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种子名称： [FreeCourseSite.com] Udemy - Complete 2022 Data Science & Machine Learning Bootcamp
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文件数目： 176个文件
文件大小： 12.43 GB
收录时间： 2023-5-19 17:03
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最近下载： 2024-11-21 01:20

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[FreeCourseSite.com] Udemy - Complete 2022 Data Science & Machine Learning Bootcamp.torrent

01 - Introduction to the Course/001 What is Machine Learning.mp440.37MB

01 - Introduction to the Course/002 What is Data Science.mp439.83MB

02 - Predict Movie Box Office Revenue with Linear Regression/001 Introduction to Linear Regression & Specifying the Problem.mp426.51MB

02 - Predict Movie Box Office Revenue with Linear Regression/002 Gather & Clean the Data.mp440.86MB

02 - Predict Movie Box Office Revenue with Linear Regression/003 Explore & Visualise the Data with Python.mp4105.12MB

02 - Predict Movie Box Office Revenue with Linear Regression/004 The Intuition behind the Linear Regression Model.mp412.86MB

02 - Predict Movie Box Office Revenue with Linear Regression/005 Analyse and Evaluate the Results.mp475.46MB

03 - Python Programming for Data Science and Machine Learning/001 Windows Users - Install Anaconda.mp432.19MB

03 - Python Programming for Data Science and Machine Learning/002 Mac Users - Install Anaconda.mp439.12MB

03 - Python Programming for Data Science and Machine Learning/003 Does LSD Make You Better at Maths.mp415.63MB

03 - Python Programming for Data Science and Machine Learning/005 [Python] - Variables and Types.mp447.55MB

03 - Python Programming for Data Science and Machine Learning/006 [Python] - Lists and Arrays.mp435.09MB

03 - Python Programming for Data Science and Machine Learning/007 [Python & Pandas] - Dataframes and Series.mp4101.4MB

03 - Python Programming for Data Science and Machine Learning/008 [Python] - Module Imports.mp4186.86MB

03 - Python Programming for Data Science and Machine Learning/009 [Python] - Functions - Part 1 Defining and Calling Functions.mp427.38MB

03 - Python Programming for Data Science and Machine Learning/010 [Python] - Functions - Part 2 Arguments & Parameters.mp499.44MB

03 - Python Programming for Data Science and Machine Learning/011 [Python] - Functions - Part 3 Results & Return Values.mp454.13MB

03 - Python Programming for Data Science and Machine Learning/012 [Python] - Objects - Understanding Attributes and Methods.mp4125.35MB

03 - Python Programming for Data Science and Machine Learning/013 How to Make Sense of Python Documentation for Data Visualisation.mp4138.12MB

03 - Python Programming for Data Science and Machine Learning/014 Working with Python Objects to Analyse Data.mp4135.45MB

03 - Python Programming for Data Science and Machine Learning/015 [Python] - Tips, Code Style and Naming Conventions.mp467.18MB

04 - Introduction to Optimisation and the Gradient Descent Algorithm/001 What's Coming Up.mp412.92MB

04 - Introduction to Optimisation and the Gradient Descent Algorithm/002 How a Machine Learns.mp410.47MB

04 - Introduction to Optimisation and the Gradient Descent Algorithm/003 Introduction to Cost Functions.mp439.02MB

04 - Introduction to Optimisation and the Gradient Descent Algorithm/004 LaTeX Markdown and Generating Data with Numpy.mp447.01MB

04 - Introduction to Optimisation and the Gradient Descent Algorithm/005 Understanding the Power Rule & Creating Charts with Subplots.mp459.49MB

04 - Introduction to Optimisation and the Gradient Descent Algorithm/006 [Python] - Loops and the Gradient Descent Algorithm.mp492.92MB

04 - Introduction to Optimisation and the Gradient Descent Algorithm/007 [Python] - Advanced Functions and the Pitfalls of Optimisation (Part 1).mp4229.5MB

04 - Introduction to Optimisation and the Gradient Descent Algorithm/008 [Python] - Tuples and the Pitfalls of Optimisation (Part 2).mp4145.22MB

04 - Introduction to Optimisation and the Gradient Descent Algorithm/009 Understanding the Learning Rate.mp4190.14MB

04 - Introduction to Optimisation and the Gradient Descent Algorithm/010 How to Create 3-Dimensional Charts.mp4152.02MB

04 - Introduction to Optimisation and the Gradient Descent Algorithm/011 Understanding Partial Derivatives and How to use SymPy.mp4102.67MB

04 - Introduction to Optimisation and the Gradient Descent Algorithm/012 Implementing Batch Gradient Descent with SymPy.mp465.49MB

04 - Introduction to Optimisation and the Gradient Descent Algorithm/013 [Python] - Loops and Performance Considerations.mp4106.59MB

04 - Introduction to Optimisation and the Gradient Descent Algorithm/014 Reshaping and Slicing N-Dimensional Arrays.mp495.11MB

04 - Introduction to Optimisation and the Gradient Descent Algorithm/015 Concatenating Numpy Arrays.mp432.48MB

04 - Introduction to Optimisation and the Gradient Descent Algorithm/016 Introduction to the Mean Squared Error (MSE).mp443.24MB

04 - Introduction to Optimisation and the Gradient Descent Algorithm/017 Transposing and Reshaping Arrays.mp458.02MB

04 - Introduction to Optimisation and the Gradient Descent Algorithm/018 Implementing a MSE Cost Function.mp454.55MB

04 - Introduction to Optimisation and the Gradient Descent Algorithm/019 Understanding Nested Loops and Plotting the MSE Function (Part 1).mp448.88MB

04 - Introduction to Optimisation and the Gradient Descent Algorithm/020 Plotting the Mean Squared Error (MSE) on a Surface (Part 2).mp496.91MB

04 - Introduction to Optimisation and the Gradient Descent Algorithm/021 Running Gradient Descent with a MSE Cost Function.mp474.32MB

04 - Introduction to Optimisation and the Gradient Descent Algorithm/022 Visualising the Optimisation on a 3D Surface.mp435.61MB

05 - Predict House Prices with Multivariable Linear Regression/001 Defining the Problem.mp430.02MB

05 - Predict House Prices with Multivariable Linear Regression/002 Gathering the Boston House Price Data.mp447.58MB

05 - Predict House Prices with Multivariable Linear Regression/003 Clean and Explore the Data (Part 1) Understand the Nature of the Dataset.mp456.73MB

05 - Predict House Prices with Multivariable Linear Regression/004 Clean and Explore the Data (Part 2) Find Missing Values.mp4107.48MB

05 - Predict House Prices with Multivariable Linear Regression/005 Visualising Data (Part 1) Historams, Distributions & Outliers.mp442.62MB

05 - Predict House Prices with Multivariable Linear Regression/006 Visualising Data (Part 2) Seaborn and Probability Density Functions.mp437.6MB

05 - Predict House Prices with Multivariable Linear Regression/007 Working with Index Data, Pandas Series, and Dummy Variables.mp4103.76MB

05 - Predict House Prices with Multivariable Linear Regression/008 Understanding Descriptive Statistics the Mean vs the Median.mp441.03MB

05 - Predict House Prices with Multivariable Linear Regression/009 Introduction to Correlation Understanding Strength & Direction.mp412.92MB

05 - Predict House Prices with Multivariable Linear Regression/010 Calculating Correlations and the Problem posed by Multicollinearity.mp482.51MB

05 - Predict House Prices with Multivariable Linear Regression/011 Visualising Correlations with a Heatmap.mp4108.43MB

05 - Predict House Prices with Multivariable Linear Regression/012 Techniques to Style Scatter Plots.mp483.87MB

05 - Predict House Prices with Multivariable Linear Regression/014 Working with Seaborn Pairplots & Jupyter Microbenchmarking Techniques.mp4175.24MB

05 - Predict House Prices with Multivariable Linear Regression/015 Understanding Multivariable Regression.mp431.51MB

05 - Predict House Prices with Multivariable Linear Regression/016 How to Shuffle and Split Training & Testing Data.mp445.09MB

05 - Predict House Prices with Multivariable Linear Regression/017 Running a Multivariable Regression.mp440.2MB

05 - Predict House Prices with Multivariable Linear Regression/018 How to Calculate the Model Fit with R-Squared.mp421.48MB

05 - Predict House Prices with Multivariable Linear Regression/019 Introduction to Model Evaluation.mp47.34MB

05 - Predict House Prices with Multivariable Linear Regression/020 Improving the Model by Transforming the Data.mp481.41MB

05 - Predict House Prices with Multivariable Linear Regression/021 How to Interpret Coefficients using p-Values and Statistical Significance.mp449.02MB

05 - Predict House Prices with Multivariable Linear Regression/022 Understanding VIF & Testing for Multicollinearity.mp4105.42MB

05 - Predict House Prices with Multivariable Linear Regression/023 Model Simplification & Baysian Information Criterion.mp4119.72MB

05 - Predict House Prices with Multivariable Linear Regression/024 How to Analyse and Plot Regression Residuals.mp428.05MB

05 - Predict House Prices with Multivariable Linear Regression/025 Residual Analysis (Part 1) Predicted vs Actual Values.mp481.48MB

05 - Predict House Prices with Multivariable Linear Regression/026 Residual Analysis (Part 2) Graphing and Comparing Regression Residuals.mp499.19MB

05 - Predict House Prices with Multivariable Linear Regression/027 Making Predictions (Part 1) MSE & R-Squared.mp4126.58MB

05 - Predict House Prices with Multivariable Linear Regression/028 Making Predictions (Part 2) Standard Deviation, RMSE, and Prediction Intervals.mp463.72MB

05 - Predict House Prices with Multivariable Linear Regression/029 Build a Valuation Tool (Part 1) Working with Pandas Series & Numpy ndarrays.mp4102.57MB

05 - Predict House Prices with Multivariable Linear Regression/030 [Python] - Conditional Statements - Build a Valuation Tool (Part 2).mp490.12MB

05 - Predict House Prices with Multivariable Linear Regression/031 Build a Valuation Tool (Part 3) Docstrings & Creating your own Python Module.mp4200.93MB

06 - Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/001 How to Translate a Business Problem into a Machine Learning Problem.mp431.01MB

06 - Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/002 Gathering Email Data and Working with Archives & Text Editors.mp495.99MB

06 - Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/003 How to Add the Lesson Resources to the Project.mp418.95MB

06 - Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/004 The Naive Bayes Algorithm and the Decision Boundary for a Classifier.mp429.41MB

06 - Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/005 Basic Probability.mp49.4MB

06 - Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/006 Joint & Conditional Probability.mp488.31MB

06 - Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/007 Bayes Theorem.mp451.07MB

06 - Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/008 Reading Files (Part 1) Absolute Paths and Relative Paths.mp439.54MB

06 - Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/009 Reading Files (Part 2) Stream Objects and Email Structure.mp487.76MB

06 - Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/010 Extracting the Text in the Email Body.mp430.49MB

06 - Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/011 [Python] - Generator Functions & the yield Keyword.mp4104.22MB

06 - Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/012 Create a Pandas DataFrame of Email Bodies.mp437.37MB

06 - Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/013 Cleaning Data (Part 1) Check for Empty Emails & Null Entries.mp490.33MB

06 - Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/014 Cleaning Data (Part 2) Working with a DataFrame Index.mp446.5MB

06 - Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/015 Saving a JSON File with Pandas.mp443.41MB

06 - Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/016 Data Visualisation (Part 1) Pie Charts.mp470.09MB

06 - Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/017 Data Visualisation (Part 2) Donut Charts.mp440.95MB

06 - Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/018 Introduction to Natural Language Processing (NLP).mp437.45MB

06 - Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/019 Tokenizing, Removing Stop Words and the Python Set Data Structure.mp492.53MB

06 - Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/020 Word Stemming & Removing Punctuation.mp446.64MB

06 - Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/021 Removing HTML tags with BeautifulSoup.mp486.57MB

06 - Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/022 Creating a Function for Text Processing.mp426.36MB

06 - Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/024 Advanced Subsetting on DataFrames the apply() Function.mp455.26MB

06 - Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/025 [Python] - Logical Operators to Create Subsets and Indices.mp457.42MB

06 - Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/026 Word Clouds & How to install Additional Python Packages.mp450.04MB

06 - Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/027 Creating your First Word Cloud.mp445.54MB

06 - Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/028 Styling the Word Cloud with a Mask.mp4105.94MB

06 - Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/029 Solving the Hamlet Challenge.mp446.79MB

06 - Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/030 Styling Word Clouds with Custom Fonts.mp499.44MB

06 - Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/031 Create the Vocabulary for the Spam Classifier.mp470.09MB

06 - Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/032 Coding Challenge Check for Membership in a Collection.mp414.85MB

06 - Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/033 Coding Challenge Find the Longest Email.mp441.1MB

06 - Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/034 Sparse Matrix (Part 1) Split the Training and Testing Data.mp458.08MB

06 - Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/035 Sparse Matrix (Part 2) Data Munging with Nested Loops.mp491.56MB

06 - Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/036 Sparse Matrix (Part 3) Using groupby() and Saving .txt Files.mp461.3MB

06 - Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/037 Coding Challenge Solution Preparing the Test Data.mp418.88MB

06 - Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/038 Checkpoint Understanding the Data.mp474.56MB

07 - Train a Naive Bayes Classifier to Create a Spam Filter Part 2/001 Setting up the Notebook and Understanding Delimiters in a Dataset.mp453.98MB

07 - Train a Naive Bayes Classifier to Create a Spam Filter Part 2/002 Create a Full Matrix.mp4104.85MB

07 - Train a Naive Bayes Classifier to Create a Spam Filter Part 2/003 Count the Tokens to Train the Naive Bayes Model.mp463.64MB

07 - Train a Naive Bayes Classifier to Create a Spam Filter Part 2/004 Sum the Tokens across the Spam and Ham Subsets.mp424.37MB

07 - Train a Naive Bayes Classifier to Create a Spam Filter Part 2/005 Calculate the Token Probabilities and Save the Trained Model.mp435.29MB

07 - Train a Naive Bayes Classifier to Create a Spam Filter Part 2/006 Coding Challenge Prepare the Test Data.mp428.62MB

08 - Test and Evaluate a Naive Bayes Classifier Part 3/001 Set up the Testing Notebook.mp419.97MB

08 - Test and Evaluate a Naive Bayes Classifier Part 3/002 Joint Conditional Probability (Part 1) Dot Product.mp447.03MB

08 - Test and Evaluate a Naive Bayes Classifier Part 3/003 Joint Conditional Probablity (Part 2) Priors.mp445.8MB

08 - Test and Evaluate a Naive Bayes Classifier Part 3/004 Making Predictions Comparing Joint Probabilities.mp437.12MB

08 - Test and Evaluate a Naive Bayes Classifier Part 3/005 The Accuracy Metric.mp428.66MB

08 - Test and Evaluate a Naive Bayes Classifier Part 3/006 Visualising the Decision Boundary.mp4149.45MB

08 - Test and Evaluate a Naive Bayes Classifier Part 3/007 False Positive vs False Negatives.mp441.39MB

08 - Test and Evaluate a Naive Bayes Classifier Part 3/008 The Recall Metric.mp418.41MB

08 - Test and Evaluate a Naive Bayes Classifier Part 3/009 The Precision Metric.mp434.47MB

08 - Test and Evaluate a Naive Bayes Classifier Part 3/010 The F-score or F1 Metric.mp416.46MB

08 - Test and Evaluate a Naive Bayes Classifier Part 3/011 A Naive Bayes Implementation using SciKit Learn.mp4145.69MB

09 - Introduction to Neural Networks and How to Use Pre-Trained Models/001 The Human Brain and the Inspiration for Artificial Neural Networks.mp432.71MB

09 - Introduction to Neural Networks and How to Use Pre-Trained Models/002 Layers, Feature Generation and Learning.mp4124.35MB

09 - Introduction to Neural Networks and How to Use Pre-Trained Models/003 Costs and Disadvantages of Neural Networks.mp476.56MB

09 - Introduction to Neural Networks and How to Use Pre-Trained Models/004 Preprocessing Image Data and How RGB Works.mp469.24MB

09 - Introduction to Neural Networks and How to Use Pre-Trained Models/005 Importing Keras Models and the Tensorflow Graph.mp449.24MB

09 - Introduction to Neural Networks and How to Use Pre-Trained Models/006 Making Predictions using InceptionResNet.mp4103.2MB

09 - Introduction to Neural Networks and How to Use Pre-Trained Models/007 Coding Challenge Solution Using other Keras Models.mp481.98MB

10 - Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/001 Solving a Business Problem with Image Classification.mp419.47MB

10 - Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/002 Installing Tensorflow and Keras for Jupyter.mp431.92MB

10 - Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/003 Gathering the CIFAR 10 Dataset.mp420.6MB

10 - Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/004 Exploring the CIFAR Data.mp481.16MB

10 - Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/005 Pre-processing Scaling Inputs and Creating a Validation Dataset.mp461.32MB

10 - Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/006 Compiling a Keras Model and Understanding the Cross Entropy Loss Function.mp476.04MB

10 - Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/007 Interacting with the Operating System and the Python Try-Catch Block.mp445.79MB

10 - Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/008 Fit a Keras Model and Use Tensorboard to Visualise Learning and Spot Problems.mp476.55MB

10 - Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/009 Use Regularisation to Prevent Overfitting Early Stopping & Dropout Techniques.mp4152.54MB

10 - Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/010 Use the Model to Make Predictions.mp4173.85MB

10 - Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/011 Model Evaluation and the Confusion Matrix.mp440.05MB

10 - Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/012 Model Evaluation and the Confusion Matrix.mp4193.26MB

11 - Use Tensorflow to Classify Handwritten Digits/001 What's coming up.mp45.23MB

11 - Use Tensorflow to Classify Handwritten Digits/002 Getting the Data and Loading it into Numpy Arrays.mp439.66MB

11 - Use Tensorflow to Classify Handwritten Digits/003 Data Exploration and Understanding the Structure of the Input Data.mp420.6MB

11 - Use Tensorflow to Classify Handwritten Digits/004 Data Preprocessing One-Hot Encoding and Creating the Validation Dataset.mp449.34MB

11 - Use Tensorflow to Classify Handwritten Digits/005 What is a Tensor.mp437.85MB

11 - Use Tensorflow to Classify Handwritten Digits/006 Creating Tensors and Setting up the Neural Network Architecture.mp4110.62MB

11 - Use Tensorflow to Classify Handwritten Digits/007 Defining the Cross Entropy Loss Function, the Optimizer and the Metrics.mp449.63MB

11 - Use Tensorflow to Classify Handwritten Digits/008 TensorFlow Sessions and Batching Data.mp473.63MB

11 - Use Tensorflow to Classify Handwritten Digits/009 Tensorboard Summaries and the Filewriter.mp498.73MB

11 - Use Tensorflow to Classify Handwritten Digits/010 Understanding the Tensorflow Graph Nodes and Edges.mp489.48MB

11 - Use Tensorflow to Classify Handwritten Digits/011 Name Scoping and Image Visualisation in Tensorboard.mp466.81MB

11 - Use Tensorflow to Classify Handwritten Digits/012 Different Model Architectures Experimenting with Dropout.mp4173.82MB

11 - Use Tensorflow to Classify Handwritten Digits/013 Prediction and Model Evaluation.mp487.34MB

12 - Serving a Tensorflow Model through a Website/001 What you'll make.mp435.52MB

12 - Serving a Tensorflow Model through a Website/002 Saving Tensorflow Models.mp4103.67MB

12 - Serving a Tensorflow Model through a Website/003 Loading a SavedModel.mp485.1MB

12 - Serving a Tensorflow Model through a Website/004 Converting a Model to Tensorflow.js.mp493.61MB

12 - Serving a Tensorflow Model through a Website/005 Introducing the Website Project and Tooling.mp468.79MB

12 - Serving a Tensorflow Model through a Website/006 HTML and CSS Styling.mp4136.76MB

12 - Serving a Tensorflow Model through a Website/007 Loading a Tensorflow.js Model and Starting your own Server.mp4175.41MB

12 - Serving a Tensorflow Model through a Website/008 Adding a Favicon.mp424.34MB

12 - Serving a Tensorflow Model through a Website/009 Styling an HTML Canvas.mp4172.69MB

12 - Serving a Tensorflow Model through a Website/010 Drawing on an HTML Canvas.mp4159.28MB

12 - Serving a Tensorflow Model through a Website/011 Data Pre-Processing for Tensorflow.js.mp425.56MB

12 - Serving a Tensorflow Model through a Website/012 Introduction to OpenCV.mp4133.24MB

12 - Serving a Tensorflow Model through a Website/013 Resizing and Adding Padding to Images.mp4147.83MB

12 - Serving a Tensorflow Model through a Website/014 Calculating the Centre of Mass and Shifting the Image.mp4210.44MB

12 - Serving a Tensorflow Model through a Website/015 Making a Prediction from a Digit drawn on the HTML Canvas.mp498.46MB

12 - Serving a Tensorflow Model through a Website/016 Adding the Game Logic.mp4158.22MB

12 - Serving a Tensorflow Model through a Website/017 Publish and Share your Website!.mp433.31MB