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Genre:eLearning | Language: English | Size:2.51 GB

Files Included :

1  Course Overview.mp4 (3.7 MB)
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01  Version Check.mp4 (550.56 KB)
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02  Module Overview.mp4 (1.3 MB)
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03  Prerequisites and Course Outline.mp4 (2.8 MB)
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04  Representation Learning Using Neural Networks.mp4 (10.35 MB)
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05  Neuron as a Mathematical Function.mp4 (9.26 MB)
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06  Activation Functions.mp4 (7.32 MB)
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07  Introducing PyTorch.mp4 (5.73 MB)
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08  TensorFlow and PyTorch.mp4 (6.75 MB)
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09  Demo - PyTorch Install and Setup.mp4 (9.51 MB)
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10  Summary.mp4 (1.4 MB)
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01  Module Overview.mp4 (3.21 MB)
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02  Demo - Creating and Initializing Tensors.mp4 (13.19 MB)
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03  Demo - Simple Operations on Tensors.mp4 (10.82 MB)
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04  Demo - Elementwise and Matrix Operations on Tensors.mp4 (7.69 MB)
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05  Demo - Converting between PyTorch Tensors and NumPy Arrays.mp4 (8.39 MB)
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06  PyTorch Support for CUDA Devices.mp4 (9.47 MB)
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07  Demo - Setting up a Deep Learning VM to Work with GPUs.mp4 (15.47 MB)
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08  Demo - Creating Tensors on CUDA-enabled Devices.mp4 (6.93 MB)
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09  Demo - Working with the Device Context Manager.mp4 (9.23 MB)
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10  Summary.mp4 (1.54 MB)
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01  Module Overview.mp4 (1.54 MB)
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02  Gradient Descent Optimization.mp4 (6.32 MB)
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03  Forward and Backward Passes.mp4 (4.92 MB)
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04  Calculating Gradients.mp4 (7.31 MB)
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05  Using Gradients to Update Model Parameters.mp4 (5.9 MB)
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06  Two Passes in Reverse Mode Automatic Differentiation.mp4 (5.99 MB)
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07  Demo - Introducing Autograd.mp4 (10.08 MB)
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08  Demo - Working with Gradients.mp4 (7.61 MB)
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09  Demo - Variables and Tensors.mp4 (3.64 MB)
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10  Demo - Training a Linear Model Using Autograd.mp4 (15.4 MB)
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11  Summary.mp4 (2.07 MB)
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01  Module Overview.mp4 (883.38 KB)
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02  Static vs  Dynamic Computation Graphs.mp4 (11.53 MB)
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03  Dynamic Computation Graphs in PyTorch.mp4 (1.83 MB)
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04  Demo - Installing Tensorflow, Graphviz, and Hidden Layer.mp4 (3.04 MB)
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05  Demo - Building Dynamic Computations Graphs with PyTorch.mp4 (4.14 MB)
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06  Demo - Visualizing Neural Networks in PyTorch Using Hidden Layer.mp4 (5.35 MB)
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07  Demo - Building Static Computation Graphs with Tensorflow.mp4 (11.05 MB)
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08  Demo - Visualizing Tensorflow Graphs with Tensorboard.mp4 (3.92 MB)
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09  Demo - Dynamic Computation Graphs in Tensorflow with Eager Execution.mp4 (5.98 MB)
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10  Debugging in PyTorch and Tensorflow.mp4 (2.13 MB)
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11  Summary and Further Study.mp4 (2.27 MB)
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1  Course Overview.mp4 (3.81 MB)
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01  Version Check.mp4 (560.53 KB)
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02  Module Overview.mp4 (1.63 MB)
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03  Prerequisites and Course Outline.mp4 (1.99 MB)
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04  CUDA Support in PyTorch.mp4 (10.08 MB)
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05  Exploring PyTorch Install Options on a Local Machine.mp4 (4.38 MB)
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06  Setting up a Virtual Machine.mp4 (9.41 MB)
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07  Installing PyTorch with CPU Support Using Conda.mp4 (19.17 MB)
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08  Installing PyTorch with CPU Support Using Pip.mp4 (10.37 MB)
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09  Adding GPU Support to the VM and Installing the CUDA Toolkit.mp4 (15.1 MB)
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10  Installing PyTorch with GPU Support Using Conda.mp4 (9.74 MB)
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11  Installing PyTorch with CUDA Support Using Pip.mp4 (5.37 MB)
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12  Module Summary.mp4 (1.87 MB)
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1  Module Overview.mp4 (1.77 MB)
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2  Linear Regression.mp4 (6.28 MB)
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3  Finding the Best Fit Line.mp4 (5.23 MB)
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4  Gradient Descent.mp4 (7.27 MB)
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5  Training a Simple Neural Network with One Neuron.mp4 (12.18 MB)
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7  Preventing Overfitting Using Regularization.mp4 (7.32 MB)
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9  Module Summary.mp4 (2.3 MB)
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01  Module Overview.mp4 (1.73 MB)
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02  Training a Neural Network Forward and Backward Passes.mp4 (4.16 MB)
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03  Optimizers.mp4 (5.76 MB)
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04  Building a Neural Network Using PyTorch Layers.mp4 (10.46 MB)
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05  Training a Neural Network Using Optimizers.mp4 (4.77 MB)
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06  Dropout.mp4 (5.66 MB)
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07  Epochs and Batches.mp4 (2.59 MB)
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08  Exploring the Bike Sharing Dataset.mp4 (11.75 MB)
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09  Using Datasets and Data Loaders in PyTorch.mp4 (5.38 MB)
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10  Building and Train a Neural Network for Bike Sharing Demand Prediction.mp4 (11.88 MB)
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11  Working with Different Neural Network Architectures.mp4 (9.11 MB)
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12  Module Summary.mp4 (1.95 MB)
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01  Module Overview.mp4 (1.77 MB)
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02  Softmax and Cross Entropy.mp4 (6.67 MB)
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03  Softmax and LogSoftmax.mp4 (4.48 MB)
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04  Evaluating Classifiers.mp4 (3.28 MB)
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05  Exploring the Graduate Admissions Dataset.mp4 (9.86 MB)
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06  Preprocessing the Data.mp4 (8.19 MB)
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07  Building a Custom Neural Network.mp4 (10.65 MB)
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08  Training and Evaluating the Neural Network.mp4 (8 MB)
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09  Customizing and Evaluating Different Models.mp4 (10.53 MB)
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10  Summary and Further Study.mp4 (2.32 MB)
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1  Course Overview.mp4 (2.92 MB)
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01  Version Check.mp4 (577.35 KB)
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02  Module Overview.mp4 (1.28 MB)
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03  Prerequisites and Course Outline.mp4 (1.82 MB)
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04  Machine Learning on the Cloud.mp4 (3.83 MB)
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05  PyTorch - Taxonomy of Solutions.mp4 (4.76 MB)
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06  Introducing SageMaker.mp4 (3.83 MB)
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07  Creating a SageMaker Notebook Instance.mp4 (17.35 MB)
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08  Prototyping a PyTorch Model on SageMaker Notebooks.mp4 (18.17 MB)
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09  PyTorch Estimators on SageMaker.mp4 (2.34 MB)
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10  Distributed Data Loading in PyTorch.mp4 (13.62 MB)
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11  Distributed Training in PyTorch.mp4 (17.78 MB)
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12  Using PyTorch Estimators for Distributed Training.mp4 (16.23 MB)
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13  Model Deployment and Prediction Using Estimators.mp4 (10.28 MB)
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14  AWS Deep Learning AMIs.mp4 (2.61 MB)
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15  Instantiating a Deep Learning VM.mp4 (18.57 MB)
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16  Building Models with GPU Support on the AWS Deep Learning VM.mp4 (13.2 MB)
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01  Module Overview.mp4 (1.42 MB)
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02  Introducing Azure Machine Learning Service.mp4 (2.64 MB)
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03  Prototyping PyTorch Models on Azure Notebooks.mp4 (16.01 MB)
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04  Azure Machine Learning Service Workflow.mp4 (4.5 MB)
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05  Understanding Terms in Azure Machine Learning.mp4 (3.81 MB)
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06  Horovod for Distributed Training.mp4 (1.84 MB)
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07  Distributed Training in PyTorch Using the Horovod Framework.mp4 (22.73 MB)
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08  Instantiating the PyTorch Estimator for Distributed Training.mp4 (17.23 MB)
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09  Distributed Run Using the PyTorch Estimator.mp4 (11.05 MB)
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10  The Azure Deep Learning VM.mp4 (2.52 MB)
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11  Instantiating an Azure Deep Learning VM.mp4 (15.53 MB)
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12  Building PyTorch Models with GPU Support on Azure Deep Learning VMs.mp4 (12.95 MB)
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1  Module Overview.mp4 (1004.95 KB)
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2  Cloud Datalab and Deep Learning VMs.mp4 (4.11 MB)
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3  Setting up a Cloud Datalab VM.mp4 (16.9 MB)
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4  Prototyping PyTorch Models Using Cloud Datalab.mp4 (5.43 MB)
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6  Using JupyterLab on a GCP Deep Learning VM.mp4 (4.64 MB)
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7  Summary and Further Study.mp4 (2.04 MB)
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1  Course Overview.mp4 (3.44 MB)
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01  Version Check.mp4 (564.49 KB)
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02  Module Overview.mp4 (1.71 MB)
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03  Prerequisites and Course Outline.mp4 (2.3 MB)
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04  Single Channel and Multichannel Images.mp4 (6.47 MB)
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05  Preprocessing Images to Train Robust Models.mp4 (8.46 MB)
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06  Setting up a Deep Learning VM.mp4 (12.06 MB)
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07  Image Preprocessing - Resizing and Rescaling Images.mp4 (12.74 MB)
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08  Cropping and Denoising Images.mp4 (11.21 MB)
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09  Standardizing Images in PyTorch.mp4 (8.89 MB)
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10  ZCA Whitening to Decorrelate Features.mp4 (5.5 MB)
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11  Image Transformations Using PyTorch Libraries.mp4 (5.72 MB)
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12  Normalizing Images Using Mean and Standard Deviation.mp4 (10.31 MB)
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13  Module Summary.mp4 (1.81 MB)
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1  Module Overview.mp4 (2.25 MB)
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2  Deep Neural Networks to Work with Images.mp4 (10.8 MB)
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3  Loading and Processing MNIST Images.mp4 (11.78 MB)
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6  Module Summary.mp4 (1.81 MB)
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1  Module Overview.mp4 (1.76 MB)
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2  Local Receptive Fields.mp4 (4.28 MB)
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3  Understanding Convolution.mp4 (5.98 MB)
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4  Convolutional Layers.mp4 (11.06 MB)
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5  Pooling Layers.mp4 (6.38 MB)
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6  Typical CNN Architecture.mp4 (5.89 MB)
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7  Applying Convolutional and Pooling Layers.mp4 (17.28 MB)
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8  Module Summary.mp4 (1.77 MB)
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01  Module Overview.mp4 (2 MB)
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02  Zero Padding and Stride Size.mp4 (6.49 MB)
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03  Batch Normalization.mp4 (7.18 MB)
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04  Activation Functions.mp4 (3.72 MB)
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05  Feature Map Size Calculations.mp4 (3.18 MB)
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06  Preparing and Exploring Image Data.mp4 (7.83 MB)
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07  Setting up a Convolutional Neural Network.mp4 (11.25 MB)
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08  Training a CNN.mp4 (10.25 MB)
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09  Hyperparameter Tuning.mp4 (10.53 MB)
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10  Module Summary.mp4 (2.1 MB)
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1  Module Overview.mp4 (1.59 MB)
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2  Preparing the CIFAR-10 Dataset.mp4 (7.73 MB)
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3  Setting up the CNN.mp4 (6.92 MB)
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4  Training the CNN.mp4 (8.45 MB)
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5  Choosing Different Activation Functions.mp4 (7.7 MB)
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6  Choosing Pooling Layers.mp4 (7.2 MB)
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7  Choosing Convolution Kernel Sizes.mp4 (8.24 MB)
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8  Additional Convolution Layers and Different Kernel Size.mp4 (8.5 MB)
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9  Module Summary.mp4 (1.64 MB)
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1  Module Overview.mp4 (1.81 MB)
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2  Transfer Learning.mp4 (7.72 MB)
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3  Using the Resnet-18 Pretrained Model.mp4 (12.04 MB)
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4  The Train Function to Find the Best Model Weights.mp4 (8.45 MB)
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5  Predictions Using Pretrained Models.mp4 (4.68 MB)
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6  Cleaning up Resources.mp4 (2.25 MB)
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7  Summary and Further Study.mp4 (2.13 MB)
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1  Course Overview.mp4 (3.85 MB)
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01  Version Check.mp4 (554.89 KB)
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02  Module Overview.mp4 (1.83 MB)
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03  Prerequisites and Course Outline.mp4 (2.3 MB)
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04  Content, Style, and Target Images.mp4 (7.26 MB)
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05  Training the Target Image for Style Transfer.mp4 (12.48 MB)
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06  Content Loss.mp4 (6.22 MB)
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07  Style Loss - Cosine Similarity and Dot Products.mp4 (5.37 MB)
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08  Style Loss - Gram Matrix.mp4 (5.75 MB)
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09  Setting up a Deep Learning Virtual Machine.mp4 (11.45 MB)
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10  Using Convolution Filters to Detect Features.mp4 (14.67 MB)
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11  Module Summary.mp4 (1.85 MB)
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1  Module Overview.mp4 (1.82 MB)
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2  Pretrained Models for Style Transfer.mp4 (3.82 MB)
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3  Loading the VGG19 Pretrained Model.mp4 (7.11 MB)
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4  Exploring and Transforming the Content and Style Images.mp4 (14.27 MB)
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5  Extracting Feature Maps from the Content and Style Images.mp4 (7.96 MB)
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6  Calculating the Gram Matrix to Extract Style Information.mp4 (5.89 MB)
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7  Training the Target Image to Perform Style Transfer.mp4 (12.44 MB)
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8  Style Transfer Using AlexNet.mp4 (14.74 MB)
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9  Module Summary.mp4 (1.01 MB)
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01  Module Overview.mp4 (2.06 MB)
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02  Understanding Generative Adversarial Networks (GANs).mp4 (9.46 MB)
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03  Training a GAN.mp4 (4.96 MB)
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04  Understanding the Leaky ReLU Activation Function.mp4 (8.79 MB)
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05  Loading and Exploring the MNIST Handwritten Digit Images.mp4 (9.01 MB)
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06  Setting up the Generator and Discriminator Neural Networks.mp4 (8.3 MB)
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07  Training the Discriminator.mp4 (9.39 MB)
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08  Training the Generator and Generating Fake Images.mp4 (7.97 MB)
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09  Cleaning up Resources.mp4 (2.66 MB)
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10  Summary and Further Study.mp4 (2.59 MB)
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1  Course Overview.mp4 (3.35 MB)
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1  Version Check.mp4 (560.2 KB)
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2  Module Overview.mp4 (1.99 MB)
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3  Prerequisites and Course Outline.mp4 (2.26 MB)
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4  RNNs for Natural Language Processing.mp4 (5.57 MB)
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5  Recurrent Neurons.mp4 (6.8 MB)
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6  Back Propagation through Time.mp4 (7.46 MB)
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7  Coping with Vanishing and Exploding Gradients.mp4 (9.61 MB)
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8  Long Memory Cells.mp4 (10.05 MB)
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9  Module Summary.mp4 (2.23 MB)
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01  Module Overview.mp4 (1.81 MB)
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02  Word Embeddings to Represent Text Data.mp4 (7.27 MB)
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03  Introducing torchtext to Process Text Data.mp4 (3.62 MB)
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04  Feeding Text Data into RNNs.mp4 (5.15 MB)
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05  Setup and Data Cleaning.mp4 (8.15 MB)
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06  Using Torchtext to Process Text Data.mp4 (18.78 MB)
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07  Designing an RNN for Binary Text Classification.mp4 (11.08 MB)
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08  Training the RNN.mp4 (10.96 MB)
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09  Using LSTM Cells and Dropout.mp4 (5.59 MB)
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10  Module Summary.mp4 (1.91 MB)
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1  Module Overview.mp4 (2.05 MB)
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2  Language Prediction Based on Names.mp4 (3.22 MB)
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3  Loading and Cleaning Data.mp4 (14.07 MB)
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4  Helper Functions to One Hot Encode Names.mp4 (6.32 MB)
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5  Designing an RNN for Multiclass Text Classification.mp4 (16.32 MB)
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6  Predicting Language from Names.mp4 (13.01 MB)
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7  Module Summary.mp4 (1.87 MB)
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01  Module Overview.mp4 (2.35 MB)
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02  Numeric Representations of Words.mp4 (4.39 MB)
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03  Word Embeddings Capture Context and Meaning.mp4 (6.44 MB)
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04  Generating Analogies Using GloVe Embeddings.mp4 (16.52 MB)
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05  Multilayer RNNs.mp4 (2.71 MB)
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06  Bidirectional RNNs.mp4 (6.71 MB)
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07  Data Cleaning and Preparation.mp4 (17.63 MB)
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08  Designing a Multilayer Bidirectional RNN.mp4 (11.32 MB)
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09  Performing Sentiment Analysis Using an RNN.mp4 (7.81 MB)
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10  Module Summary.mp4 (1.99 MB)
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01  Module Overview.mp4 (1.96 MB)
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02  Using Sequences and Vectors with RNNs.mp4 (5.44 MB)
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04  Representing Input and Target Sentences.mp4 (2.7 MB)
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05  Teacher Forcing.mp4 (4.96 MB)
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07  Preparing Sentence Pairs.mp4 (10.68 MB)
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08  Designing the Encoder and Decoder.mp4 (10.19 MB)
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10  Translating Sentences.mp4 (9.27 MB)
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11  Summary and Further Study.mp4 (3.16 MB)
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1  Course Overview.mp4 (3.43 MB)
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01  Version Check.mp4 (612.26 KB)
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02  Module Overview.mp4 (2.18 MB)
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03  Prerequisites and Course Outline.mp4 (1.99 MB)
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04  Introducing Transfer Learning.mp4 (7.2 MB)
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06  Categorizing Transfer Learning.mp4 (9.73 MB)
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07  Transfer Learning Scenarios.mp4 (7.92 MB)
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08  Freeze or Fine-tune Layers.mp4 (7.45 MB)
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09  Benefits of Transfer Learning.mp4 (3.66 MB)
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10  Pre-trained Models in PyTorch.mp4 (9.67 MB)
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12  Exploring Pre-trained Models in PyTorch.mp4 (21.1 MB)
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13  Module Summary.mp4 (1.9 MB)
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1  Module Overview.mp4 (2.66 MB)
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8  Fine-tuning Top Layers.mp4 (7.13 MB)
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9  Module Summary.mp4 (1.64 MB)
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1  Module Overview.mp4 (2.34 MB)
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2  Exploring and Loading the Chest X-Ray Dataset.mp4 (10.25 MB)
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3  Training a Model from Scratch.mp4 (10.19 MB)
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4  Exploring and Loading the Natural Images Dataset.mp4 (7.65 MB)
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5  Fine-tuning the Network.mp4 (8.92 MB)
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6  Cleaning up Resources.mp4 (2.54 MB)
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7  Summary and Further Study.mp4 (1.94 MB)
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1  Course Overview.mp4 (4.13 MB)
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01  Version Check.mp4 (575.06 KB)
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02  Prerequisites and Course Outline.mp4 (2.68 MB)
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03  Structural and Predictive Models.mp4 (8 MB)
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04  Demo - Install and Setup Pytorch.mp4 (8.18 MB)
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05  Demo - Preparing Data.mp4 (12.32 MB)
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06  Demo - Building a Simple Neural Network to Perform Regression.mp4 (11.7 MB)
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07  Demo - Exploring the Diamonds Dataset.mp4 (9.54 MB)
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08  Demo - Preparing and Processing Data.mp4 (10.25 MB)
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09  Demo - Building and Training a Regression Model.mp4 (16.7 MB)
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10  Demo - Exploring and Preprocessing Data.mp4 (15.6 MB)
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11  Demo - Defining the Neural Network and Helper Functions.mp4 (12.79 MB)
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1  Text as Sequential Data.mp4 (4.33 MB)
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2  The Recurrent Neuron.mp4 (5.18 MB)
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3  RNN Training and Long Memory Cells.mp4 (8.18 MB)
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4  RNN to Generate Names in Languages.mp4 (4.94 MB)
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5  Demo - Loading and Preparing Training Data.mp4 (11.14 MB)
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6  Demo - Setting up Helper Functions.mp4 (9.87 MB)
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7  Demo - Defining the RNN.mp4 (18.26 MB)
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8  Demo - Training the RNN and Generating Names.mp4 (16.41 MB)
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01  Finding Patterns in Data.mp4 (4.53 MB)
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02  Association Rule Learning.mp4 (3.6 MB)
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03  Clustering.mp4 (4.84 MB)
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04  Content Based Approaches to Recommendations.mp4 (7.14 MB)
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05  Collaborative Filtering.mp4 (5.66 MB)
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06  Nearest Neighborhood.mp4 (4 MB)
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07  Matrix Factorization.mp4 (9.28 MB)
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08  Alternating Least Squares to Estimate the Ratings Matrix.mp4 (5.8 MB)
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09  Evaluation Metrics vs  Loss Metrics.mp4 (4.08 MB)
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10  Mean Average Precision @ K.mp4 (11.27 MB)
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11  Demo - Initializing the Ratings Matrix.mp4 (11.56 MB)
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12  Demo - Setting up the Neural Network.mp4 (12.31 MB)
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13  Demo - The Train Helper Function.mp4 (20.3 MB)
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14  Demo - The Evaluate Helper Function.mp4 (6.51 MB)
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16  Summary and Further Study.mp4 (2.12 MB)
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1  Course Overview.mp4 (3.78 MB)
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01  Version Check.mp4 (606.15 KB)
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02  Module Overview.mp4 (2.89 MB)
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03  Prerequisites and Course Outline.mp4 (1.88 MB)
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04  Saving and Loading PyTorch Models.mp4 (10.23 MB)
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05  Building and Training a Classifier Model.mp4 (11.85 MB)
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06  Saving and Loading Models Using torch save().mp4 (16.55 MB)
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07  Saving Model Using the state dict.mp4 (15.29 MB)
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08  Saving and Loading Checkpoints.mp4 (10.08 MB)
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09  Introducing ONNX.mp4 (2.76 MB)
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10  Exporting a Model to ONNX and Loading in Caffe2.mp4 (18.34 MB)
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11  Module Summary.mp4 (1.88 MB)
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1  Module Overview.mp4 (1.67 MB)
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3  Training Using Multiple Processes.mp4 (14.27 MB)
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5  Training on Multiple GPUs.mp4 (12.41 MB)
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6  Module Summary.mp4 (1.69 MB)
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1  Module Overview.mp4 (1.97 MB)
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2  Distributed Training on the Cloud.mp4 (5.48 MB)
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3  Setting up a SageMaker Notebook Instance.mp4 (8.88 MB)
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4  Setting up Training and Test Data Loaders.mp4 (9.46 MB)
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5  Define the Training Function.mp4 (9.4 MB)
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8  Module Summary.mp4 (1.54 MB)
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1  Module Overview.mp4 (1.78 MB)
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2  Exploring Options to Deploy PyTorch Models.mp4 (6.18 MB)
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4  Creating a Flask App to Serve the PyTorch Model.mp4 (10.87 MB)
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5  Using the Model for Prediction.mp4 (4.51 MB)
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6  Installing Docker.mp4 (5.56 MB)
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7  Creating and Using a Clipper Cluster for Prediction.mp4 (17.09 MB)
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9  Summary and Further Study.mp4 (2.01 MB)
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https://nitroflare.com/view/DFD1C5B84E33901/Pluralsight_-_Building_Deep_Learning_Solutions_with_PyTorch.z02
https://nitroflare.com/view/FD614EE70DFBE27/Pluralsight_-_Building_Deep_Learning_Solutions_with_PyTorch.zip