TensorFlow Mastery: Constructing Dynamic Machine Learning Models
Created by Team Mammoth
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71 lectures
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Skill level
All levels
02
Sections
9
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Lectures
71
04
Instructor
Team Mammoth
What's inside
This course includes.
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9
Sections
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Certificate of completion
Included
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Mobile and desktop access
Included
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AI learning assistance
Included
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Course content
Curriculum & lectures.
+ Intro to Tensorflow 11 lectures
00. Course Intro
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01. Intro To Tensorflow
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02. Installing Tensorflow
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03. Intro To Linear Regression
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04. Linear Regression Model - Creating Dataset
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05. Linear Regression Model - Building The Model
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06. Linear Regression Model - Creating A Loss Function
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07. Linear Regression Model - Training The Model
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08. Linear Regression Model - Testing The Model
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09. Summary And Outro
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Intro to Tensorflow - Source Files
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+ Image Recognition with MNIST 11 lectures
02. Intro To Mnist
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00. Course Intro
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01. Intro To Image Recognition-1
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05. Building A Cnn Part 3 - Adding Loss And Optimizer Functions
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04. Building A Cnn Part 2 - Building The Model
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03. Building A Cnn Part 1 - Obtaining Data
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06. Building A Cnn Part 4 - Train And Test Functions
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07. Building A Cnn Part 5 - Train And Test The Model
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08. Mnist Image Recognition With Keras Sequential Model
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09. Summary And Outro
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+ 01 Build Beginner Models in TensorFlow 2.0 3 lectures
01-01 Course Overview
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01-02 Build Models On The Web
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01 Source Files
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+ 02 AI Uninformed Search Algorithms 11 lectures
01 What Are Search Algorithms
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02 Depth First Search
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02B Build A Depth First Search Algorithm
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03 What Is Breadth First Search (BFS)
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03B Build A Breadth First Search Algorithm
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04 Depth Limited Search
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05 Iterative Deepening Depth First Search
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06 What Is Uniform Cost Search
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06B Build A Uniform Cost Search Algorithm
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07 Bidirectional Search
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02 Source Files
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+ 03 AI Informed Search Algorithms 5 lectures
03-01 What Are Informed Search Algorithms
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03-02 What Is Greedy Best-first Search
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03-02b Build A Greedy Best First Search Algorithm
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03-03 What Is A Search
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03 Source Files
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+ 04 How Machine Learning Works 7 lectures
04-01 How Does A Machine Learning Agent Learn
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04-02 What Is Inductive Learning
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04-03 Make Decisions With Decision Trees
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04-04 Performance Of A Machine Learning Algorithm
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04-05 Handle Noise In Data
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04-06 Statistical Learning
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04 Source Files
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+ 05 Logistic Regression 12 lectures
01 What Is Logistic Regression
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03 Prepare Data For Logistic Regression
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03a How To Prepare Data
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-04 Build A Logistic Regression Model
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04a How To Build A Logistic Regression Model
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04b What Is Optimization
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05a How To Optimize A Logistic Regression Model
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05 Optimize The Logistic Regression Model
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06 Train The Model
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07 Test The Model
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08 Visualize Results
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06 Source Files
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+ 06 Gradient Boosted Classification 7 lectures
01 What Is Gradient Boosting
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02 Prepare Data For Gradient Boosted Classification
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03 Build Binary Classes
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04 How To Shape Data For Classification
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04b Shape Data For Classification
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05 How To Build A Boosted Trees Classifier
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05b Build A Boosted Trees Classifier
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+ 07 Gradient Boosted Regression 4 lectures
07.01 Build Input Functions
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07.02 Build A Boosted Trees Regressor
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07.03 Train And Evaluate The Model
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07 Source Files
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Instructors
Taught by people who ship.
Team Mammoth
Instructor
Produced by a team of Mammoth Club industry experts. Over 14 years, Mammoth Club has built a global student community in 190+ countries with 9+ million courses sold, releasing over 1,000+ courses and 5,000+ hours of video content.
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