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036 [Python] - Loops and the Gradient Descent Algorithm.mp4
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031 Course-Resources.txt
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031 What's Coming Up_.en_US.srt
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031 What's Coming Up_.mp4
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032 How a Machine Learns.en_US.srt
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032 How a Machine Learns.mp4
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033 Introduction to Cost Functions.en_US.srt
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033 Introduction to Cost Functions.mp4
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80.6 MB
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034 LaTeX Markdown and Generating Data with Numpy.en_US.srt
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034 LaTeX Markdown and Generating Data with Numpy.mp4
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035 Understanding the Power Rule & Creating Charts with Subplots.en_US.srt
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035 Understanding the Power Rule & Creating Charts with Subplots.mp4
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109.5 MB
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036 [Python] - Loops and the Gradient Descent Algorithm.en_US.srt
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037 [exercise] Python Loops Coding Exercise.zip
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037 [exercise_info] Python Loops Coding Exercise.html
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037 [exercise_solution] Python Loops Coding Exercise.zip
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037 [Python] - Advanced Functions and the Pitfalls of Optimisation (Part 1).en_US.srt
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037 [Python] - Advanced Functions and the Pitfalls of Optimisation (Part 1).mp4
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038 [Python] - Tuples and the Pitfalls of Optimisation (Part 2).en_US.srt
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038 [Python] - Tuples and the Pitfalls of Optimisation (Part 2).mp4
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039 Understanding the Learning Rate.en_US.srt
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039 Understanding the Learning Rate.mp4
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040 How to Create 3-Dimensional Charts.en_US.srt
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040 How to Create 3-Dimensional Charts.mp4
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308.6 MB
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041 Understanding Partial Derivatives and How to use SymPy.en_US.srt
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041 Understanding Partial Derivatives and How to use SymPy.mp4
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202.7 MB
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042 Implementing Batch Gradient Descent with SymPy.en_US.srt
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042 Implementing Batch Gradient Descent with SymPy.mp4
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043 [Python] - Loops and Performance Considerations.en_US.srt
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043 [Python] - Loops and Performance Considerations.mp4
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215.6 MB
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044 Reshaping and Slicing N-Dimensional Arrays.en_US.srt
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044 Reshaping and Slicing N-Dimensional Arrays.mp4
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176.5 MB
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045 Concatenating Numpy Arrays.en_US.srt
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045 Concatenating Numpy Arrays.mp4
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89.3 MB
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046 Introduction to the Mean Squared Error (MSE).en_US.srt
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046 Introduction to the Mean Squared Error (MSE).mp4
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78.8 MB
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047 Transposing and Reshaping Arrays.en_US.srt
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047 Transposing and Reshaping Arrays.mp4
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048 Implementing a MSE Cost Function.en_US.srt
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048 Implementing a MSE Cost Function.mp4
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102.2 MB
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049 Understanding Nested Loops and Plotting the MSE Function (Part 1).en_US.srt
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049 Understanding Nested Loops and Plotting the MSE Function (Part 1).mp4
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050 Plotting the Mean Squared Error (MSE) on a Surface (Part 2).en_US.srt
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050 Plotting the Mean Squared Error (MSE) on a Surface (Part 2).mp4
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051 Running Gradient Descent with a MSE Cost Function.en_US.srt
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051 Running Gradient Descent with a MSE Cost Function.mp4
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052 Visualising the Optimisation on a 3D Surface.en_US.srt
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052 Visualising the Optimisation on a 3D Surface.mp4
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92.5 MB
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053 03-Gradient-Descent.ipynb.zip
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053 Download the Complete Notebook Here.html
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054 Any Feedback on this Section_.html
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/01 Introduction to the Course/
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001 What is Machine Learning_.mp4
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33.9 MB
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002 What is Data Science_.en_US.srt
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002 What is Data Science_.mp4
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75.1 MB
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003 Download the Syllabus.html
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003 ML-Data-Science-Syllabus.pdf
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106.5 KB
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004 App-Brewery-Cornell-Notes-Template.txt
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004 Top Tips for Succeeding on this Course.html
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005 Course Resources List.html
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001 What is Machine Learning_.en_US.srt
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/02 Predict Movie Box Office Revenue with Linear Regression/
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006 Course-Resources.txt
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006 Introduction to Linear Regression & Specifying the Problem.en_US.srt
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006 Introduction to Linear Regression & Specifying the Problem.mp4
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007 cost-revenue-dirty.csv
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007 Gather & Clean the Data.en_US.srt
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007 Gather & Clean the Data.mp4
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007 The-Numbers-Movie-Budgets.txt
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008 cost-revenue-clean.csv
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008 Explore & Visualise the Data with Python.en_US.srt
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008 Explore & Visualise the Data with Python.mp4
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008 Try-Jupyter-in-your-Browser.txt
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009 01-Linear-Regression-checkpoint.ipynb.zip
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009 The Intuition behind the Linear Regression Model.en_US.srt
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009 The Intuition behind the Linear Regression Model.mp4
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010 Analyse and Evaluate the Results.en_US.srt
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010 Analyse and Evaluate the Results.mp4
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011 01-Linear-Regression-complete.ipynb.zip
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011 Download the Complete Notebook Here.html
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012 Join the Student Community.html
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013 Any Feedback on this Section_.html
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/03 Python Programming for Data Science and Machine Learning/
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014 Course-Resources.txt
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014 Windows Users - Install Anaconda.en_US.srt
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014 Windows Users - Install Anaconda.mp4
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015 Course-Resources.txt
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015 Mac Users - Install Anaconda.en_US.srt
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015 Mac Users - Install Anaconda.mp4
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016 Does LSD Make You Better at Maths_.en_US.srt
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016 Does LSD Make You Better at Maths_.mp4
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017 12-Rules-to-Learn-to-Code.pdf
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017 Download the 12 Rules to Learn to Code.html
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018 [Python] - Variables and Types.en_US.srt
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018 [Python] - Variables and Types.mp4
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019 [exercise] Python Variable Coding Exercise.zip
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019 [exercise_info] Python Variable Coding Exercise.html
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019 [exercise_solution] Python Variable Coding Exercise.zip
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019 [Python] - Lists and Arrays.en_US.srt
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019 [Python] - Lists and Arrays.mp4
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020 [exercise] Python Lists Coding Exercise.zip
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020 [exercise_info] Python Lists Coding Exercise.html
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020 [exercise_solution] Python Lists Coding Exercise.zip
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020 [Python & Pandas] - Dataframes and Series.en_US.srt
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020 [Python & Pandas] - Dataframes and Series.mp4
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020 lsd-math-score-data.csv
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021 [Python] - Module Imports.en_US.srt
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021 [Python] - Module Imports.mp4
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022 [Python] - Functions - Part 1_ Defining and Calling Functions.en_US.srt
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022 [Python] - Functions - Part 1_ Defining and Calling Functions.mp4
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023 [exercise] Python Functions Coding Exercise - Part 1.zip
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023 [exercise_info] Python Functions Coding Exercise - Part 1.html
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023 [exercise_solution] Python Functions Coding Exercise - Part 1.zip
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023 [Python] - Functions - Part 2_ Arguments & Parameters.en_US.srt
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023 [Python] - Functions - Part 2_ Arguments & Parameters.mp4
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024 [exercise] Python Functions Coding Exercise - Part 2.zip
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024 [exercise_info] Python Functions Coding Exercise - Part 2.html
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024 [exercise_solution] Python Functions Coding Exercise - Part 2.zip
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024 [Python] - Functions - Part 3_ Results & Return Values.en_US.srt
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024 [Python] - Functions - Part 3_ Results & Return Values.mp4
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025 [exercise] Python Functions Coding Exercise - Part 3.zip
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025 [exercise_info] Python Functions Coding Exercise - Part 3.html
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025 [exercise_solution] Python Functions Coding Exercise - Part 3.zip
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025 [Python] - Objects - Understanding Attributes and Methods.en_US.srt
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025 [Python] - Objects - Understanding Attributes and Methods.mp4
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245.9 MB
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026 How to Make Sense of Python Documentation for Data Visualisation.en_US.srt
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026 How to Make Sense of Python Documentation for Data Visualisation.mp4
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027 Working with Python Objects to Analyse Data.en_US.srt
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027 Working with Python Objects to Analyse Data.mp4
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271.4 MB
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028 [Python] - Tips, Code Style and Naming Conventions.en_US.srt
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028 [Python] - Tips, Code Style and Naming Conventions.mp4
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029 02-Python-Intro.ipynb.zip
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37.3 KB
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029 Download the Complete Notebook Here.html
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030 Any Feedback on this Section_.html
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/05 Predict House Prices with Multivariable Linear Regression/
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055 Course-Resources.txt
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055 Defining the Problem.en_US.srt
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055 Defining the Problem.mp4
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60.6 MB
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056 Gathering the Boston House Price Data.en_US.srt
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056 Gathering the Boston House Price Data.mp4
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95.5 MB
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057 Clean and Explore the Data (Part 1)_ Understand the Nature of the Dataset.en_US.srt
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057 Clean and Explore the Data (Part 1)_ Understand the Nature of the Dataset.mp4
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104.2 MB
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058 Clean and Explore the Data (Part 2)_ Find Missing Values.en_US.srt
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058 Clean and Explore the Data (Part 2)_ Find Missing Values.mp4
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218.0 MB
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059 Visualising Data (Part 1)_ Historams, Distributions & Outliers.en_US.srt
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059 Visualising Data (Part 1)_ Historams, Distributions & Outliers.mp4
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060 Visualising Data (Part 2)_ Seaborn and Probability Density Functions.en_US.srt
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060 Visualising Data (Part 2)_ Seaborn and Probability Density Functions.mp4
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69.4 MB
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061 Working with Index Data, Pandas Series, and Dummy Variables.en_US.srt
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061 Working with Index Data, Pandas Series, and Dummy Variables.mp4
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205.1 MB
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062 Understanding Descriptive Statistics_ the Mean vs the Median.en_US.srt
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062 Understanding Descriptive Statistics_ the Mean vs the Median.mp4
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76.0 MB
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063 Introduction to Correlation_ Understanding Strength & Direction.en_US.srt
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063 Introduction to Correlation_ Understanding Strength & Direction.mp4
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21.4 MB
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064 Calculating Correlations and the Problem posed by Multicollinearity.en_US.srt
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064 Calculating Correlations and the Problem posed by Multicollinearity.mp4
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162.4 MB
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065 Visualising Correlations with a Heatmap.en_US.srt
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065 Visualising Correlations with a Heatmap.mp4
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200.8 MB
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066 Techniques to Style Scatter Plots.en_US.srt
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066 Techniques to Style Scatter Plots.mp4
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157.3 MB
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067 A Note for the Next Lesson.html
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068 Working with Seaborn Pairplots & Jupyter Microbenchmarking Techniques.en_US.srt
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27.2 KB
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068 Working with Seaborn Pairplots & Jupyter Microbenchmarking Techniques.mp4
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350.7 MB
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069 Understanding Multivariable Regression.en_US.srt
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7.1 KB
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069 Understanding Multivariable Regression.mp4
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64.6 MB
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070 How to Shuffle and Split Training & Testing Data.en_US.srt
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10.9 KB
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070 How to Shuffle and Split Training & Testing Data.mp4
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87.4 MB
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071 Running a Multivariable Regression.en_US.srt
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9.3 KB
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071 Running a Multivariable Regression.mp4
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77.6 MB
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072 How to Calculate the Model Fit with R-Squared.en_US.srt
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072 How to Calculate the Model Fit with R-Squared.mp4
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40.4 MB
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073 Introduction to Model Evaluation.en_US.srt
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073 Introduction to Model Evaluation.mp4
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12.6 MB
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074 Improving the Model by Transforming the Data.en_US.srt
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074 Improving the Model by Transforming the Data.mp4
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149.4 MB
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075 How to Interpret Coefficients using p-Values and Statistical Significance.en_US.srt
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075 How to Interpret Coefficients using p-Values and Statistical Significance.mp4
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93.4 MB
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076 Understanding VIF & Testing for Multicollinearity.en_US.srt
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076 Understanding VIF & Testing for Multicollinearity.mp4
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172.6 MB
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077 Model Simplification & Baysian Information Criterion.en_US.srt
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077 Model Simplification & Baysian Information Criterion.mp4
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240.6 MB
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078 How to Analyse and Plot Regression Residuals.en_US.srt
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14.0 KB
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078 How to Analyse and Plot Regression Residuals.mp4
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48.9 MB
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079 Residual Analysis (Part 1)_ Predicted vs Actual Values.en_US.srt
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17.2 KB
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079 Residual Analysis (Part 1)_ Predicted vs Actual Values.mp4
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153.6 MB
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080 Residual Analysis (Part 2)_ Graphing and Comparing Regression Residuals.en_US.srt
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21.5 KB
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080 Residual Analysis (Part 2)_ Graphing and Comparing Regression Residuals.mp4
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186.9 MB
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081 Making Predictions (Part 1)_ MSE & R-Squared.en_US.srt
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22.4 KB
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081 Making Predictions (Part 1)_ MSE & R-Squared.mp4
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228.4 MB
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082 Making Predictions (Part 2)_ Standard Deviation, RMSE, and Prediction Intervals.en_US.srt
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14.0 KB
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082 Making Predictions (Part 2)_ Standard Deviation, RMSE, and Prediction Intervals.mp4
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122.2 MB
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083 Build a Valuation Tool (Part 1)_ Working with Pandas Series & Numpy ndarrays.en_US.srt
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083 Build a Valuation Tool (Part 1)_ Working with Pandas Series & Numpy ndarrays.mp4
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208.1 MB
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084 [Python] - Conditional Statements - Build a Valuation Tool (Part 2).en_US.srt
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084 [Python] - Conditional Statements - Build a Valuation Tool (Part 2).mp4
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167.6 MB
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085 [exercise] Python Conditional Statement Coding Exercise.zip
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0.2 KB
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085 [exercise_info] Python Conditional Statement Coding Exercise.html
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085 [exercise_solution] Python Conditional Statement Coding Exercise.zip
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085 Build a Valuation Tool (Part 3)_ Docstrings & Creating your own Python Module.en_US.srt
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26.7 KB
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085 Build a Valuation Tool (Part 3)_ Docstrings & Creating your own Python Module.mp4
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176.1 MB
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086 04-Multivariable-Regression.ipynb.zip
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3.7 MB
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086 04-Valuation-Tool.ipynb.zip
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3.0 KB
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086 boston-valuation.py
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3.1 KB
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086 Download the Complete Notebook Here.html
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087 Any Feedback on this Section_.html
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/06 Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails_ Part 1/
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088 Course-Resources.txt
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088 How to Translate a Business Problem into a Machine Learning Problem.en_US.srt
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9.2 KB
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088 How to Translate a Business Problem into a Machine Learning Problem.mp4
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60.2 MB
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089 Gathering Email Data and Working with Archives & Text Editors.en_US.srt
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089 Gathering Email Data and Working with Archives & Text Editors.mp4
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195.9 MB
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089 SpamData.zip
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22.3 MB
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090 How to Add the Lesson Resources to the Project.en_US.srt
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4.7 KB
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090 How to Add the Lesson Resources to the Project.mp4
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36.8 MB
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091 The Naive Bayes Algorithm and the Decision Boundary for a Classifier.en_US.srt
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091 The Naive Bayes Algorithm and the Decision Boundary for a Classifier.mp4
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59.9 MB
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092 Basic Probability.en_US.srt
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092 Basic Probability.mp4
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16.3 MB
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093 Joint & Conditional Probability.en_US.srt
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093 Joint & Conditional Probability.mp4
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177.5 MB
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094 Bayes Theorem.en_US.srt
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094 Bayes Theorem.mp4
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99.1 MB
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095 Reading Files (Part 1)_ Absolute Paths and Relative Paths.en_US.srt
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11.2 KB
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095 Reading Files (Part 1)_ Absolute Paths and Relative Paths.mp4
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74.6 MB
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096 Reading Files (Part 2)_ Stream Objects and Email Structure.en_US.srt
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096 Reading Files (Part 2)_ Stream Objects and Email Structure.mp4
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175.5 MB
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097 Extracting the Text in the Email Body.en_US.srt
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097 Extracting the Text in the Email Body.mp4
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57.9 MB
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098 [Python] - Generator Functions & the yield Keyword.en_US.srt
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098 [Python] - Generator Functions & the yield Keyword.mp4
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207.3 MB
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099 Create a Pandas DataFrame of Email Bodies.en_US.srt
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099 Create a Pandas DataFrame of Email Bodies.mp4
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71.2 MB
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100 Cleaning Data (Part 1)_ Check for Empty Emails & Null Entries.en_US.srt
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16.9 KB
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100 Cleaning Data (Part 1)_ Check for Empty Emails & Null Entries.mp4
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201.2 MB
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101 Cleaning Data (Part 2)_ Working with a DataFrame Index.en_US.srt
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8.7 KB
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101 Cleaning Data (Part 2)_ Working with a DataFrame Index.mp4
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89.0 MB
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102 Saving a JSON File with Pandas.en_US.srt
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102 Saving a JSON File with Pandas.mp4
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85.2 MB
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103 Data Visualisation (Part 1)_ Pie Charts.en_US.srt
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103 Data Visualisation (Part 1)_ Pie Charts.mp4
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137.4 MB
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104 Data Visualisation (Part 2)_ Donut Charts.en_US.srt
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104 Data Visualisation (Part 2)_ Donut Charts.mp4
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77.0 MB
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105 Introduction to Natural Language Processing (NLP).en_US.srt
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105 Introduction to Natural Language Processing (NLP).mp4
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61.0 MB
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106 Tokenizing, Removing Stop Words and the Python Set Data Structure.en_US.srt
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106 Tokenizing, Removing Stop Words and the Python Set Data Structure.mp4
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144.8 MB
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107 Word Stemming & Removing Punctuation.en_US.srt
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107 Word Stemming & Removing Punctuation.mp4
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87.7 MB
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108 Removing HTML tags with BeautifulSoup.en_US.srt
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108 Removing HTML tags with BeautifulSoup.mp4
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175.6 MB
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109 Creating a Function for Text Processing.en_US.srt
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109 Creating a Function for Text Processing.mp4
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46.9 MB
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110 A Note for the Next Lesson.html
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111 Advanced Subsetting on DataFrames_ the apply() Function.en_US.srt
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12.8 KB
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111 Advanced Subsetting on DataFrames_ the apply() Function.mp4
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112 [Python] - Logical Operators to Create Subsets and Indices.en_US.srt
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112 [Python] - Logical Operators to Create Subsets and Indices.mp4
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113 Word Clouds & How to install Additional Python Packages.en_US.srt
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113 Word Clouds & How to install Additional Python Packages.mp4
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114 Creating your First Word Cloud.en_US.srt
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114 Creating your First Word Cloud.mp4
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115 Styling the Word Cloud with a Mask.en_US.srt
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115 Styling the Word Cloud with a Mask.mp4
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116 Solving the Hamlet Challenge.en_US.srt
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116 Solving the Hamlet Challenge.mp4
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117 Styling Word Clouds with Custom Fonts.en_US.srt
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117 Styling Word Clouds with Custom Fonts.mp4
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118 Create the Vocabulary for the Spam Classifier.en_US.srt
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118 Create the Vocabulary for the Spam Classifier.mp4
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119 Coding Challenge_ Check for Membership in a Collection.en_US.srt
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119 Coding Challenge_ Check for Membership in a Collection.mp4
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120 Coding Challenge_ Find the Longest Email.en_US.srt
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120 Coding Challenge_ Find the Longest Email.mp4
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121 Sparse Matrix (Part 1)_ Split the Training and Testing Data.en_US.srt
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121 Sparse Matrix (Part 1)_ Split the Training and Testing Data.mp4
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122 Sparse Matrix (Part 2)_ Data Munging with Nested Loops.en_US.srt
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122 Sparse Matrix (Part 2)_ Data Munging with Nested Loops.mp4
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123 Sparse Matrix (Part 3)_ Using groupby() and Saving .txt Files.en_US.srt
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123 Sparse Matrix (Part 3)_ Using groupby() and Saving .txt Files.mp4
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124 Coding Challenge Solution_ Preparing the Test Data.en_US.srt
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124 Coding Challenge Solution_ Preparing the Test Data.mp4
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125 Checkpoint_ Understanding the Data.en_US.srt
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125 Checkpoint_ Understanding the Data.mp4
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126 06-Bayes-Classifier-Pre-Processing.ipynb.zip
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126 Download the Complete Notebook Here.html
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127 Any Feedback on this Section_.html
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128 Course-Resources.txt
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128 Setting up the Notebook and Understanding Delimiters in a Dataset.en_US.srt
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128 Setting up the Notebook and Understanding Delimiters in a Dataset.mp4
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128 SpamData.zip
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129 Create a Full Matrix.en_US.srt
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129 Create a Full Matrix.mp4
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130 Count the Tokens to Train the Naive Bayes Model.en_US.srt
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130 Count the Tokens to Train the Naive Bayes Model.mp4
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131 Sum the Tokens across the Spam and Ham Subsets.en_US.srt
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131 Sum the Tokens across the Spam and Ham Subsets.mp4
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132 Calculate the Token Probabilities and Save the Trained Model.en_US.srt
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132 Calculate the Token Probabilities and Save the Trained Model.mp4
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133 Coding Challenge_ Prepare the Test Data.en_US.srt
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133 Coding Challenge_ Prepare the Test Data.mp4
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134 07-Bayes-Classifier-Training.ipynb.zip
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134 Download the Complete Notebook Here.html
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135 Any Feedback on this Section_.html
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136 Course-Resources.txt
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136 Set up the Testing Notebook.en_US.srt
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136 Set up the Testing Notebook.mp4
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136 SpamData.zip
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23.9 MB
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137 Joint Conditional Probability (Part 1)_ Dot Product.en_US.srt
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137 Joint Conditional Probability (Part 1)_ Dot Product.mp4
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138 Joint Conditional Probablity (Part 2)_ Priors.en_US.srt
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138 Joint Conditional Probablity (Part 2)_ Priors.mp4
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139 Making Predictions_ Comparing Joint Probabilities.en_US.srt
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139 Making Predictions_ Comparing Joint Probabilities.mp4
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71.4 MB
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140 The Accuracy Metric.en_US.srt
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140 The Accuracy Metric.mp4
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48.7 MB
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141 Visualising the Decision Boundary.en_US.srt
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141 Visualising the Decision Boundary.mp4
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290.8 MB
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142 False Positive vs False Negatives.en_US.srt
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142 False Positive vs False Negatives.mp4
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75.1 MB
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143 The Recall Metric.en_US.srt
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143 The Recall Metric.mp4
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144 The Precision Metric.en_US.srt
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144 The Precision Metric.mp4
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64.5 MB
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145 The F-score or F1 Metric.en_US.srt
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145 The F-score or F1 Metric.mp4
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30.1 MB
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146 A Naive Bayes Implementation using SciKit Learn.en_US.srt
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146 A Naive Bayes Implementation using SciKit Learn.mp4
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283.7 MB
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147 07-Bayes-Classifier-Testing-Inference-Evaluation.ipynb.zip
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248.9 KB
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147 08-Naive-Bayes-with-scikit-learn.ipynb.zip
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13.6 KB
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147 Download the Complete Notebook Here.html
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0.7 KB
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148 Any Feedback on this Section_.html
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/09 Introduction to Neural Networks and How to Use Pre-Trained Models/
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149 Course-Resources.txt
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0.1 KB
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149 The Human Brain and the Inspiration for Artificial Neural Networks.en_US.srt
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149 The Human Brain and the Inspiration for Artificial Neural Networks.mp4
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63.5 MB
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150 Layers, Feature Generation and Learning.en_US.srt
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150 Layers, Feature Generation and Learning.mp4
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248.7 MB
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151 Costs and Disadvantages of Neural Networks.en_US.srt
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151 Costs and Disadvantages of Neural Networks.mp4
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153.0 MB
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152 Preprocessing Image Data and How RGB Works.en_US.srt
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152 Preprocessing Image Data and How RGB Works.mp4
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135.9 MB
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152 TF-Keras-Classification-Images.zip
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513.1 KB
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153 Importing Keras Models and the Tensorflow Graph.en_US.srt
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153 Importing Keras Models and the Tensorflow Graph.mp4
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76.9 MB
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154 Making Predictions using InceptionResNet.en_US.srt
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154 Making Predictions using InceptionResNet.mp4
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190.0 MB
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155 Coding Challenge Solution_ Using other Keras Models.en_US.srt
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155 Coding Challenge Solution_ Using other Keras Models.mp4
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162.5 MB
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156 09-Neural-Nets-Pretrained-Image-Classification.ipynb.zip
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585.6 KB
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156 Download the Complete Notebook Here.html
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0.7 KB
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157 Any Feedback on this Section_.html
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158 Course-Resources.txt
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0.1 KB
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158 Solving a Business Problem with Image Classification.en_US.srt
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4.9 KB
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158 Solving a Business Problem with Image Classification.mp4
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39.4 MB
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159 Installing Tensorflow and Keras for Jupyter.en_US.srt
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6.4 KB
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159 Installing Tensorflow and Keras for Jupyter.mp4
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61.3 MB
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160 Gathering the CIFAR 10 Dataset.en_US.srt
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160 Gathering the CIFAR 10 Dataset.mp4
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37.1 MB
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161 Exploring the CIFAR Data.en_US.srt
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17.9 KB
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161 Exploring the CIFAR Data.mp4
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158.4 MB
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162 Pre-processing_ Scaling Inputs and Creating a Validation Dataset.en_US.srt
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19.6 KB
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162 Pre-processing_ Scaling Inputs and Creating a Validation Dataset.mp4
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108.7 MB
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163 Compiling a Keras Model and Understanding the Cross Entropy Loss Function.en_US.srt
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18.4 KB
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163 Compiling a Keras Model and Understanding the Cross Entropy Loss Function.mp4
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146.4 MB
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164 Interacting with the Operating System and the Python Try-Catch Block.en_US.srt
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23.4 KB
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164 Interacting with the Operating System and the Python Try-Catch Block.mp4
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203.1 MB
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165 Fit a Keras Model and Use Tensorboard to Visualise Learning and Spot Problems.en_US.srt
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13.9 KB
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165 Fit a Keras Model and Use Tensorboard to Visualise Learning and Spot Problems.mp4
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152.7 MB
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166 Use Regularisation to Prevent Overfitting_ Early Stopping & Dropout Techniques.en_US.srt
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27.9 KB
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166 Use Regularisation to Prevent Overfitting_ Early Stopping & Dropout Techniques.mp4
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301.0 MB
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167 Use the Model to Make Predictions.en_US.srt
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167 Use the Model to Make Predictions.mp4
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314.6 MB
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168 Model Evaluation and the Confusion Matrix.en_US.srt
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168 Model Evaluation and the Confusion Matrix.mp4
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85.8 MB
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169 Model Evaluation and the Confusion Matrix.en_US.srt
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39.9 KB
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169 Model Evaluation and the Confusion Matrix.mp4
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384.2 MB
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170 10-Neural-Nets-Keras-CIFAR10-Classification.ipynb.zip
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123.0 KB
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170 Download the Complete Notebook Here.html
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0.7 KB
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171 Any Feedback on this Section_.html
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1.0 KB
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/11 Use Tensorflow to Classify Handwritten Digits/
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172 Course-Resources.txt
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0.1 KB
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172 What's coming up_.en_US.srt
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2.5 KB
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172 What's coming up_.mp4
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8.0 MB
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173 Getting the Data and Loading it into Numpy Arrays.en_US.srt
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8.9 KB
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173 Getting the Data and Loading it into Numpy Arrays.mp4
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78.7 MB
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173 MNIST.zip
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15.5 MB
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174 Data Exploration and Understanding the Structure of the Input Data.en_US.srt
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6.4 KB
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174 Data Exploration and Understanding the Structure of the Input Data.mp4
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37.4 MB
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175 Data Preprocessing_ One-Hot Encoding and Creating the Validation Dataset.en_US.srt
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12.5 KB
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175 Data Preprocessing_ One-Hot Encoding and Creating the Validation Dataset.mp4
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92.8 MB
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176 What is a Tensor_.en_US.srt
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176 What is a Tensor_.mp4
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73.1 MB
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177 Creating Tensors and Setting up the Neural Network Architecture.en_US.srt
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177 Creating Tensors and Setting up the Neural Network Architecture.mp4
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215.1 MB
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178 Defining the Cross Entropy Loss Function, the Optimizer and the Metrics.en_US.srt
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13.9 KB
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178 Defining the Cross Entropy Loss Function, the Optimizer and the Metrics.mp4
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89.0 MB
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179 TensorFlow Sessions and Batching Data.en_US.srt
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20.2 KB
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179 TensorFlow Sessions and Batching Data.mp4
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135.1 MB
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180 Tensorboard Summaries and the Filewriter.en_US.srt
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180 Tensorboard Summaries and the Filewriter.mp4
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195.6 MB
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181 Understanding the Tensorflow Graph_ Nodes and Edges.en_US.srt
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20.9 KB
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181 Understanding the Tensorflow Graph_ Nodes and Edges.mp4
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175.5 MB
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182 Name Scoping and Image Visualisation in Tensorboard.en_US.srt
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182 Name Scoping and Image Visualisation in Tensorboard.mp4
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93.0 MB
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183 Different Model Architectures_ Experimenting with Dropout.en_US.srt
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183 Different Model Architectures_ Experimenting with Dropout.mp4
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352.1 MB
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184 Prediction and Model Evaluation.en_US.srt
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18.6 KB
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184 Prediction and Model Evaluation.mp4
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170.2 MB
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185 11-Neural-Networks-TF-Handwriting-Recognition.ipynb.zip
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6.8 KB
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185 Download the Complete Notebook Here.html
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0.7 KB
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186 Any Feedback on this Section_.html
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/12 Serving a Tensorflow Model through a Website/
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187 What you'll make.en_US.srt
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9.6 KB
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187 What you'll make.mp4
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67.5 MB
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188 11-Neural-Networks-TF-Handwriting-Recognition.ipynb.zip
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6.5 KB
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188 Saving Tensorflow Models.en_US.srt
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20.9 KB
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188 Saving Tensorflow Models.mp4
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201.1 MB
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189 12-TF-SavedModel-Export-Completed.ipynb.zip
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6.3 KB
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189 Loading a SavedModel.en_US.srt
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25.8 KB
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189 Loading a SavedModel.mp4
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151.9 MB
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189 MNIST-Model-Load-Files.zip
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3.0 MB
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190 Converting a Model to Tensorflow.js.en_US.srt
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20.8 KB
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190 Converting a Model to Tensorflow.js.mp4
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179.9 MB
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190 TFJS.zip
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1.6 MB
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191 Introducing the Website Project and Tooling.en_US.srt
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17.0 KB
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191 Introducing the Website Project and Tooling.mp4
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131.5 MB
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191 math-garden-stub.zip
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45.1 KB
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192 HTML and CSS Styling.en_US.srt
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37.4 KB
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192 HTML and CSS Styling.mp4
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256.5 MB
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193 Loading a Tensorflow.js Model and Starting your own Server.en_US.srt
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36.7 KB
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193 Loading a Tensorflow.js Model and Starting your own Server.mp4
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336.9 MB
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193 x-test0-ylabel7.txt
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4.7 KB
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193 x-test1-ylabel2.txt
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193 x-test2-ylabel1.txt
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194 Adding a Favicon.en_US.srt
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194 Adding a Favicon.mp4
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44.2 MB
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195 Styling an HTML Canvas.en_US.srt
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38.9 KB
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195 Styling an HTML Canvas.mp4
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327.6 MB
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196 Drawing on an HTML Canvas.en_US.srt
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196 Drawing on an HTML Canvas.mp4
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305.1 MB
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197 Data Pre-Processing for Tensorflow.js.en_US.srt
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197 Data Pre-Processing for Tensorflow.js.mp4
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44.4 MB
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198 Introduction to OpenCV.en_US.srt
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37.8 KB
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198 Introduction to OpenCV.mp4
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453.3 MB
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198 math-garden-stub-12.12-checkpoint.zip
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4.3 MB
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199 Resizing and Adding Padding to Images.en_US.srt
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26.4 KB
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199 Resizing and Adding Padding to Images.mp4
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300.2 MB
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200 Calculating the Centre of Mass and Shifting the Image.en_US.srt
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34.9 KB
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200 Calculating the Centre of Mass and Shifting the Image.mp4
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425.6 MB
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201 Making a Prediction from a Digit drawn on the HTML Canvas.en_US.srt
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16.8 KB
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201 Making a Prediction from a Digit drawn on the HTML Canvas.mp4
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190.6 MB
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202 Adding the Game Logic.en_US.srt
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37.4 KB
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202 Adding the Game Logic.mp4
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299.9 MB
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202 math-garden-stub-complete.zip
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4.3 MB
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203 Publish and Share your Website!.en_US.srt
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9.4 KB
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203 Publish and Share your Website!.mp4
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62.4 MB
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204 Any Feedback on this Section_.html
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1.0 KB
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/13 Next Steps/
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205 Where next_.html
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4.7 KB
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206 What Modules Do You Want to See_.html
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0.9 KB
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207 Stay in Touch!.html
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Total files 452
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