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[08-2020] python-data-science-machine-learning-bootcamp

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08-2020 python-data-science-machine-learning-bootcamp

文件列表
  1. 04 Introduction to Optimisation and the Gradient Descent Algorithm/036 [Python] - Loops and the Gradient Descent Algorithm.mp4449.2MB
  2. 01 Introduction to the Course/001 What is Machine Learning_.mp432.37MB
  3. 01 Introduction to the Course/002 What is Data Science_.mp471.61MB
  4. 02 Predict Movie Box Office Revenue with Linear Regression/006 Introduction to Linear Regression & Specifying the Problem.mp438.77MB
  5. 02 Predict Movie Box Office Revenue with Linear Regression/007 Gather & Clean the Data.mp469.03MB
  6. 02 Predict Movie Box Office Revenue with Linear Regression/008 Explore & Visualise the Data with Python.mp4188.77MB
  7. 02 Predict Movie Box Office Revenue with Linear Regression/009 The Intuition behind the Linear Regression Model.mp419.46MB
  8. 02 Predict Movie Box Office Revenue with Linear Regression/010 Analyse and Evaluate the Results.mp4142.22MB
  9. 03 Python Programming for Data Science and Machine Learning/014 Windows Users - Install Anaconda.mp458.18MB
  10. 03 Python Programming for Data Science and Machine Learning/015 Mac Users - Install Anaconda.mp481.31MB
  11. 03 Python Programming for Data Science and Machine Learning/016 Does LSD Make You Better at Maths_.mp466.9MB
  12. 03 Python Programming for Data Science and Machine Learning/018 [Python] - Variables and Types.mp484.28MB
  13. 03 Python Programming for Data Science and Machine Learning/019 [Python] - Lists and Arrays.mp460.9MB
  14. 03 Python Programming for Data Science and Machine Learning/020 [Python & Pandas] - Dataframes and Series.mp4203.61MB
  15. 03 Python Programming for Data Science and Machine Learning/021 [Python] - Module Imports.mp4349.51MB
  16. 03 Python Programming for Data Science and Machine Learning/022 [Python] - Functions - Part 1_ Defining and Calling Functions.mp446.99MB
  17. 03 Python Programming for Data Science and Machine Learning/023 [Python] - Functions - Part 2_ Arguments & Parameters.mp4189.68MB
  18. 03 Python Programming for Data Science and Machine Learning/024 [Python] - Functions - Part 3_ Results & Return Values.mp494.36MB
  19. 03 Python Programming for Data Science and Machine Learning/025 [Python] - Objects - Understanding Attributes and Methods.mp4234.49MB
  20. 03 Python Programming for Data Science and Machine Learning/026 How to Make Sense of Python Documentation for Data Visualisation.mp4265.05MB
  21. 03 Python Programming for Data Science and Machine Learning/027 Working with Python Objects to Analyse Data.mp4258.83MB
  22. 03 Python Programming for Data Science and Machine Learning/028 [Python] - Tips, Code Style and Naming Conventions.mp4130.73MB
  23. 04 Introduction to Optimisation and the Gradient Descent Algorithm/031 What's Coming Up_.mp424.59MB
  24. 04 Introduction to Optimisation and the Gradient Descent Algorithm/032 How a Machine Learns.mp416.56MB
  25. 04 Introduction to Optimisation and the Gradient Descent Algorithm/033 Introduction to Cost Functions.mp476.86MB
  26. 04 Introduction to Optimisation and the Gradient Descent Algorithm/034 LaTeX Markdown and Generating Data with Numpy.mp477.24MB
  27. 04 Introduction to Optimisation and the Gradient Descent Algorithm/035 Understanding the Power Rule & Creating Charts with Subplots.mp4104.46MB
  28. 04 Introduction to Optimisation and the Gradient Descent Algorithm/037 [Python] - Advanced Functions and the Pitfalls of Optimisation (Part 1).mp4446.84MB
  29. 04 Introduction to Optimisation and the Gradient Descent Algorithm/038 [Python] - Tuples and the Pitfalls of Optimisation (Part 2).mp4302.32MB
  30. 04 Introduction to Optimisation and the Gradient Descent Algorithm/039 Understanding the Learning Rate.mp4280.53MB
  31. 04 Introduction to Optimisation and the Gradient Descent Algorithm/040 How to Create 3-Dimensional Charts.mp4294.27MB
  32. 04 Introduction to Optimisation and the Gradient Descent Algorithm/041 Understanding Partial Derivatives and How to use SymPy.mp4193.33MB
  33. 04 Introduction to Optimisation and the Gradient Descent Algorithm/042 Implementing Batch Gradient Descent with SymPy.mp4117.53MB
  34. 04 Introduction to Optimisation and the Gradient Descent Algorithm/043 [Python] - Loops and Performance Considerations.mp4205.61MB
  35. 04 Introduction to Optimisation and the Gradient Descent Algorithm/044 Reshaping and Slicing N-Dimensional Arrays.mp4168.28MB
  36. 04 Introduction to Optimisation and the Gradient Descent Algorithm/045 Concatenating Numpy Arrays.mp485.15MB
  37. 04 Introduction to Optimisation and the Gradient Descent Algorithm/046 Introduction to the Mean Squared Error (MSE).mp475.17MB
  38. 04 Introduction to Optimisation and the Gradient Descent Algorithm/047 Transposing and Reshaping Arrays.mp4101.68MB
  39. 04 Introduction to Optimisation and the Gradient Descent Algorithm/048 Implementing a MSE Cost Function.mp497.47MB
  40. 04 Introduction to Optimisation and the Gradient Descent Algorithm/049 Understanding Nested Loops and Plotting the MSE Function (Part 1).mp483.37MB
  41. 04 Introduction to Optimisation and the Gradient Descent Algorithm/050 Plotting the Mean Squared Error (MSE) on a Surface (Part 2).mp468.86MB
  42. 04 Introduction to Optimisation and the Gradient Descent Algorithm/051 Running Gradient Descent with a MSE Cost Function.mp4129.83MB
  43. 04 Introduction to Optimisation and the Gradient Descent Algorithm/052 Visualising the Optimisation on a 3D Surface.mp488.19MB
  44. 05 Predict House Prices with Multivariable Linear Regression/055 Defining the Problem.mp457.82MB
  45. 05 Predict House Prices with Multivariable Linear Regression/056 Gathering the Boston House Price Data.mp491.05MB
  46. 05 Predict House Prices with Multivariable Linear Regression/057 Clean and Explore the Data (Part 1)_ Understand the Nature of the Dataset.mp499.37MB
  47. 05 Predict House Prices with Multivariable Linear Regression/058 Clean and Explore the Data (Part 2)_ Find Missing Values.mp4207.91MB
  48. 05 Predict House Prices with Multivariable Linear Regression/059 Visualising Data (Part 1)_ Historams, Distributions & Outliers.mp471.94MB
  49. 05 Predict House Prices with Multivariable Linear Regression/060 Visualising Data (Part 2)_ Seaborn and Probability Density Functions.mp466.18MB
  50. 05 Predict House Prices with Multivariable Linear Regression/061 Working with Index Data, Pandas Series, and Dummy Variables.mp4195.59MB
  51. 05 Predict House Prices with Multivariable Linear Regression/062 Understanding Descriptive Statistics_ the Mean vs the Median.mp472.5MB
  52. 05 Predict House Prices with Multivariable Linear Regression/063 Introduction to Correlation_ Understanding Strength & Direction.mp420.4MB
  53. 05 Predict House Prices with Multivariable Linear Regression/064 Calculating Correlations and the Problem posed by Multicollinearity.mp4154.88MB
  54. 05 Predict House Prices with Multivariable Linear Regression/065 Visualising Correlations with a Heatmap.mp4191.5MB
  55. 05 Predict House Prices with Multivariable Linear Regression/066 Techniques to Style Scatter Plots.mp4149.97MB
  56. 05 Predict House Prices with Multivariable Linear Regression/068 Working with Seaborn Pairplots & Jupyter Microbenchmarking Techniques.mp4334.45MB
  57. 05 Predict House Prices with Multivariable Linear Regression/069 Understanding Multivariable Regression.mp461.6MB
  58. 05 Predict House Prices with Multivariable Linear Regression/070 How to Shuffle and Split Training & Testing Data.mp483.32MB
  59. 05 Predict House Prices with Multivariable Linear Regression/071 Running a Multivariable Regression.mp474.05MB
  60. 05 Predict House Prices with Multivariable Linear Regression/072 How to Calculate the Model Fit with R-Squared.mp438.56MB
  61. 05 Predict House Prices with Multivariable Linear Regression/073 Introduction to Model Evaluation.mp412.03MB
  62. 05 Predict House Prices with Multivariable Linear Regression/074 Improving the Model by Transforming the Data.mp4142.49MB
  63. 05 Predict House Prices with Multivariable Linear Regression/075 How to Interpret Coefficients using p-Values and Statistical Significance.mp489.12MB
  64. 05 Predict House Prices with Multivariable Linear Regression/076 Understanding VIF & Testing for Multicollinearity.mp4164.6MB
  65. 05 Predict House Prices with Multivariable Linear Regression/077 Model Simplification & Baysian Information Criterion.mp4229.47MB
  66. 05 Predict House Prices with Multivariable Linear Regression/078 How to Analyse and Plot Regression Residuals.mp446.63MB
  67. 05 Predict House Prices with Multivariable Linear Regression/079 Residual Analysis (Part 1)_ Predicted vs Actual Values.mp4146.46MB
  68. 05 Predict House Prices with Multivariable Linear Regression/080 Residual Analysis (Part 2)_ Graphing and Comparing Regression Residuals.mp4178.21MB
  69. 05 Predict House Prices with Multivariable Linear Regression/081 Making Predictions (Part 1)_ MSE & R-Squared.mp4217.8MB
  70. 05 Predict House Prices with Multivariable Linear Regression/082 Making Predictions (Part 2)_ Standard Deviation, RMSE, and Prediction Intervals.mp4116.49MB
  71. 05 Predict House Prices with Multivariable Linear Regression/083 Build a Valuation Tool (Part 1)_ Working with Pandas Series & Numpy ndarrays.mp4198.42MB
  72. 05 Predict House Prices with Multivariable Linear Regression/084 [Python] - Conditional Statements - Build a Valuation Tool (Part 2).mp4159.88MB
  73. 05 Predict House Prices with Multivariable Linear Regression/085 Build a Valuation Tool (Part 3)_ Docstrings & Creating your own Python Module.mp4167.92MB
  74. 06 Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails_ Part 1/088 How to Translate a Business Problem into a Machine Learning Problem.mp457.44MB
  75. 06 Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails_ Part 1/089 Gathering Email Data and Working with Archives & Text Editors.mp4186.87MB
  76. 06 Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails_ Part 1/090 How to Add the Lesson Resources to the Project.mp435.07MB
  77. 06 Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails_ Part 1/091 The Naive Bayes Algorithm and the Decision Boundary for a Classifier.mp457.15MB
  78. 06 Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails_ Part 1/092 Basic Probability.mp415.52MB
  79. 06 Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails_ Part 1/093 Joint & Conditional Probability.mp4169.32MB
  80. 06 Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails_ Part 1/094 Bayes Theorem.mp494.54MB
  81. 06 Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails_ Part 1/095 Reading Files (Part 1)_ Absolute Paths and Relative Paths.mp471.15MB
  82. 06 Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails_ Part 1/096 Reading Files (Part 2)_ Stream Objects and Email Structure.mp4167.33MB
  83. 06 Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails_ Part 1/097 Extracting the Text in the Email Body.mp455.23MB
  84. 06 Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails_ Part 1/098 [Python] - Generator Functions & the yield Keyword.mp4197.71MB
  85. 06 Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails_ Part 1/099 Create a Pandas DataFrame of Email Bodies.mp467.91MB
  86. 06 Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails_ Part 1/100 Cleaning Data (Part 1)_ Check for Empty Emails & Null Entries.mp4191.89MB
  87. 06 Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails_ Part 1/101 Cleaning Data (Part 2)_ Working with a DataFrame Index.mp484.91MB
  88. 06 Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails_ Part 1/102 Saving a JSON File with Pandas.mp481.28MB
  89. 06 Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails_ Part 1/103 Data Visualisation (Part 1)_ Pie Charts.mp4131.08MB
  90. 06 Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails_ Part 1/104 Data Visualisation (Part 2)_ Donut Charts.mp473.4MB
  91. 06 Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails_ Part 1/105 Introduction to Natural Language Processing (NLP).mp458.19MB
  92. 06 Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails_ Part 1/106 Tokenizing, Removing Stop Words and the Python Set Data Structure.mp4138.05MB
  93. 06 Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails_ Part 1/107 Word Stemming & Removing Punctuation.mp483.68MB
  94. 06 Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails_ Part 1/108 Removing HTML tags with BeautifulSoup.mp4167.47MB
  95. 06 Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails_ Part 1/109 Creating a Function for Text Processing.mp444.76MB
  96. 06 Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails_ Part 1/111 Advanced Subsetting on DataFrames_ the apply() Function.mp497.91MB
  97. 06 Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails_ Part 1/112 [Python] - Logical Operators to Create Subsets and Indices.mp4101.3MB
  98. 06 Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails_ Part 1/113 Word Clouds & How to install Additional Python Packages.mp492.38MB
  99. 06 Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails_ Part 1/114 Creating your First Word Cloud.mp4152.62MB
  100. 06 Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails_ Part 1/115 Styling the Word Cloud with a Mask.mp4184.63MB
  101. 06 Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails_ Part 1/116 Solving the Hamlet Challenge.mp492.62MB
  102. 06 Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails_ Part 1/117 Styling Word Clouds with Custom Fonts.mp4184.2MB
  103. 06 Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails_ Part 1/118 Create the Vocabulary for the Spam Classifier.mp4124.04MB
  104. 06 Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails_ Part 1/119 Coding Challenge_ Check for Membership in a Collection.mp419.91MB
  105. 06 Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails_ Part 1/120 Coding Challenge_ Find the Longest Email.mp476.59MB
  106. 06 Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails_ Part 1/121 Sparse Matrix (Part 1)_ Split the Training and Testing Data.mp4119.49MB
  107. 06 Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails_ Part 1/122 Sparse Matrix (Part 2)_ Data Munging with Nested Loops.mp4160.46MB
  108. 06 Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails_ Part 1/123 Sparse Matrix (Part 3)_ Using groupby() and Saving .txt Files.mp4110.82MB
  109. 06 Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails_ Part 1/124 Coding Challenge Solution_ Preparing the Test Data.mp437.54MB
  110. 06 Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails_ Part 1/125 Checkpoint_ Understanding the Data.mp4134.39MB
  111. 07 Train a Naive Bayes Classifier to Create a Spam Filter_ Part 2/128 Setting up the Notebook and Understanding Delimiters in a Dataset.mp499.17MB
  112. 07 Train a Naive Bayes Classifier to Create a Spam Filter_ Part 2/129 Create a Full Matrix.mp4200.5MB
  113. 07 Train a Naive Bayes Classifier to Create a Spam Filter_ Part 2/130 Count the Tokens to Train the Naive Bayes Model.mp4111.37MB
  114. 07 Train a Naive Bayes Classifier to Create a Spam Filter_ Part 2/131 Sum the Tokens across the Spam and Ham Subsets.mp439.64MB
  115. 07 Train a Naive Bayes Classifier to Create a Spam Filter_ Part 2/132 Calculate the Token Probabilities and Save the Trained Model.mp470.53MB
  116. 07 Train a Naive Bayes Classifier to Create a Spam Filter_ Part 2/133 Coding Challenge_ Prepare the Test Data.mp453.69MB
  117. 08 Test and Evaluate a Naive Bayes Classifier_ Part 3/136 Set up the Testing Notebook.mp436.94MB
  118. 08 Test and Evaluate a Naive Bayes Classifier_ Part 3/137 Joint Conditional Probability (Part 1)_ Dot Product.mp484.72MB
  119. 08 Test and Evaluate a Naive Bayes Classifier_ Part 3/138 Joint Conditional Probablity (Part 2)_ Priors.mp490.7MB
  120. 08 Test and Evaluate a Naive Bayes Classifier_ Part 3/139 Making Predictions_ Comparing Joint Probabilities.mp468.14MB
  121. 08 Test and Evaluate a Naive Bayes Classifier_ Part 3/140 The Accuracy Metric.mp446.42MB
  122. 08 Test and Evaluate a Naive Bayes Classifier_ Part 3/141 Visualising the Decision Boundary.mp4277.29MB
  123. 08 Test and Evaluate a Naive Bayes Classifier_ Part 3/142 False Positive vs False Negatives.mp471.62MB
  124. 08 Test and Evaluate a Naive Bayes Classifier_ Part 3/143 The Recall Metric.mp431.99MB
  125. 08 Test and Evaluate a Naive Bayes Classifier_ Part 3/144 The Precision Metric.mp461.48MB
  126. 08 Test and Evaluate a Naive Bayes Classifier_ Part 3/145 The F-score or F1 Metric.mp428.68MB
  127. 08 Test and Evaluate a Naive Bayes Classifier_ Part 3/146 A Naive Bayes Implementation using SciKit Learn.mp4270.52MB
  128. 09 Introduction to Neural Networks and How to Use Pre-Trained Models/149 The Human Brain and the Inspiration for Artificial Neural Networks.mp460.54MB
  129. 09 Introduction to Neural Networks and How to Use Pre-Trained Models/150 Layers, Feature Generation and Learning.mp4237.2MB
  130. 09 Introduction to Neural Networks and How to Use Pre-Trained Models/151 Costs and Disadvantages of Neural Networks.mp4145.87MB
  131. 09 Introduction to Neural Networks and How to Use Pre-Trained Models/152 Preprocessing Image Data and How RGB Works.mp4129.57MB
  132. 09 Introduction to Neural Networks and How to Use Pre-Trained Models/153 Importing Keras Models and the Tensorflow Graph.mp473.36MB
  133. 09 Introduction to Neural Networks and How to Use Pre-Trained Models/154 Making Predictions using InceptionResNet.mp4181.21MB
  134. 09 Introduction to Neural Networks and How to Use Pre-Trained Models/155 Coding Challenge Solution_ Using other Keras Models.mp4154.95MB
  135. 10 Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/158 Solving a Business Problem with Image Classification.mp437.55MB
  136. 10 Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/159 Installing Tensorflow and Keras for Jupyter.mp458.44MB
  137. 10 Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/160 Gathering the CIFAR 10 Dataset.mp435.34MB
  138. 10 Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/161 Exploring the CIFAR Data.mp4151.05MB
  139. 10 Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/162 Pre-processing_ Scaling Inputs and Creating a Validation Dataset.mp4103.65MB
  140. 10 Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/163 Compiling a Keras Model and Understanding the Cross Entropy Loss Function.mp4139.62MB
  141. 10 Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/164 Interacting with the Operating System and the Python Try-Catch Block.mp4193.73MB
  142. 10 Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/165 Fit a Keras Model and Use Tensorboard to Visualise Learning and Spot Problems.mp4145.59MB
  143. 10 Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/166 Use Regularisation to Prevent Overfitting_ Early Stopping & Dropout Techniques.mp4287.09MB
  144. 10 Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/167 Use the Model to Make Predictions.mp4300.05MB
  145. 10 Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/168 Model Evaluation and the Confusion Matrix.mp481.83MB
  146. 10 Build an Artificial Neural Network to Recognise Images using Keras & Tensorflow/169 Model Evaluation and the Confusion Matrix.mp4366.42MB
  147. 11 Use Tensorflow to Classify Handwritten Digits/172 What's coming up_.mp47.62MB
  148. 11 Use Tensorflow to Classify Handwritten Digits/173 Getting the Data and Loading it into Numpy Arrays.mp475.07MB
  149. 11 Use Tensorflow to Classify Handwritten Digits/174 Data Exploration and Understanding the Structure of the Input Data.mp435.65MB
  150. 11 Use Tensorflow to Classify Handwritten Digits/175 Data Preprocessing_ One-Hot Encoding and Creating the Validation Dataset.mp488.53MB
  151. 11 Use Tensorflow to Classify Handwritten Digits/176 What is a Tensor_.mp469.7MB
  152. 11 Use Tensorflow to Classify Handwritten Digits/177 Creating Tensors and Setting up the Neural Network Architecture.mp4205.12MB
  153. 11 Use Tensorflow to Classify Handwritten Digits/178 Defining the Cross Entropy Loss Function, the Optimizer and the Metrics.mp484.85MB
  154. 11 Use Tensorflow to Classify Handwritten Digits/179 TensorFlow Sessions and Batching Data.mp4128.85MB
  155. 11 Use Tensorflow to Classify Handwritten Digits/180 Tensorboard Summaries and the Filewriter.mp4186.5MB
  156. 11 Use Tensorflow to Classify Handwritten Digits/181 Understanding the Tensorflow Graph_ Nodes and Edges.mp4167.36MB
  157. 11 Use Tensorflow to Classify Handwritten Digits/182 Name Scoping and Image Visualisation in Tensorboard.mp488.68MB
  158. 11 Use Tensorflow to Classify Handwritten Digits/183 Different Model Architectures_ Experimenting with Dropout.mp4335.83MB
  159. 11 Use Tensorflow to Classify Handwritten Digits/184 Prediction and Model Evaluation.mp4162.28MB
  160. 12 Serving a Tensorflow Model through a Website/187 What you'll make.mp464.36MB
  161. 12 Serving a Tensorflow Model through a Website/188 Saving Tensorflow Models.mp4191.76MB
  162. 12 Serving a Tensorflow Model through a Website/189 Loading a SavedModel.mp4144.86MB
  163. 12 Serving a Tensorflow Model through a Website/190 Converting a Model to Tensorflow.js.mp4171.61MB
  164. 12 Serving a Tensorflow Model through a Website/191 Introducing the Website Project and Tooling.mp4125.38MB
  165. 12 Serving a Tensorflow Model through a Website/192 HTML and CSS Styling.mp4244.58MB
  166. 12 Serving a Tensorflow Model through a Website/193 Loading a Tensorflow.js Model and Starting your own Server.mp4321.34MB
  167. 12 Serving a Tensorflow Model through a Website/194 Adding a Favicon.mp442.12MB
  168. 12 Serving a Tensorflow Model through a Website/195 Styling an HTML Canvas.mp4312.41MB
  169. 12 Serving a Tensorflow Model through a Website/196 Drawing on an HTML Canvas.mp4290.97MB
  170. 12 Serving a Tensorflow Model through a Website/197 Data Pre-Processing for Tensorflow.js.mp442.36MB
  171. 12 Serving a Tensorflow Model through a Website/198 Introduction to OpenCV.mp4432.34MB
  172. 12 Serving a Tensorflow Model through a Website/199 Resizing and Adding Padding to Images.mp4286.25MB
  173. 12 Serving a Tensorflow Model through a Website/200 Calculating the Centre of Mass and Shifting the Image.mp4405.91MB
  174. 12 Serving a Tensorflow Model through a Website/201 Making a Prediction from a Digit drawn on the HTML Canvas.mp4181.8MB
  175. 12 Serving a Tensorflow Model through a Website/202 Adding the Game Logic.mp4286MB
  176. 12 Serving a Tensorflow Model through a Website/203 Publish and Share your Website!.mp459.48MB
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