt 5.8 KB |
| Building Computer Vision Applications with Python/6 - Fun with Cuts/1. Image cuts.srt 5.8 KB |
| Building Computer Vision Applications with Python/2 - The Basics of Image Processing/1. Image representation.srt 5.8 KB |
| Artificial Intelligence Foundations Neural Networks/2 - Key Components in Neural Network Architecture/1. Multilayer perceptron.srt 5.8 KB |
| Artificial Intelligence Foundations Neural Networks/4 - Build a Simple Neural Network Using Keras/1. The Keras Sequential model.srt 5.9 KB |
| Building Computer Vision Applications with Python/4 - Filters/5. Edge detection filters.srt 6.0 KB |
| Machine Learning Foundations Linear Algebra/7 - Eigenvalues and Eigenvectors/4. Google PageRank algorithm.srt 6.0 KB |
| Artificial Intelligence Foundations Neural Networks/1 - What Are Neural Networks/1. Machine learning and neural networks.srt 6.0 KB |
| Machine Learning Foundations Linear Algebra/5 - Gaussian Elimination/1. Solving linear equations using Gaussian elimination.srt 6.1 KB |
| Building Computer Vision Applications with Python/4 - Filters/1. Convolution filters.srt 6.3 KB |
| Machine Learning Foundations Linear Algebra/3 - Vector Projections and Basis/3. Changing basis of vectors.srt 6.4 KB |
| Reinforcement Learning Foundations/1 - Getting Started with Reinforcement Learning/2. A basic RL problem.srt 6.7 KB |
| Reinforcement Learning Foundations/4 - Temporal Difference Methods/2. SARSA.srt 6.8 KB |
| Artificial Intelligence Foundations Neural Networks/2 - Key Components in Neural Network Architecture/4. How neural networks learn.srt 6.8 KB |
| Machine Learning Foundations Linear Algebra/2 - Vectors Basics/1. Introduction to vectors.srt 6.9 KB |
| Building Computer Vision Applications with Python/4 - Filters/3. Median filters.srt 6.9 KB |
| Artificial Intelligence Foundations Thinking Machines/4 - Common AI Programs/3. The Internet of Things.srt 6.9 KB |
| Reinforcement Learning Foundations/1 - Getting Started with Reinforcement Learning/3. Markov decision process.srt 7.0 KB |
| Building Computer Vision Applications with Python/3 - From Color to Black and White/4. Adaptive thresholding.srt 7.2 KB |
| Artificial Intelligence Foundations Neural Networks/4 - Build a Simple Neural Network Using Keras/2. Use case and determine evaluation metric.srt 7.2 KB |
| Artificial Intelligence Foundations Neural Networks/5 - Best Practices for Optimizing a Neural Network/1. Overfitting and underfitting Two common ANN problems.srt 7.4 KB |
| Building Computer Vision Applications with Python/7 - Morphological Modifications/1. Why modify objects.srt 7.4 KB |
| Artificial Intelligence Foundations Thinking Machines/3 - Finding the Right Approach/4. Backpropagation.srt 7.6 KB |
| Artificial Intelligence Foundations Thinking Machines/5 - Mixing with Other Technologies/1. Big data.srt 7.6 KB |
| Machine Learning Foundations Linear Algebra/1 - Introduction to Linear Algebra/2. Applications of linear algebra in ML.srt 7.6 KB |
| Artificial Intelligence Foundations Thinking Machines/2 - The Rise of Machine Learning/2. Artificial neural networks.srt 7.9 KB |
| Artificial Intelligence Foundations Thinking Machines/1 - What Is Artificial Intelligence/2. The history of AI.srt 7.9 KB |
| Artificial Intelligence Foundations Thinking Machines/5 - Mixing with Other Technologies/2. Data science.srt 8.0 KB |
| Artificial Intelligence Foundations Thinking Machines/3 - Finding the Right Approach/2. Data vs. reasoning.srt 8.1 KB |
| Artificial Intelligence Foundations Thinking Machines/4 - Common AI Programs/1. Robotics.srt 8.1 KB |
| Artificial Intelligence Foundations Thinking Machines/3 - Finding the Right Approach/3. Unsupervised learning.srt 8.1 KB |
| Artificial Intelligence Foundations Thinking Machines/3 - Finding the Right Approach/1. Match patterns.srt 8.1 KB |
| Artificial Intelligence Foundations Thinking Machines/4 - Common AI Programs/2. Natural language processing.srt 8.2 KB |
| Artificial Intelligence Foundations Thinking Machines/7 - Avoiding Pitfalls/1. Pitfalls.srt 8.2 KB |
| Artificial Intelligence Foundations Thinking Machines/1 - What Is Artificial Intelligence/3. Strong vs. weak AI.srt 8.3 KB |
| Artificial Intelligence Foundations Thinking Machines/2 - The Rise of Machine Learning/1. Machine learning.srt 8.3 KB |
| Artificial Intelligence Foundations Neural Networks/4 - Build a Simple Neural Network Using Keras/5. Train the neural network using Keras.srt 8.4 KB |
| Building Computer Vision Applications with Python/2 - The Basics of Image Processing/3. Image file management.srt 8.4 KB |
| Artificial Intelligence Foundations Thinking Machines/1 - What Is Artificial Intelligence/4. Plan AI.srt 8.4 KB |
| Artificial Intelligence Foundations Thinking Machines/1 - What Is Artificial Intelligence/1. Define general intelligence.srt 8.4 KB |
| Artificial Intelligence Foundations Thinking Machines/2 - The Rise of Machine Learning/3. Perceptrons.srt 8.5 KB |
| Artificial Intelligence Foundations Thinking Machines/3 - Finding the Right Approach/5. Regression.srt 8.9 KB |
| Artificial Intelligence Foundations Neural Networks/3 - Other Types of Neural Networks/2. Recurrent neural networks (RNN).srt 9.8 KB |
| Building Computer Vision Applications with Python/6 - Fun with Cuts/2. Stitching two images together.srt 9.9 KB |
| Artificial Intelligence Foundations Neural Networks/5 - Best Practices for Optimizing a Neural Network/4. Regularization techniques to improve overfitting models.srt 11.3 KB |
| Hands-On PyTorch Machine Learning/3 - Torchvision/1. Torchvision introduction.srt 12.0 KB |
| Artificial Intelligence Foundations Neural Networks/3 - Other Types of Neural Networks/1. Convolutional neural networks (CNN).srt 12.2 KB |
| Machine Learning Foundations Linear Algebra/Ex_Files_ML_Foundations_Linear_Algebra.zip 33.3 KB |
| Deep Learning Getting Started/Ex_Files_Deep_Learning_Getting_Started.zip 103.0 KB |
| Artificial Intelligence Foundations Neural Networks/5 - Best Practices for Optimizing a Neural Network/5. Challenge Manually tune hyperparameters.mp4 1.1 MB |
| Artificial Intelligence Foundations Neural Networks/4 - Build a Simple Neural Network Using Keras/6. Challenge Build a neural network.mp4 1.3 MB |
| Reinforcement Learning Foundations/3 - Monte Carlo Method/5. Monte Carlo control.mp4 1.4 MB |
| Deep Learning Getting Started/7 - Conclusion/1. Extending your deep learning education.mp4 1.5 MB |
| Artificial Intelligence Foundations Neural Networks/0 - Introduction/2. What you should know.mp4 1.6 MB |
| Hands-On PyTorch Machine Learning/6 - Conclusion/1. Continuing your PyTorch learning process.mp4 1.7 MB |
| Reinforcement Learning Foundations/5 - Modified Forms of Reinforcement/2. Multi-agent reinforcement learning.mp4 1.8 MB |
| Building Computer Vision Applications with Python/0 - Introduction/3. Using the exercise files.mp4 1.8 MB |
| Building Computer Vision Applications with Python/8 - Conclusion/1. Next steps.mp4 1.8 MB |
| Building Computer Vision Applications with Python/1 - Setting Up Your Environment/1. Installing Anaconda and OpenCV.mp4 1.9 MB |
| Reinforcement Learning Foundations/5 - Modified Forms of Reinforcement/3. Inverse reinforcement learning.mp4 2.2 MB |
| Reinforcement Learning Foundations/2 - Reinforcement Learning Algorithms/2. Temporal difference methods.mp4 2.3 MB |
| Reinforcement Learning Foundations/3 - Monte Carlo Method/3. Monte Carlo prediction.mp4 2.4 MB |
| Machine Learning Foundations Linear Algebra/8 - Conclusion/1. Next steps.mp4 2.5 MB |
| Hands-On PyTorch Machine Learning/0 - Introduction/1. Explore the capabilities of PyTorch.mp4 2.5 MB |
| Deep Learning Getting Started/1 - Introduction to Deep Learning/4. The perceptron.mp4 2.6 MB |
| Artificial Intelligence Foundations Neural Networks/6 - Conclusion/1. Next steps.mp4 2.6 MB |
| Deep Learning Getting Started/1 - Introduction to Deep Learning/1. What is deep learning.mp4 2.6 MB |
| Deep Learning Getting Started/6 - Deep Learning Exercise/4. Predicting root causes with deep learning.mp4 2.7 MB |
| Building Computer Vision Applications with Python/0 - Introduction/2. What you should know.mp4 2.7 MB |
| Deep Learning Getting Started/3 - Training a Neural Network/2. Forward propagation.mp4 2.8 MB |
| Artificial Intelligence Foundations Neural Networks/1 - What Are Neural Networks/3. Artificial neural networks.mp4 2.9 MB |
| Building Computer Vision Applications with Python/3 - From Color to Black and White/5. Challenge Removing color.mp4 2.9 MB |
| Building Computer Vision Applications with Python/5 - Image Scaling/5. Challenge Resize a picture.mp4 2.9 MB |
| Deep Learning Getting Started/3 - Training a Neural Network/5. Gradient descent.mp4 3.0 MB |
| Deep Learning Getting Started/4 - Deep Learning Example 1/5. Saving and loading models.mp4 3.0 MB |
| Reinforcement Learning Foundations/2 - Reinforcement Learning Algorithms/3. Other RL algorithms.mp4 3.2 MB |
| Deep Learning Getting Started/3 - Training a Neural Network/7. Validation and testing.mp4 3.2 MB |
| Reinforcement Learning Foundations/3 - Monte Carlo Method/1. The setting.mp4 3.2 MB |
| Deep Learning Getting Started/2 - Neural Network Architecture/5. The output layer.mp4 3.4 MB |
| Building Computer Vision Applications with Python/5 - Image Scaling/3. Image upscaling methods.mp4 3.5 MB |
| Deep Learning Getting Started/3 - Training a Neural Network/8. An ANN model.mp4 3.5 MB |
| Hands-On PyTorch Machine Learning/1 - Preparation/3. PyTorch use case description.mp4 3.6 MB |
| Building Computer Vision Applications with Python/6 - Fun with Cuts/4. Challenge Stitch two pictures together.mp4 3.6 MB |
| Deep Learning Getting Started/6 - Deep Learning Exercise/3. Building the RCA model.mp4 3.6 MB |
| Reinforcement Learning Foundations/0 - Introduction/1. Reinforcement learning in a nutshell.mp4 3.7 MB |
| Artificial Intelligence Foundations Neural Networks/4 - Build a Simple Neural Network Using Keras/4. Data preprocessing.mp4 3.7 MB |
| Deep Learning Getting Started/5 - Deep Learning Example 2/1. Spam classification problem.mp4 3.7 MB |
| Artificial Intelligence Foundations Neural Networks/0 - Introduction/3. How to use the challenge exercise files.mp4 3.7 MB |
| Deep Learning Getting Started/2 - Neural Network Architecture/4. Activation functions.mp4 3.9 MB |
| Deep Learning Getting Started/0 - Introduction/1. Getting started with deep learning.mp4 4.0 MB |
| Deep Learning Getting Started/6 - Deep Learning Exercise/2. Preprocessing RCA data.mp4 4.0 MB |
| Deep Learning Getting Started/5 - Deep Learning Example 2/4. Predictions for text.mp4 4.1 MB |
| Building Computer Vision Applications with Python/3 - From Color to Black and White/6. Solution Removing color.mp4 4.2 MB |
| Building Computer Vision Applications with Python/5 - Image Scaling/1. Image downscaling methods.mp4 4.2 MB |
| Deep Learning Getting Started/3 - Training a Neural Network/9. Reusing existing network architectures.mp4 4.2 MB |
| Artificial Intelligence Foundations Thinking Machines/8 - Conclusion/1. Next steps.mp4 4.3 MB |
| Reinforcement Learning Foundations/5 - Modified Forms of Reinforcement/1. Deep reinforcement learning.mp4 4.3 MB |
| Deep Learning Getting Started/3 - Training a Neural Network/10. Using available open-source models.mp4 4.3 MB |
| Artificial Intelligence Foundations Neural Networks/0 - Introduction/1. Neural networks 101 Your path to AI brilliance.mp4 4.4 MB |
| Hands-On PyTorch Machine Learning/3 - Torchvision/2. Torchvision for video and image understanding.mp4 4.5 MB |
| Deep Learning Getting Started/1 - Introduction to Deep Learning/3. An analogy for deep learning.mp4 4.5 MB |
| Reinforcement Learning Foundations/3 - Monte Carlo Method/6. Additional modifications.mp4 4.5 MB |
| Artificial Intelligence Foundations Neural Networks/2 - Key Components in Neural Network Architecture/2. Layers Input, hidden, and output.mp4 4.5 MB |
| Deep Learning Getting Started/4 - Deep Learning Example 1/6. Predictions with deep learning models.mp4 4.6 MB |
| Deep Learning Getting Started/2 - Neural Network Architecture/2. Hidden layers.mp4 4.6 MB |
| Artificial Intelligence Foundations Neural Networks/4 - Build a Simple Neural Network Using Keras/3. Data checks and data preparation.mp4 4.7 MB |
| Deep Learning Getting Started/4 - Deep Learning Example 1/1. The Iris classification problem.mp4 4.7 MB |
| Deep Learning Getting Started/3 - Training a Neural Network/3. Measuring accuracy and error.mp4 4.7 MB |
| Building Computer Vision Applications with Python/4 - Filters/6. Challenge Convolution filters.mp4 4.8 MB |
| Deep Learning Getting Started/3 - Training a Neural Network/4. Back propagation.mp4 4.8 MB |
| Deep Learning Getting Started/3 - Training a Neural Network/6. Batches and epochs.mp4 4.8 MB |
| Deep Learning Getting Started/0 - Introduction/2. Prerequisites for the course.mp4 4.9 MB |
| Hands-On PyTorch Machine Learning/2 - PyTorch Basics/5. Advanced PyTorch autograd.mp4 5.0 MB |
| Artificial Intelligence Foundations Neural Networks/1 - What Are Neural Networks/2. Biological neural networks.mp4 5.0 MB |
| Reinforcement Learning Foundations/4 - Temporal Difference Methods/1. The setting.mp4 5.2 MB |
| Deep Learning Getting Started/1 - Introduction to Deep Learning/6. Training an ANN.mp4 5.2 MB |
| Deep Learning Getting Started/5 - Deep Learning Example 2/3. Building a spam model.mp4 5.2 MB |
| Machine Learning Foundations Linear Algebra/0 - Introduction/2. What you should know.mp4 5.4 MB |
| Hands-On PyTorch Machine Learning/2 - PyTorch Basics/4. Understand PyTorch autograd.mp4 5.4 MB |
| Deep Learning Getting Started/1 - Introduction to Deep Learning/2. Linear regression.mp4 5.6 MB |
| Deep Learning Getting Started/2 - Neural Network Architecture/3. Weights and biases.mp4 5.6 MB |
| Artificial Intelligence Foundations Neural Networks/2 - Key Components in Neural Network Architecture/3. Transfer and activation functions.mp4 5.7 MB |
| Deep Learning Getting Started/3 - Training a Neural Network/1. Setup and initialization.mp4 5.7 MB |
| Deep Learning Getting Started/1 - Introduction to Deep Learning/5. Artificial neural networks.mp4 5.8 MB |
| Deep Learning Getting Started/6 - Deep Learning Exercise/1. Exercise problem statement.mp4 5.8 MB |
| Deep Learning Getting Started/2 - Neural Network Architecture/1. The input layer.mp4 5.9 MB |
| Artificial Intelligence Foundations Neural Networks/5 - Best Practices for Optimizing a Neural Network/2. Hyperparameters and neural networks.mp4 6.0 MB |
| Deep Learning Getting Started/0 - Introduction/3. Setting up the environment.mp4 6.0 MB |
| Artificial Intelligence Foundations Neural Networks/5 - Best Practices for Optimizing a Neural Network/6. Solution Manually tune hyperparameters.mp4 6.1 MB |
| Building Computer Vision Applications with Python/5 - Image Scaling/6. Solution Resize a picture.mp4 6.1 MB |
| Building Computer Vision Applications with Python/2 - The Basics of Image Processing/5. Rotations and flips.mp4 6.1 MB |
| Reinforcement Learning Foundations/6 - Conclusion/1. Your reinforcement learning journey.mp4 6.2 MB |
| Building Computer Vision Applications with Python/3 - From Color to Black and White/2. Weighted grayscale.mp4 6.2 MB |
| Building Computer Vision Applications with Python/4 - Filters/7. Solution Convolution filters.mp4 6.2 MB |
| Artificial Intelligence Foundations Neural Networks/5 - Best Practices for Optimizing a Neural Network/3. How do you improve model performance.mp4 6.2 MB |
| Artificial Intelligence Foundations Neural Networks/1 - What Are Neural Networks/4. Single-layer perceptron.mp4 6.4 MB |
| Building Computer Vision Applications with Python/6 - Fun with Cuts/5. Solution Stitch two pictures together.mp4 6.4 MB |
| Artificial Intelligence Foundations Neural Networks/4 - Build a Simple Neural Network Using Keras/1. The Keras Sequential model.mp4 6.5 MB |
| Machine Learning Foundations Linear Algebra/6 - Matrices from Orthogonality to Gram–Schmidt Process/3. Orthogonal matrix.mp4 6.6 MB |
| Hands-On PyTorch Machine Learning/4 - Torchaudio/1. Torchaudio introduction.mp4 6.6 MB |
| Artificial Intelligence Foundations Neural Networks/2 - Key Components in Neural Network Architecture/1. Multilayer perceptron.mp4 6.7 MB |
| Reinforcement Learning Foundations/3 - Monte Carlo Method/4. First visit and every visit MC prediction.mp4 6.8 MB |
| Hands-On PyTorch Machine Learning/Ex_Files_Hands_On_PyTorch_ML.zip 6.8 MB |
| Artificial Intelligence Foundations Neural Networks/5 - Best Practices for Optimizing a Neural Network/1. Overfitting and underfitting Two common ANN problems.mp4 6.9 MB |
| Hands-On PyTorch Machine Learning/2 - PyTorch Basics/1. Understand PyTorch tensors.mp4 7.0 MB |
| Reinforcement Learning Foundations/4 - Temporal Difference Methods/4. Expected SARSA.mp4 7.1 MB |
| Artificial Intelligence Foundations Thinking Machines/0 - Introduction/1. Welcome.mp4 7.1 MB |
| Building Computer Vision Applications with Python/7 - Morphological Modifications/3. Open and close.mp4 7.1 MB |
| Deep Learning Getting Started/5 - Deep Learning Example 2/2. Creating text representations.mp4 7.1 MB |
| Building Computer Vision Applications with Python/2 - The Basics of Image Processing/6. Challenge Manipulate some pictures.mp4 7.2 MB |
| Building Computer Vision Applications with Python/0 - Introduction/1. Computer vision under the hood.mp4 7.4 MB |
| Machine Learning Foundations Linear Algebra/6 - Matrices from Orthogonality to Gram–Schmidt Process/1. Matrices changing basis.mp4 7.4 MB |
| Hands-On PyTorch Machine Learning/2 - PyTorch Basics/2. Understand PyTorch basic operations.mp4 7.5 MB |
| Reinforcement Learning Foundations/3 - Monte Carlo Method/2. Exploration and exploitation.mp4 7.7 MB |
| Hands-On PyTorch Machine Learning/1 - Preparation/1. PyTorch overview.mp4 7.7 MB |
| Artificial Intelligence Foundations Neural Networks/3 - Other Types of Neural Networks/3. Transformer architecture.mp4 7.8 MB |
| Hands-On PyTorch Machine Learning/5 - Torchtext/1. Torchtext introduction.mp4 7.9 MB |
| Building Computer Vision Applications with Python/7 - Morphological Modifications/5. Solution Help a robot.mp4 7.9 MB |
| Building Computer Vision Applications with Python/2 - The Basics of Image Processing/2. Color encoding.mp4 7.9 MB |
| Deep Learning Getting Started/4 - Deep Learning Example 1/3. Creating a deep learning model.mp4 8.1 MB |
| Hands-On PyTorch Machine Learning/2 - PyTorch Basics/3. Understand PyTorch NumPy Bridge.mp4 8.1 MB |
| Building Computer Vision Applications with Python/4 - Filters/4. Gaussian filters.mp4 8.2 MB |
| Machine Learning Foundations Linear Algebra/4 - Introduction to Matrices/1. Matrices introduction.mp4 8.4 MB |
| Machine Learning Foundations Linear Algebra/5 - Gaussian Elimination/3. Inverse and determinant.mp4 8.4 MB |
| Building Computer Vision Applications with Python/4 - Filters/1. Convolution filters.mp4 8.5 MB |
| Reinforcement Learning Foundations/1 - Getting Started with Reinforcement Learning/4. A basic RL solution.mp4 8.6 MB |
| Machine Learning Foundations Linear Algebra/0 - Introduction/1. Introduction.mp4 8.6 MB |
| Building Computer Vision Applications with Python/2 - The Basics of Image Processing/4. Resolution.mp4 8.8 MB |
| Artificial Intelligence Foundations Neural Networks/1 - What Are Neural Networks/1. Machine learning and neural networks.mp4 8.8 MB |
| Machine Learning Foundations Linear Algebra/4 - Introduction to Matrices/3. Types of matrix transformation.mp4 8.9 MB |
| Artificial Intelligence Foundations Neural Networks/2 - Key Components in Neural Network Architecture/4. How neural networks learn.mp4 8.9 MB |
| Building Computer Vision Applications with Python/7 - Morphological Modifications/4. Challenge Help a robot.mp4 9.0 MB |
| Reinforcement Learning Foundations/4 - Temporal Difference Methods/3. SARSAMAX (Q-learning).mp4 9.1 MB |
| Deep Learning Getting Started/4 - Deep Learning Example 1/2. Input preprocessing.mp4 9.4 MB |
| Deep Learning Getting Started/4 - Deep Learning Example 1/4. Training and evaluation.mp4 9.4 MB |
| Building Computer Vision Applications with Python/2 - The Basics of Image Processing/7. Solution Manipulate some pictures.mp4 9.5 MB |
| Machine Learning Foundations Linear Algebra/4 - Introduction to Matrices/2. Types of matrices.mp4 9.6 MB |
| Machine Learning Foundations Linear Algebra/5 - Gaussian Elimination/2. Gaussian elimination and finding the inverse matrix.mp4 9.7 MB |
| Machine Learning Foundations Linear Algebra/2 - Vectors Basics/3. Coordinate system.mp4 9.8 MB |
| Artificial Intelligence Foundations Neural Networks/4 - Build a Simple Neural Network Using Keras/2. Use case and determine evaluation metric.mp4 9.9 MB |
| Reinforcement Learning Foundations/1 - Getting Started with Reinforcement Learning/1. Terms in reinforcement learning.mp4 10.2 MB |
| Artificial Intelligence Foundations Neural Networks/4 - Build a Simple Neural Network Using Keras/5. Train the neural network using Keras.mp4 10.3 MB |
| Machine Learning Foundations Linear Algebra/7 - Eigenvalues and Eigenvectors/1. Introduction to eigenvalues and eigenvectors.mp4 10.4 MB |
| Artificial Intelligence Foundations Thinking Machines/1 - What Is Artificial Intelligence/2. The history of AI.mp4 10.4 MB |
| Building Computer Vision Applications with Python/3 - From Color to Black and White/3. Converting grayscale to black and white.mp4 10.5 MB |
| Building Computer Vision Applications with Python/1 - Setting Up Your Environment/2. Testing your environment.mp4 10.6 MB |
| Artificial Intelligence Foundations Neural Networks/4 - Build a Simple Neural Network Using Keras/7. Solution Build a neural network.mp4 10.8 MB |
| Building Computer Vision Applications with Python/3 - From Color to Black and White/1. Average grayscale.mp4 10.9 MB |
| Machine Learning Foundations Linear Algebra/6 - Matrices from Orthogonality to Gram–Schmidt Process/4. Gram–Schmidt process.mp4 11.1 MB |
| Machine Learning Foundations Linear Algebra/1 - Introduction to Linear Algebra/1. Defining linear algebra.mp4 11.2 MB |
| Building Computer Vision Applications with Python/4 - Filters/2. Average filters.mp4 11.4 MB |
| Artificial Intelligence Foundations Thinking Machines/3 - Finding the Right Approach/2. Data vs. reasoning.mp4 11.4 MB |
| Building Computer Vision Applications with Python/5 - Image Scaling/2. Downscaling example.mp4 11.4 MB |
| Building Computer Vision Applications with Python/7 - Morphological Modifications/2. Erosion and dilation.mp4 11.4 MB |
| Artificial Intelligence Foundations Thinking Machines/6 - What Has Changed/3. Self-supervised learning.mp4 11.4 MB |
| Machine Learning Foundations Linear Algebra/7 - Eigenvalues and Eigenvectors/2. Calculating eigenvalues and eigenvectors.mp4 11.5 MB |
| Building Computer Vision Applications with Python/5 - Image Scaling/4. Upscaling example.mp4 11.7 MB |
| Artificial Intelligence Foundations Thinking Machines/6 - What Has Changed/1. Generative AI.mp4 11.7 MB |
| Artificial Intelligence Foundations Thinking Machines/4 - Common AI Programs/3. The Internet of Things.mp4 11.7 MB |
| Machine Learning Foundations Linear Algebra/4 - Introduction to Matrices/4. Composition or combination of matrix transformations.mp4 11.8 MB |
| Artificial Intelligence Foundations Neural Networks/5 - Best Practices for Optimizing a Neural Network/4. Regularization techniques to improve overfitting models.mp4 11.8 MB |
| Artificial Intelligence Foundations Thinking Machines/1 - What Is Artificial Intelligence/1. Define general intelligence.mp4 11.9 MB |
| Machine Learning Foundations Linear Algebra/3 - Vector Projections and Basis/4. Basis, linear independence, and span.mp4 12.0 MB |
| Building Computer Vision Applications with Python/2 - The Basics of Image Processing/1. Image representation.mp4 12.1 MB |
| Hands-On PyTorch Machine Learning/1 - Preparation/4. PyTorch data exploration.mp4 12.1 MB |
| Reinforcement Learning Foundations/2 - Reinforcement Learning Algorithms/1. Monte Carlo method.mp4 12.2 MB |
| Artificial Intelligence Foundations Thinking Machines/7 - Avoiding Pitfalls/1. Pitfalls.mp4 12.3 MB |
| Machine Learning Foundations Linear Algebra/3 - Vector Projections and Basis/1. Dot product of vectors.mp4 12.4 MB |
| Machine Learning Foundations Linear Algebra/2 - Vectors Basics/2. Vector arithmetic.mp4 12.4 MB |
| Machine Learning Foundations Linear Algebra/7 - Eigenvalues and Eigenvectors/4. Google PageRank algorithm.mp4 12.4 MB |
| Building Computer Vision Applications with Python/6 - Fun with Cuts/3. Cuts in panoramic photography.mp4 12.5 MB |
| Artificial Intelligence Foundations Thinking Machines/6 - What Has Changed/2. Foundation models.mp4 12.6 MB |
| Artificial Intelligence Foundations Thinking Machines/5 - Mixing with Other Technologies/1. Big data.mp4 12.7 MB |
| Artificial Intelligence Foundations Neural Networks/3 - Other Types of Neural Networks/2. Recurrent neural networks (RNN).mp4 12.8 MB |
| Artificial Intelligence Foundations Thinking Machines/3 - Finding the Right Approach/4. Backpropagation.mp4 13.0 MB |
| Artificial Intelligence Foundations Thinking Machines/1 - What Is Artificial Intelligence/3. Strong vs. weak AI.mp4 13.0 MB |
| Hands-On PyTorch Machine Learning/1 - Preparation/2. PyTorch environment setup.mp4 13.0 MB |
| Artificial Intelligence Foundations Thinking Machines/5 - Mixing with Other Technologies/2. Data science.mp4 13.1 MB |
| Artificial Intelligence Foundations Thinking Machines/2 - The Rise of Machine Learning/2. Artificial neural networks.mp4 13.1 MB |
| Machine Learning Foundations Linear Algebra/7 - Eigenvalues and Eigenvectors/3. Changing to the eigenbasis.mp4 13.2 MB |
| Hands-On PyTorch Machine Learning/4 - Torchaudio/2. Torchaudio for audio understanding.mp4 13.2 MB |
| Artificial Intelligence Foundations Thinking Machines/3 - Finding the Right Approach/5. Regression.mp4 13.5 MB |
| Artificial Intelligence Foundations Thinking Machines/3 - Finding the Right Approach/3. Unsupervised learning.mp4 13.6 MB |
| Hands-On PyTorch Machine Learning/3 - Torchvision/1. Torchvision introduction.mp4 13.7 MB |
| Building Computer Vision Applications with Python/6 - Fun with Cuts/1. Image cuts.mp4 13.7 MB |
| Machine Learning Foundations Linear Algebra/3 - Vector Projections and Basis/2. Scalar and vector projection.mp4 13.8 MB |
| Artificial Intelligence Foundations Thinking Machines/2 - The Rise of Machine Learning/1. Machine learning.mp4 13.8 MB |
| Building Computer Vision Applications with Python/7 - Morphological Modifications/1. Why modify objects.mp4 13.8 MB |
| Artificial Intelligence Foundations Thinking Machines/1 - What Is Artificial Intelligence/4. Plan AI.mp4 13.9 MB |
| Artificial Intelligence Foundations Thinking Machines/2 - The Rise of Machine Learning/3. Perceptrons.mp4 14.1 MB |
| Building Computer Vision Applications with Python/4 - Filters/5. Edge detection filters.mp4 14.2 MB |
| Artificial Intelligence Foundations Thinking Machines/4 - Common AI Programs/1. Robotics.mp4 14.2 MB |
| Hands-On PyTorch Machine Learning/5 - Torchtext/2. Torchtext for translation.mp4 14.3 MB |
| Machine Learning Foundations Linear Algebra/6 - Matrices from Orthogonality to Gram–Schmidt Process/2. Transforming to the new basis.mp4 14.4 MB |
| Artificial Intelligence Foundations Thinking Machines/4 - Common AI Programs/2. Natural language processing.mp4 14.5 MB |
| Reinforcement Learning Foundations/1 - Getting Started with Reinforcement Learning/2. A basic RL problem.mp4 15.1 MB |
| Reinforcement Learning Foundations/4 - Temporal Difference Methods/2. SARSA.mp4 15.2 MB |
| Artificial Intelligence Foundations Neural Networks/3 - Other Types of Neural Networks/1. Convolutional neural networks (CNN).mp4 15.6 MB |
| Artificial Intelligence Foundations Thinking Machines/3 - Finding the Right Approach/1. Match patterns.mp4 15.6 MB |
| Machine Learning Foundations Linear Algebra/5 - Gaussian Elimination/1. Solving linear equations using Gaussian elimination.mp4 17.1 MB |
| Machine Learning Foundations Linear Algebra/3 - Vector Projections and Basis/3. Changing basis of vectors.mp4 17.1 MB |
| Reinforcement Learning Foundations/1 - Getting Started with Reinforcement Learning/3. Markov decision process.mp4 17.4 MB |
| Building Computer Vision Applications with Python/2 - The Basics of Image Processing/3. Image file management.mp4 19.1 MB |
| Building Computer Vision Applications with Python/3 - From Color to Black and White/4. Adaptive thresholding.mp4 20.9 MB |
| Machine Learning Foundations Linear Algebra/1 - Introduction to Linear Algebra/2. Applications of linear algebra in ML.mp4 22.8 MB |
| Building Computer Vision Applications with Python/4 - Filters/3. Median filters.mp4 25.4 MB |
| Machine Learning Foundations Linear Algebra/2 - Vectors Basics/1. Introduction to vectors.mp4 29.9 MB |
| Building Computer Vision Applications with Python/6 - Fun with Cuts/2. Stitching two images together.mp4 44.1 MB |
| Building Computer Vision Applications with Python/Ex_Files_Computer_Vision_Deep_Dive_in_Python.zip 145.8 MB |
- Torrent indexed: 10 months
- Torrent updated: Sunday 25th of August 2024 06:50:38 PM
- Torrent hash: AD3F47E9AA6BF9084D2D7E77062D9A0DD0A4A4A7
- Torrent size: 1.8 GB
- Torrent category: Tutorials
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