Deep Learning & Neural Networks with Python enroll
Learn the fundamentals of Deep Learning applications by building, training and deploying PyTorch models from scratch. You’ll work with transfer learning using convolutional neural networks (CNNs) and recurrent neural networks (RNNs) as well as learn how to deploy your models.
28 Sections · 280 Pages · By Michael Frantz
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Level
Advanced to Professional
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Duration
250 Hours. Or, ~4 months of dedicated learning @ 15-20 hours per week
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What's included
157 lessons. 94 videos. 100+ functional examples. 36 exercises. 2-3 portfolio projects.
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Certificate
Deep Learning & Neural Networks Certificate
This course is part of the following career track:
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1) Introduction
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2) Fundamentals: Linear Regression with Torch
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Introduction to Neural Networks3 min
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Video: Introduction to Neural Networks1 min
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Linear Regression with Torch
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Single-variable Linear Regression with Torch7 min
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Video: Single Variable Linear Regression5 min
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Video: Linear Regression Refresher5 min
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Video: Mean Squared Error (MSE)3 min
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Video Solution: Exercise 2.1 Visualize Your Data2 min
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Mean Squared Error (MSE) Loss Function7 min
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Video: scikit-learn Walkthrough2 min
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Sklearn Linear Regression4 min
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Pytorch Linear Regression7 min
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Video: Pytorch Linear Regression8 min
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What is Gradient Descent5 min
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Link: DS/ML Gradient Descent
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Training Models with PyTorch
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Train Your First Torch Model15 min
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Video Solution: Exercise 2.5 Train Your Model1 min
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Multi Variate Linear Regression22 min
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Video: Multi Variate Linear Regression10 min
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PyTorch Linear Layer10 min
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Non Linearities and Activation Functions5 min
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Video: Non Linearities and Activation Functions3 min
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Deep Neural Networks
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Create a Deep Neural Network15 min
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Video: Create a Deep Neural Network8 min
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Feedback: Linear Regression with Torch
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Feedback: Linear Regression with Torch
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3) Fundamentals: Tensors and Tensor Operations
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Introduction: Pytorch Tensor3 min
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Video Introduction: Pytorch Tensor1 min
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What is a Tensor12 min
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Video: What is a Tensor5 min
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Tensor Operations
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Tensor Scalar Operations5 min
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Video: Tensor Scalar Operations1 min
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Tensor Element Wise Operations6 min
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Video: Tensor Element Wise Operations1 min
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Broadcasting with Pytorch6 min
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Tensor Multiplication
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Vector Multiplication: The Dot Product of Two Vectors4 min
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Video: Vector Multiplication and Dot Product of Two Vectors2 min
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Matrix Multiplication5 min
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Video: Matrix Multiplication5 min
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Additional Tensor Functions
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Useful Tensor Functions and Methods3 min
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Torch Reshape Methods7 min
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Create Custom Sigmoid and Softmax Functions4 min
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Video: Create Custom Sigmoid and Softmax Functions3 min
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Feedback: Tensors and Tensor Operations
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Feedback: Tensors and Tensor Operations
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4) Fundamentals: Torch Data API
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Getting Started with the PyTorch Data API4 min
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Video: Pytorch Data API1 min
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Python Pandas: Create DataFrame In-Memory7 min
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Create a PyTorch Dataset5 min
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Video: Creating Pytorch Datasets4 min
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PyTorch DataLoader15 min
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Video: PyTorch Dataloaders6 min
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Out-of-Memory Data: Create a
fastai
Image Dataset8 min -
Video: Working with OOM Datasets3 min
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CIFAR-10 Dataset & Utility Functions6 min
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Video: Image Loading Dataset & Utility Functions4 min
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PyTorch DataSet to DataLoader using
collate_fn
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Video: DataSet to DataLoader with Collate Function5 min
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Feedback: PyTorch Data API
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5) Fundamentals: Loss Functions
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Loss Functions5 min
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Video: Intro to Loss Functions1 min
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Regression: Mean Squared Error8 min
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Video: Revisiting Mean Squared Error (MSE)3 min
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Classification
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Classification: Cross Entropy Loss17 min
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Video: Cross Entropy Loss8 min
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Binary Classification: Binary Cross Entropy6 min
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Video: Binary Cross Entropy3 min
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Multi-class Classification5 min
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Video: Multi-class Classification2 min
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Feedback
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Feedback: Loss Functions
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6) Fundamentals: Optimizers
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Introduction to ML Optimizers4 min
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Video: Intro to ML Optimizers1 min
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Create a Bivariate Dataset5 min
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Create a Small Neural Network8 min
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Video: Exercise 6.1 & 6.2 Solution3 min
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Stochastic Gradient Descent
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Stochastic Gradient Descent (SGD)6 min
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Video: Stochastic Gradient Descent (SGD)2 min
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SGD with Momentum7 min
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Video: SGD with Momentum3 min
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RMSProp and Adam
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RMSProp Optimizer7 min
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Video: RMSProp Optimizer4 min
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Adam Optimizer6 min
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Video: Adam Optimizer6 min
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Feedback
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Feedback: Optimizers
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7) Fundamentals: How Models Learn
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Introduction: How Machine Learning Models Learn3 min
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Video: Intoduction to How Models Learn1 min
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The Derivative
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Definition of Derivative5 min
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Video: Revisiting Derivatives4 min
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Example of the Chain Rule Derivative9 min
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Video: The Chain Rule & Torch Backprop6 min
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Gradient: Partial Derivative5 min
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Video: From Derivative to Gradient3 min
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Exploding Gradient and Vanishing Gradient Problem3 min
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Video: Problems with Training Neural Networks2 min
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Improved Parameter Initialization3 min
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Video: Improved Parameter Initialization2 min
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Parameters and Batches
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ReLU Activation Function8 min
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Video: Non-Saturating Activation Functions4 min
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Batch Normalization7 min
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Video: Batch Normalization5 min
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Feedback: How Models Learn
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8) Fundamentals: Introduction to Classification
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Introduction to Classification5 min
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Video: Introduction to Classification1 min
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Video: GPU Setup1 min
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Explore Images with Matplotlib imshow11 min
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Video: Explore Images with Matplotlib imshow6 min
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Classifier Model
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Build a Classification Model7 min
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Video: Build a Classification Model7 min
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How to Train a Classification Model4 min
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Train Your Classification Model6 min
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Classification Model Evaluation8 min
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Video: Train and Evaluate a Classification Model12 min
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Feedback
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Feedback: Introduction to Classification
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9) Introduction to High-Level Libraries
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Introduction: Pytorch Lightning and FastAi5 min
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Video: Introduction to Pytorch Lightning and FastAi2 min
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MNIST Dataset
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MNIST Dataset Library5 min
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Video: MNIST Dataset Library1 min
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MNIST Data Cleaning7 min
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Video Solution: MNIST Data Cleaning1 min
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Load and Save Dataset3 min
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PyTorch Lightning
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What is Pytorch Lightning3 min
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Launch Tensorboard in Google Colab3 min
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Video: Introduction to Pytorch Lightning1 min
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Pytorch Lightning: DataModule3 min
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Pytorch Lightning: LightningModule5 min
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Pytorch Lightning: Logging and Callback Functions3 min
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Pytorch Lightning: Trainer6 min
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ML Model Inference and Checkpoint8 min
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Fastai
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What is FastAi3 min
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Video: What is FastAi2 min
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Fastai DataLoaders10 min
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Fastai Learner10 min
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Feedback
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Feedback: Introduction to High-Level Libraries
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Mini-Project: Fundamentals
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Mini-Project Introduction3 min
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Video: Fundamentals Mini-Project Introduction5 min
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Mini Project Setup & Instructions4 min
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Data Selection, EDA, and Feature Engineering5 min
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Training a Non-Deep Learning Model3 min
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Training a Deep Learning Model3 min
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Fundamentals Mini Project Report8 min
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Feedback
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Feedback: Fundamentals Mini Project
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10) Convolutional Neural Networks (CNNs)
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Introduction: Convolutional Neural Networks (CNNs)3 min
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CNNs for Edge Detection12 min
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CNN: Reflective and Zero Padding5 min
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CNN: Strided Convolution3 min
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Multi-Channel Convolutional Filter6 min
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The Pytorch Conv2d Layer3 min
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Average and Max Pooling7 min
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Feedback
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Feedback: Introduction to Convolutional Neural Networks (CNNs)
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11) Build a CNN Image Classifier
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Introduction: Build a Convolutional Neural Network3 min
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Video: Image Classification with CNNs1 min
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Data Cleaning
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Fastai: MNIST Dataset Cleaning6 min
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Video: Fastai MNIST Dataset Cleaning2 min
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CNN Model
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Convolutional Neural Network Tutorial7 min
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Video: Convolutional Neural Network Tutorial3 min
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Convolutional Neural Network Training7 min
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Video: Convolutional Neural Network Training3 min
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Examine Feature Map of CNN Layers6 min
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Video: What is Your Model Learning?3 min
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Feedback
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Feedback: Our First Convolutional Neural Network (CNN) Image Classifier
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12) Transfer Learning and ResNet
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Introduction: Transfer Learning3 min
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Video: ResNet and Transfer Learning6 min
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Data Cleaning
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Pets Data Cleaning6 min
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ResNet
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What is ResNet8 min
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Video: What is ResNet4 min
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ResNet Image Classification5 min
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Video: ResNet Image Classification1 min
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Transfer Learning
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Transfer Learning: ResNet Pre-trained with ImageNet13 min
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Video: Transfer Learning with ResNet and ImageNet6 min
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Serve Models with GradIO7 min
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Video: Serve Models with GradIO3 min
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Build the Residual Block from Scratch (Optional)16 min
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Feedback
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Feedback: Transfer Learning and ResNet
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13) Image Classification: Augmentation
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Introduction: Image Augmentation4 min
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Video: Introduction to Image Augmentation1 min
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Albumentations
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Image Augmentation with Albumentations3 min
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Video: Image Augmentation with Albumentations1 min
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Torchvision
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Image Augmentation with Torchvision3 min
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Video: Image Augmentation with Torchvision1 min
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Fastai
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Image Augmentation with Fastai3 min
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Video: Image Augmentation with Fastai3 min
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Experiment: Is Image Augmentation Worth It?10 min
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Feedback
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Feedback: Augmentation
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14) Data Prep for NLP with RNNs
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Introduction: NLP in Deep Learning4 min
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Video: Intro Data Prep for NLP1 min
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Traditional Approaches
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Traditional NLP with Count Vectorizer6 min
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Video: Traditional Text Prep for ML3 min
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Normalization
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NLP Normalization6 min
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Video: Text Normalization1 min
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Tokenization
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NLP Tokenization3 min
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Video: NLP Tokenization1 min
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Create a Tokenizer7 min
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Video: Create a Tokenizer3 min
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Tokenizer Examples11 min
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Video: Tokenizer Examples3 min
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Feedback
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Feedback: Data Preparation for NLP
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15) NLP with Recurrent Neural Networks
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Introduction: Recurrent Neural Networks (RNN)4 min
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Tokenizer Data Cleaning3 min
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Video: Text Classification with RNNs (Intro & Setup)2 min
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Word Embeddings
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Word Embedding and Word Vectors15 min
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Video: Embeddings, Word Vectors and Identifying Bias5 min
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Recurrent Neural Networks
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Introduction to Recurrent Neural Networks (RNNs)3 min
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Video: Understanding Vanilla RNNs10 min
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Quiz: What is an RNN
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Problems with RNNs4 min
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Journal: RNN Problems
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Create a Recurrent Neural Network14 min
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Video: Problems with RNNs (and Solutions!)2 min
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LSTM and GRU
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GRU & LSTM Solutions for RNNs3 min
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Gated Recurrent Unit (GRU)8 min
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Long Short Term Memory (LSTM)6 min
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Text Classification
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NLP with RNNs for Text Classification15 min
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Video: Training an Emotion Classifier with RNNs7 min
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Feedback
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Feedback: Introduction to Recurrent Neural Networks
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16) NLP with RNNs: Deep Dive - RNNs from Scratch
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Introduction: Create RNNs from Scratch6 min
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Create a RNN from Scratch Tutorial13 min
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Video: RNNs from Scratch1 min
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Problems and Solutions
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Problems with Vanilla RNNs3 min
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Gated Recurrent Unit Networks (GRUs)10 min
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Long Short-Term Memory (LSTM) Networks12 min
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Feedback
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Feedback: Deep Dive - RNNs from Scratch
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17) NLP with RNNs: Pre-Training and Transfer Learning for RNNs
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Introduction: Pre-Training and Transfer Learning with RNNs6 min
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Video: Pre-Training and Transfer Learning with RNNs3 min
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Language Modeling
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NLP Dataloaders13 min
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Video: Language Modeling Data3 min
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Build with Pre-Trained Language Model8 min
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Video: Language Modeling2 min
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Transfer Learning
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NLP Transfer Learning27 min
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Video: Transfer Learning with RNNs3 min
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Sanity Checks
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Transfer Learning vs Model from Scratch7 min
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Video: Transfer Learning vs Model from Scratch3 min
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Feedback
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Feedback: Pre-Training and Transfer Learning for RNNs
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18) Continue your NLP Learning Journey
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19) Introduction to Production
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20) Production: Batch Interface Lab
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21) Production: Serving with a User Interface
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22) Production: Building an Online Inference Service
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Mini-Project: Production
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Capstone Project
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Deep Learning Capstone Introduction3 min
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Video: Capstone Project Introduction4 min
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Problem Statement3 min
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Data Collection4 min
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Data Splitting, EDA, and Feature Engineering7 min
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ML Model Training & Selection5 min
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Production3 min
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Report3 min
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Presentation3 min
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Feedback
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Feedback: Capstone Project
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Completion Certificate