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[FreeCourseSite.com] Udemy - Complete 2022 Data Science & Machine Learning Bootcamp
magnet:?xt=urn:btih:2878a661db0025b802912ab5584b7b2a9c539eee&dn=[FreeCourseSite.com] Udemy - Complete 2022 Data Science & Machine Learning Bootcamp
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2878a661db0025b802912ab5584b7b2a9c539eee
infohash:
176
文件数量
17.07 GB
文件大小
2022-4-25 03:28
创建日期
2024-7-5 12:50
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相关分词
FreeCourseSite
com
Udemy
-
Complete
2022
Data
Science
&
Machine
Learning
Bootcamp
01. Introduction to the Course/01. What is Machine Learning.mp4 45.29 MB
01. Introduction to the Course/02. What is Data Science.mp4 42.86 MB
02. Predict Movie Box Office Revenue with Linear Regression/01. Introduction to Linear Regression & Specifying the Problem.mp4 30.32 MB
02. Predict Movie Box Office Revenue with Linear Regression/02. Gather & Clean the Data.mp4 97.02 MB
02. Predict Movie Box Office Revenue with Linear Regression/03. Explore & Visualise the Data with Python.mp4 148.15 MB
02. Predict Movie Box Office Revenue with Linear Regression/04. The Intuition behind the Linear Regression Model.mp4 29.63 MB
02. Predict Movie Box Office Revenue with Linear Regression/05. Analyse and Evaluate the Results.mp4 105.16 MB
03. Python Programming for Data Science and Machine Learning/01. Windows Users - Install Anaconda.mp4 49.6 MB
03. Python Programming for Data Science and Machine Learning/02. Mac Users - Install Anaconda.mp4 52.41 MB
03. Python Programming for Data Science and Machine Learning/03. Does LSD Make You Better at Maths.mp4 42.25 MB
03. Python Programming for Data Science and Machine Learning/05. [Python] - Variables and Types.mp4 71.36 MB
03. Python Programming for Data Science and Machine Learning/07. [Python] - Lists and Arrays.mp4 53.47 MB
03. Python Programming for Data Science and Machine Learning/09. [Python & Pandas] - Dataframes and Series.mp4 153.2 MB
03. Python Programming for Data Science and Machine Learning/10. [Python] - Module Imports.mp4 232.07 MB
03. Python Programming for Data Science and Machine Learning/11. [Python] - Functions - Part 1 Defining and Calling Functions.mp4 41.61 MB
03. Python Programming for Data Science and Machine Learning/13. [Python] - Functions - Part 2 Arguments & Parameters.mp4 128.2 MB
03. Python Programming for Data Science and Machine Learning/15. [Python] - Functions - Part 3 Results & Return Values.mp4 82.63 MB
03. Python Programming for Data Science and Machine Learning/17. [Python] - Objects - Understanding Attributes and Methods.mp4 156.77 MB
03. Python Programming for Data Science and Machine Learning/18. How to Make Sense of Python Documentation for Data Visualisation.mp4 171.46 MB
03. Python Programming for Data Science and Machine Learning/19. Working with Python Objects to Analyse Data.mp4 169.98 MB
03. Python Programming for Data Science and Machine Learning/20. [Python] - Tips, Code Style and Naming Conventions.mp4 81.53 MB
04. Introduction to Optimisation and the Gradient Descent Algorithm/01. What's Coming Up.mp4 20.92 MB
04. Introduction to Optimisation and the Gradient Descent Algorithm/02. How a Machine Learns.mp4 22.78 MB
04. Introduction to Optimisation and the Gradient Descent Algorithm/03. Introduction to Cost Functions.mp4 66.21 MB
04. Introduction to Optimisation and the Gradient Descent Algorithm/04. LaTeX Markdown and Generating Data with Numpy.mp4 90.52 MB
04. Introduction to Optimisation and the Gradient Descent Algorithm/05. Understanding the Power Rule & Creating Charts with Subplots.mp4 90.17 MB
04. Introduction to Optimisation and the Gradient Descent Algorithm/06. [Python] - Loops and the Gradient Descent Algorithm.mp4 287.46 MB
04. Introduction to Optimisation and the Gradient Descent Algorithm/08. [Python] - Advanced Functions and the Pitfalls of Optimisation (Part 1).mp4 291.33 MB
04. Introduction to Optimisation and the Gradient Descent Algorithm/09. [Python] - Tuples and the Pitfalls of Optimisation (Part 2).mp4 219.01 MB
04. Introduction to Optimisation and the Gradient Descent Algorithm/10. Understanding the Learning Rate.mp4 236.6 MB
04. Introduction to Optimisation and the Gradient Descent Algorithm/11. How to Create 3-Dimensional Charts.mp4 193.48 MB
04. Introduction to Optimisation and the Gradient Descent Algorithm/12. Understanding Partial Derivatives and How to use SymPy.mp4 132.81 MB
04. Introduction to Optimisation and the Gradient Descent Algorithm/13. Implementing Batch Gradient Descent with SymPy.mp4 86.82 MB
04. Introduction to Optimisation and the Gradient Descent Algorithm/14. [Python] - Loops and Performance Considerations.mp4 131.07 MB
04. Introduction to Optimisation and the Gradient Descent Algorithm/15. Reshaping and Slicing N-Dimensional Arrays.mp4 140.81 MB
04. Introduction to Optimisation and the Gradient Descent Algorithm/16. Concatenating Numpy Arrays.mp4 71.33 MB
04. Introduction to Optimisation and the Gradient Descent Algorithm/17. Introduction to the Mean Squared Error (MSE).mp4 64.56 MB
04. Introduction to Optimisation and the Gradient Descent Algorithm/18. Transposing and Reshaping Arrays.mp4 86.9 MB
04. Introduction to Optimisation and the Gradient Descent Algorithm/19. Implementing a MSE Cost Function.mp4 81.11 MB
04. Introduction to Optimisation and the Gradient Descent Algorithm/20. Understanding Nested Loops and Plotting the MSE Function (Part 1).mp4 73.16 MB
04. Introduction to Optimisation and the Gradient Descent Algorithm/21. Plotting the Mean Squared Error (MSE) on a Surface (Part 2).mp4 124.88 MB
04. Introduction to Optimisation and the Gradient Descent Algorithm/22. Running Gradient Descent with a MSE Cost Function.mp4 111.22 MB
04. Introduction to Optimisation and the Gradient Descent Algorithm/23. Visualising the Optimisation on a 3D Surface.mp4 74.81 MB
05. Predict House Prices with Multivariable Linear Regression/01. Defining the Problem.mp4 39.91 MB
05. Predict House Prices with Multivariable Linear Regression/02. Gathering the Boston House Price Data.mp4 56.24 MB
05. Predict House Prices with Multivariable Linear Regression/03. Clean and Explore the Data (Part 1) Understand the Nature of the Dataset.mp4 87.14 MB
05. Predict House Prices with Multivariable Linear Regression/04. Clean and Explore the Data (Part 2) Find Missing Values.mp4 135.02 MB
05. Predict House Prices with Multivariable Linear Regression/05. Visualising Data (Part 1) Historams, Distributions & Outliers.mp4 64.55 MB
05. Predict House Prices with Multivariable Linear Regression/06. Visualising Data (Part 2) Seaborn and Probability Density Functions.mp4 57.32 MB
05. Predict House Prices with Multivariable Linear Regression/07. Working with Index Data, Pandas Series, and Dummy Variables.mp4 140.76 MB
05. Predict House Prices with Multivariable Linear Regression/08. Understanding Descriptive Statistics the Mean vs the Median.mp4 62.18 MB
05. Predict House Prices with Multivariable Linear Regression/09. Introduction to Correlation Understanding Strength & Direction.mp4 33.09 MB
05. Predict House Prices with Multivariable Linear Regression/10. Calculating Correlations and the Problem posed by Multicollinearity.mp4 111.43 MB
05. Predict House Prices with Multivariable Linear Regression/11. Visualising Correlations with a Heatmap.mp4 168.65 MB
05. Predict House Prices with Multivariable Linear Regression/12. Techniques to Style Scatter Plots.mp4 128.53 MB
05. Predict House Prices with Multivariable Linear Regression/14. Working with Seaborn Pairplots & Jupyter Microbenchmarking Techniques.mp4 214.4 MB
05. Predict House Prices with Multivariable Linear Regression/15. Understanding Multivariable Regression.mp4 48.8 MB
05. Predict House Prices with Multivariable Linear Regression/16. How to Shuffle and Split Training & Testing Data.mp4 64.34 MB
05. Predict House Prices with Multivariable Linear Regression/17. Running a Multivariable Regression.mp4 55.56 MB
05. Predict House Prices with Multivariable Linear Regression/18. How to Calculate the Model Fit with R-Squared.mp4 32.4 MB
05. Predict House Prices with Multivariable Linear Regression/19. Introduction to Model Evaluation.mp4 15.99 MB
05. Predict House Prices with Multivariable Linear Regression/20. Improving the Model by Transforming the Data.mp4 126.87 MB
05. Predict House Prices with Multivariable Linear Regression/21. How to Interpret Coefficients using p-Values and Statistical Significance.mp4 65.41 MB
05. Predict House Prices with Multivariable Linear Regression/22. Understanding VIF & Testing for Multicollinearity.mp4 143.82 MB
05. Predict House Prices with Multivariable Linear Regression/23. Model Simplification & Baysian Information Criterion.mp4 150.15 MB
05. Predict House Prices with Multivariable Linear Regression/24. How to Analyse and Plot Regression Residuals.mp4 64.18 MB
05. Predict House Prices with Multivariable Linear Regression/25. Residual Analysis (Part 1) Predicted vs Actual Values.mp4 124.42 MB
05. Predict House Prices with Multivariable Linear Regression/26. Residual Analysis (Part 2) Graphing and Comparing Regression Residuals.mp4 153.01 MB
05. Predict House Prices with Multivariable Linear Regression/27. Making Predictions (Part 1) MSE & R-Squared.mp4 152.68 MB
05. Predict House Prices with Multivariable Linear Regression/28. Making Predictions (Part 2) Standard Deviation, RMSE, and Prediction Intervals.mp4 84.85 MB
05. Predict House Prices with Multivariable Linear Regression/29. Build a Valuation Tool (Part 1) Working with Pandas Series & Numpy ndarrays.mp4 131.31 MB
05. Predict House Prices with Multivariable Linear Regression/30. [Python] - Conditional Statements - Build a Valuation Tool (Part 2).mp4 134.38 MB
05. Predict House Prices with Multivariable Linear Regression/32. Build a Valuation Tool (Part 3) Docstrings & Creating your own Python Module.mp4 244.17 MB
06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/01. How to Translate a Business Problem into a Machine Learning Problem.mp4 42.26 MB
06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/02. Gathering Email Data and Working with Archives & Text Editors.mp4 112.05 MB
06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/03. How to Add the Lesson Resources to the Project.mp4 28.9 MB
06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/04. The Naive Bayes Algorithm and the Decision Boundary for a Classifier.mp4 33.39 MB
06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/05. Basic Probability.mp4 28.55 MB
06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/06. Joint & Conditional Probability.mp4 141.82 MB
06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/07. Bayes Theorem.mp4 83.6 MB
06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/08. Reading Files (Part 1) Absolute Paths and Relative Paths.mp4 60.9 MB
06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/09. Reading Files (Part 2) Stream Objects and Email Structure.mp4 104.32 MB
06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/10. Extracting the Text in the Email Body.mp4 47.43 MB
06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/11. [Python] - Generator Functions & the yield Keyword.mp4 133.16 MB
06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/12. Create a Pandas DataFrame of Email Bodies.mp4 48.66 MB
06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/13. Cleaning Data (Part 1) Check for Empty Emails & Null Entries.mp4 121.94 MB
06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/14. Cleaning Data (Part 2) Working with a DataFrame Index.mp4 61.83 MB
06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/15. Saving a JSON File with Pandas.mp4 56.35 MB
06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/16. Data Visualisation (Part 1) Pie Charts.mp4 90.68 MB
06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/17. Data Visualisation (Part 2) Donut Charts.mp4 61.78 MB
06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/18. Introduction to Natural Language Processing (NLP).mp4 50.81 MB
06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/19. Tokenizing, Removing Stop Words and the Python Set Data Structure.mp4 117.75 MB
06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/20. Word Stemming & Removing Punctuation.mp4 71.44 MB
06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/21. Removing HTML tags with BeautifulSoup.mp4 95.82 MB
06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/22. Creating a Function for Text Processing.mp4 53.91 MB
06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/24. Advanced Subsetting on DataFrames the apply() Function.mp4 83.39 MB
06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/25. [Python] - Logical Operators to Create Subsets and Indices.mp4 86.41 MB
06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/26. Word Clouds & How to install Additional Python Packages.mp4 79.48 MB
06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/27. Creating your First Word Cloud.mp4 98.44 MB
06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/28. Styling the Word Cloud with a Mask.mp4 131.37 MB
06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/29. Solving the Hamlet Challenge.mp4 57.1 MB
06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/30. Styling Word Clouds with Custom Fonts.mp4 127.29 MB
06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/31. Create the Vocabulary for the Spam Classifier.mp4 106.96 MB
06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/32. Coding Challenge Check for Membership in a Collection.mp4 32.34 MB
06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/33. Coding Challenge Find the Longest Email.mp4 54.47 MB
06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/34. Sparse Matrix (Part 1) Split the Training and Testing Data.mp4 87.62 MB
06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/35. Sparse Matrix (Part 2) Data Munging with Nested Loops.mp4 137.23 MB
06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/36. Sparse Matrix (Part 3) Using groupby() and Saving .txt Files.mp4 80.5 MB
06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/37. Coding Challenge Solution Preparing the Test Data.mp4 28.92 MB
06. Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails Part 1/38. Checkpoint Understanding the Data.mp4 96.37 MB
07. Train a Naive Bayes Classifier to Create a Spam Filter Part 2/01. Setting up the Notebook and Understanding Delimiters in a Dataset.mp4 72.5 MB
07. Train a Naive Bayes Classifier to Create a Spam Filter Part 2/02. Create a Full Matrix.mp4 132.24 MB
07. Train a Naive Bayes Classifier to Create a Spam Filter Part 2/03. Count the Tokens to Train the Naive Bayes Model.mp4 96.18 MB
07. Train a Naive Bayes Classifier to Create a Spam Filter Part 2/04. Sum the Tokens across the Spam and Ham Subsets.mp4 46.71 MB
07. Train a Naive Bayes Classifier to Create a Spam Filter Part 2/05. Calculate the Token Probabilities and Save the Trained Model.mp4 53.45 MB
07. Train a Naive Bayes Classifier to Create a Spam Filter Part 2/06. Coding Challenge Prepare the Test Data.mp4 35.6 MB
08. Test and Evaluate a Naive Bayes Classifier Part 3/01. Set up the Testing Notebook.mp4 26.45 MB
08. Test and Evaluate a Naive Bayes Classifier Part 3/02. Joint Conditional Probability (Part 1) Dot Product.mp4 66.4 MB
08. Test and Evaluate a Naive Bayes Classifier Part 3/03. Joint Conditional Probablity (Part 2) Priors.mp4 63.98 MB
08. Test and Evaluate a Naive Bayes Classifier Part 3/04. Making Predictions Comparing Joint Probabilities.mp4 52.34 MB
08. Test and Evaluate a Naive Bayes Classifier Part 3/05. The Accuracy Metric.mp4 40.54 MB
08. Test and Evaluate a Naive Bayes Classifier Part 3/06. Visualising the Decision Boundary.mp4 205.31 MB
08. Test and Evaluate a Naive Bayes Classifier Part 3/07. False Positive vs False Negatives.mp4 63.25 MB
08. Test and Evaluate a Naive Bayes Classifier Part 3/08. The Recall Metric.mp4 28.15 MB
08. Test and Evaluate a Naive Bayes Classifier Part 3/09. The Precision Metric.mp4 53.33 MB
08. Test and Evaluate a Naive Bayes Classifier Part 3/10. The F-score or F1 Metric.mp4 24.71 MB
08. Test and Evaluate a Naive Bayes Classifier Part 3/11. A Naive Bayes Implementation using SciKit Learn.mp4 195.1 MB
09. Introduction to Neural Networks and How to Use Pre-Trained Models/01. The Human Brain and the Inspiration for Artificial Neural Networks.mp4 51.81 MB
09. Introduction to Neural Networks and How to Use Pre-Trained Models/02. Layers, Feature Generation and Learning.mp4 146.7 MB
09. Introduction to Neural Networks and How to Use Pre-Trained Models/03. Costs and Disadvantages of Neural Networks.mp4 91.98 MB
09. Introduction to Neural Networks and How to Use Pre-Trained Models/04. Preprocessing Image Data and How RGB Works.mp4 93.6 MB
09. Introduction to Neural Networks and How to Use Pre-Trained Models/05. Importing Keras Models and the Tensorflow Graph.mp4 65.47 MB
09. Introduction to Neural Networks and How to Use Pre-Trained Models/06. Making Predictions using InceptionResNet.mp4 134.58 MB
09. Introduction to Neural Networks and How to Use Pre-Trained Models/07. Coding Challenge Solution Using other Keras Models.mp4 103.53 MB
10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/01. Solving a Business Problem with Image Classification.mp4 30.53 MB
10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/02. Installing Tensorflow and Keras for Jupyter.mp4 42.1 MB
10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/03. Gathering the CIFAR 10 Dataset.mp4 31.37 MB
10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/04. Exploring the CIFAR Data.mp4 110.31 MB
10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/05. Pre-processing Scaling Inputs and Creating a Validation Dataset.mp4 93.16 MB
10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/06. Compiling a Keras Model and Understanding the Cross Entropy Loss Function.mp4 103.6 MB
10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/07. Interacting with the Operating System and the Python Try-Catch Block.mp4 133.41 MB
10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/08. Fit a Keras Model and Use Tensorboard to Visualise Learning and Spot Problems.mp4 100.42 MB
10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/09. Use Regularisation to Prevent Overfitting Early Stopping & Dropout Techniques.mp4 191.54 MB
10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/10. Use the Model to Make Predictions.mp4 218.25 MB
10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/11. Model Evaluation and the Confusion Matrix.mp4 62.76 MB
10. Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/12. Model Evaluation and the Confusion Matrix.mp4 251.83 MB
11. Use Tensorflow to Classify Handwritten Digits/01. What's coming up.mp4 7.1 MB
11. Use Tensorflow to Classify Handwritten Digits/02. Getting the Data and Loading it into Numpy Arrays.mp4 52.82 MB
11. Use Tensorflow to Classify Handwritten Digits/03. Data Exploration and Understanding the Structure of the Input Data.mp4 32.41 MB
11. Use Tensorflow to Classify Handwritten Digits/04. Data Preprocessing One-Hot Encoding and Creating the Validation Dataset.mp4 70.19 MB
11. Use Tensorflow to Classify Handwritten Digits/05. What is a Tensor.mp4 45.39 MB
11. Use Tensorflow to Classify Handwritten Digits/06. Creating Tensors and Setting up the Neural Network Architecture.mp4 150.86 MB
11. Use Tensorflow to Classify Handwritten Digits/07. Defining the Cross Entropy Loss Function, the Optimizer and the Metrics.mp4 75.11 MB
11. Use Tensorflow to Classify Handwritten Digits/08. TensorFlow Sessions and Batching Data.mp4 100.32 MB
11. Use Tensorflow to Classify Handwritten Digits/09. Tensorboard Summaries and the Filewriter.mp4 128.29 MB
11. Use Tensorflow to Classify Handwritten Digits/10. Understanding the Tensorflow Graph Nodes and Edges.mp4 115.75 MB
11. Use Tensorflow to Classify Handwritten Digits/11. Name Scoping and Image Visualisation in Tensorboard.mp4 155.37 MB
11. Use Tensorflow to Classify Handwritten Digits/12. Different Model Architectures Experimenting with Dropout.mp4 213.67 MB
11. Use Tensorflow to Classify Handwritten Digits/13. Prediction and Model Evaluation.mp4 110.72 MB
12. Serving a Tensorflow Model through a Website/01. What you'll make.mp4 38.44 MB
12. Serving a Tensorflow Model through a Website/02. Saving Tensorflow Models.mp4 109.98 MB
12. Serving a Tensorflow Model through a Website/03. Loading a SavedModel.mp4 103.93 MB
12. Serving a Tensorflow Model through a Website/04. Converting a Model to Tensorflow.js.mp4 132.49 MB
12. Serving a Tensorflow Model through a Website/05. Introducing the Website Project and Tooling.mp4 78.04 MB
12. Serving a Tensorflow Model through a Website/06. HTML and CSS Styling.mp4 150.23 MB
12. Serving a Tensorflow Model through a Website/07. Loading a Tensorflow.js Model and Starting your own Server.mp4 188.04 MB
12. Serving a Tensorflow Model through a Website/08. Adding a Favicon.mp4 41.51 MB
12. Serving a Tensorflow Model through a Website/09. Styling an HTML Canvas.mp4 187.37 MB
12. Serving a Tensorflow Model through a Website/10. Drawing on an HTML Canvas.mp4 171.97 MB
12. Serving a Tensorflow Model through a Website/11. Data Pre-Processing for Tensorflow.js.mp4 61.89 MB
12. Serving a Tensorflow Model through a Website/12. Introduction to OpenCV.mp4 235.33 MB
12. Serving a Tensorflow Model through a Website/13. Resizing and Adding Padding to Images.mp4 157.5 MB
12. Serving a Tensorflow Model through a Website/14. Calculating the Centre of Mass and Shifting the Image.mp4 223.26 MB
12. Serving a Tensorflow Model through a Website/15. Making a Prediction from a Digit drawn on the HTML Canvas.mp4 104.41 MB
12. Serving a Tensorflow Model through a Website/16. Adding the Game Logic.mp4 172.83 MB
12. Serving a Tensorflow Model through a Website/17. Publish and Share your Website!.mp4 38.75 MB
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