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Премиум
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    PyTorch for Deep Learning
  • Урок 2. 00:05:54
    Course Welcome and What Is Deep Learning
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    Why Use Machine Learning or Deep Learning
  • Урок 4. 00:05:40
    The Number 1 Rule of Machine Learning and What Is Deep Learning Good For
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    Machine Learning vs. Deep Learning
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    Anatomy of Neural Networks
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    Different Types of Learning Paradigms
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    What Can Deep Learning Be Used For
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    What Is and Why PyTorch
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    What Are Tensors
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    What We Are Going To Cover With PyTorch
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    How To and How Not To Approach This Course
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    Important Resources For This Course
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    Getting Setup to Write PyTorch Code
  • Урок 15. 00:13:26
    Introduction to PyTorch Tensors
  • Урок 16. 00:09:59
    Creating Random Tensors in PyTorch
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    Creating Tensors With Zeros and Ones in PyTorch
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    Creating a Tensor Range and Tensors Like Other Tensors
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    Dealing With Tensor Data Types
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    Getting Tensor Attributes
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    Manipulating Tensors (Tensor Operations)
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    Matrix Multiplication (Part 1)
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    Matrix Multiplication (Part 2): The Two Main Rules of Matrix Multiplication
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    Matrix Multiplication (Part 3): Dealing With Tensor Shape Errors
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    Finding the Min Max Mean and Sum of Tensors (Tensor Aggregation)
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    Finding The Positional Min and Max of Tensors
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    Reshaping, Viewing and Stacking Tensors
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    Squeezing, Unsqueezing and Permuting Tensors
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    Selecting Data From Tensors (Indexing)
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    PyTorch Tensors and NumPy
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    PyTorch Reproducibility (Taking the Random Out of Random)
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    Different Ways of Accessing a GPU in PyTorch
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    Setting up Device Agnostic Code and Putting Tensors On and Off the GPU
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    PyTorch Fundamentals: Exercises and Extra-Curriculum
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    Introduction and Where You Can Get Help
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    Getting Setup and What We Are Covering
  • Урок 37. 00:09:42
    Creating a Simple Dataset Using the Linear Regression Formula
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    Splitting Our Data Into Training and Test Sets
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    Building a function to Visualize Our Data
  • Урок 40. 00:14:10
    Creating Our First PyTorch Model for Linear Regression
  • Урок 41. 00:06:11
    Breaking Down What's Happening in Our PyTorch Linear regression Model
  • Урок 42. 00:06:27
    Discussing Some of the Most Important PyTorch Model Building Classes
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    Checking Out the Internals of Our PyTorch Model
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    Making Predictions With Our Random Model Using Inference Mode
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    Training a Model Intuition (The Things We Need)
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    Setting Up an Optimizer and a Loss Function
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    PyTorch Training Loop Steps and Intuition
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    Writing Code for a PyTorch Training Loop
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    Reviewing the Steps in a Training Loop Step by Step
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    Running Our Training Loop Epoch by Epoch and Seeing What Happens
  • Урок 51. 00:11:38
    Writing Testing Loop Code and Discussing What's Happening Step by Step
  • Урок 52. 00:14:43
    Reviewing What Happens in a Testing Loop Step by Step
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    Writing Code to Save a PyTorch Model
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    Writing Code to Load a PyTorch Model
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    Setting Up to Practice Everything We Have Done Using Device-Agnostic Code
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    Putting Everything Together (Part 1): Data
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    Putting Everything Together (Part 2): Building a Model
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    Putting Everything Together (Part 3): Training a Model
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    Putting Everything Together (Part 4): Making Predictions With a Trained Model
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    Putting Everything Together (Part 5): Saving and Loading a Trained Model
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    Exercise: Imposter Syndrome
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    PyTorch Workflow Exercises: Extra-Curriculum
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    Introduction to Machine Learning Classification With PyTorch
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    Classification Problem Example: Input and Output Shapes
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    Typical Architecture of a Classification Neural Network (Overview)
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    Making a Toy Classification Dataset
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    Turning Our Data into Tensors and Making a Training and Test Split
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    Laying Out Steps for Modelling and Setting Up Device-Agnostic Code
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    Coding a Small Neural Network to Handle Our Classification Data
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    Making Our Neural Network Visual
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    Setting Up a Loss Function Optimizer and Evaluation Function for Our Classification Network
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    Going from Model Logits to Prediction Probabilities to Prediction Labels
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    Coding a Training and Testing Optimization Loop for Our Classification Model
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    Writing Code to Download a Helper Function to Visualize Our Models Predictions
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    Discussing Options to Improve a Model
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    Creating a New Model with More Layers and Hidden Units
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    Writing Training and Testing Code to See if Our New and Upgraded Model Performs Better
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    Creating a Straight Line Dataset to See if Our Model is Learning Anything
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    Building and Training a Model to Fit on Straight Line Data
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    Evaluating Our Models Predictions on Straight Line Data
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    Introducing the Missing Piece for Our Classification Model Non-Linearity
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    Building Our First Neural Network with Non-Linearity
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    Writing Training and Testing Code for Our First Non-Linear Model
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    Making Predictions with and Evaluating Our First Non-Linear Model
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    Replicating Non-Linear Activation Functions with Pure PyTorch
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    Putting It All Together (Part 1): Building a Multiclass Dataset
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    Creating a Multi-Class Classification Model with PyTorch
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    Setting Up a Loss Function and Optimizer for Our Multi-Class Model
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    Going from Logits to Prediction Probabilities to Prediction Labels with a Multi-Class Model
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    Making Predictions with and Evaluating Our Multi-Class Classification Model
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    Discussing a Few More Classification Metrics
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    PyTorch Classification Exercises and Extra-Curriculum
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    What Is a Computer Vision Problem and What We Are Going to Cover
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    Computer Vision Input and Output Shapes (
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    What Is a Convolutional Neural Network (CNN)
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    Discussing and Importing the Base Computer Vision Libraries in PyTorch
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    Getting a Computer Vision Dataset and Checking Out Its- Input and Output Shapes
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    Visualizing Random Samples of Data
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    DataLoader Overview Understanding Mini-Batch
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    Turning Our Datasets Into DataLoaders
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    Model 0: Creating a Baseline Model with Two Linear Layers
  • Урок 102. 00:10:30
    Creating a Loss Function: an Optimizer for Model 0
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    Creating a Function to Time Our Modelling Code
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    Writing Training and Testing Loops for Our Batched Data
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    Writing an Evaluation Function to Get Our Models Results
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    Setup Device-Agnostic Code for Running Experiments on the GPU
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    Model 1: Creating a Model with Non-Linear Functions
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    Mode 1: Creating a Loss Function and Optimizer
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    Turing Our Training Loop into a Function
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    Turing Our Testing Loop into a Function
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    Training and Testing Model 1 with Our Training and Testing Functions
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    Getting a Results Dictionary for Model 1
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    Model 2: Convolutional Neural Networks High Level Overview
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    Model 2: Coding Our First Convolutional Neural Network with PyTorch
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    Model 2: Breaking Down Conv2D Step by Step
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    Model 2: Breaking Down MaxPool2D Step by Step
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    Mode 2: Using a Trick to Find the Input and Output Shapes of Each of Our Layers
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    Model 2: Setting Up a Loss Function and Optimizer
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    Model 2: Training Our First CNN and Evaluating Its Results
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    Comparing the Results of Our Modelling Experiments
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    Making Predictions on Random Test Samples with the Best Trained Model
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    Plotting Our Best Model Predictions on Random Test Samples and Evaluating Them
  • Урок 123. 00:15:21
    Making Predictions Across the Whole Test Dataset and Importing Libraries to Plot a Confusion Matrix
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    Evaluating Our Best Models Predictions with a Confusion Matrix
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    Saving and Loading Our Best Performing Model
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    Recapping What We Have Covered and Exercises and Extra-Curriculum
  • Урок 127. 00:09:54
    What Is a Custom Dataset and What We Are Going to Cover
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    Importing PyTorch and Setting Up Device-Agnostic Code
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    Downloading a Custom Dataset of Pizza, Steak and Sushi Images
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    Becoming One With the Data (Part 1): Exploring the Data Format
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    Becoming One With the Data (Part 2): Visualizing a Random Image
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    Becoming One With the Data (Part 3): Visualizing a Random Image with Matplotlib
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    Transforming Data (Part 1): Turning Images Into Tensors
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    Transforming Data (Part 2): Visualizing Transformed Images
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    Loading All of Our Images and Turning Them Into Tensors With ImageFolder
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    Visualizing a Loaded Image From the Train Dataset
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    Turning Our Image Datasets into PyTorch DataLoaders
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    Creating a Custom Dataset Class in PyTorch High Level Overview
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    Creating a Helper Function to Get Class Names From a Directory
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    Writing a PyTorch Custom Dataset Class from Scratch to Load Our Images
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    Compare Our Custom Dataset Class to the Original ImageFolder Class
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    Writing a Helper Function to Visualize Random Images from Our Custom Dataset
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    Turning Our Custom Datasets Into DataLoaders
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    Exploring State of the Art Data Augmentation With Torchvision Transforms
  • Урок 145. 00:08:16
    Building a Baseline Model (Part 1): Loading and Transforming Data
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    Building a Baseline Model (Part 2): Replicating Tiny VGG from Scratch
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    Building a Baseline Model (Part 3): Doing a Forward Pass to Test Our Model Shapes
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    Using the Torchinfo Package to Get a Summary of Our Model
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    Creating Training and Testing loop Functions
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    Creating a Train Function to Train and Evaluate Our Models
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    Training and Evaluating Model 0 With Our Training Functions
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    Plotting the Loss Curves of Model 0
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    Discussing the Balance Between Overfitting and Underfitting and How to Deal With Each
  • Урок 154. 00:11:04
    Creating Augmented Training Datasets and DataLoaders for Model 1
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    Constructing and Training Model 1
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    Plotting the Loss Curves of Model 1
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    Plotting the Loss Curves of All of Our Models Against Each Other
  • Урок 158. 00:05:33
    Predicting on Custom Data (Part 1): Downloading an Image
  • Урок 159. 00:07:01
    Predicting on Custom Data (Part2): Loading In a Custom Image With PyTorch
  • Урок 160. 00:14:08
    Predicting on Custom Data (Part 3): Getting Our Custom Image Into the Right Format
  • Урок 161. 00:04:25
    Predicting on Custom Data (Part 4): Turning Our Models Raw Outputs Into Prediction Labels
  • Урок 162. 00:12:48
    Predicting on Custom Data (Part 5): Putting It All Together
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    Summary of What We Have Covered Plus Exercises and Extra-Curriculum
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    What Is Going Modular and What We Are Going to Cover
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    Going Modular Notebook (Part 1): Running It End to End
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    Downloading a Dataset
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    Writing the Outline for Our First Python Script to Setup the Data
  • Урок 168. 00:10:36
    Creating a Python Script to Create Our PyTorch DataLoaders
  • Урок 169. 00:09:19
    Turning Our Model Building Code into a Python Script
  • Урок 170. 00:06:17
    Turning Our Model Training Code into a Python Script
  • Урок 171. 00:06:08
    Turning Our Utility Function to Save a Model into a Python Script
  • Урок 172. 00:15:47
    Creating a Training Script to Train Our Model in One Line of Code
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    Going Modular Summary Exercises and Extra-Curriculum
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    Thank You!