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TensorFlow Innovation Hub - Build Cutting-Edge Machine Learning Models
Intro to Tensorflow 2
00 Course Intro (6:10)
01 Intro to Tensorflow (5:33)
02 Installing Tensorflow (3:52)
03 Intro to Linear Regression (9:26)
04 Linear Regression Model - Creating Dataset (5:49)
05 Linear Regression Model - Building the Model (7:22)
06 Linear Regression Model - Creating a Loss Function (5:57)
07 Linear Regression Model - Training the Model (12:42)
08 Linear Regression Model - Testing the Model (5:22)
09 Summary and Outro (2:55)
Source Files
Image Recognition with MNIST
00 Course Intro (6:57)
01 Intro To Image Recognition (14:07)
02 Intro To MNIST (4:43)
03 Building A CNN Part 1 - Obtaining Data (15:40)
04 Building A CNN Part 2 - Building The Model (10:14)
05 Building A CNN Part 3 - Adding Loss And Optimizer Functions (4:57)
06 Building A CNN Part 4 - Train And Test Functions (10:58)
07 Building A CNN Part 5 - Train And Test The Mode (9:17)
08 MNIST Image Recognition With Keras Sequential Model (13:24)
09 Summary And Outro (3:40)
Source Files
Machine Learning: Beginners - Build Beginner Models in TensorFlow 2.0
00 Course Overview (3:30)
01 Build Models On The Web (5:06)
Source Files
Machine Learning: Beginners - AI Uninformed Search Algorithms
00 What Are Search Algorithms (7:21)
01 Depth First Search (9:00)
02 Build A Depth First Search Algorithm (8:26)
03 What Is Breadth First Search (BFS) (5:08)
04 Build A Breadth First Search Algorithm (6:56)
05 Depth Limited Search (3:58)
06 Iterative Deepening Depth First Search (5:32)
07 What Is Uniform Cost Search (6:04)
08 Build A Uniform Cost Search Algorithm (8:07)
09 Bidirectional Search (4:44)
Source Files
Machine Learning: Beginners - AI Informed Search Algorithms
00 What Are Informed Search Algorithms (4:07)
01 What Is Greedy Best-first Search (8:16)
02 Build A Greedy Best First Search Algorithm (10:43)
03 What Is A Search (5:10)
Source Files
Machine Learning: Beginners - How Machine Learning Works
00 How Does A Machine Learning Agent Learn (7:37)
01 What Is Inductive Learning (4:10)
02 Make Decisions With Decision Trees (10:50)
03 Performance Of A Machine Learning Algorithm (4:13)
04 Handle Noise In Data (5:20)
05 Statistical Learning (3:56)
Source Files
Machine Learning: Beginners - Logistic Regression
00 What Is Logistic Regression (4:26)
01 How To Prepare Data (8:52)
02 Prepare Data For Logistic Regression (12:19)
03 Build A Logistic Regression Model (5:29)
04 How To Build A Logistic Regression Model (3:28)
05 What Is Optimization (12:10)
06 How To Optimize A Logistic Regression Model (12:45)
07 Optimize The Logistic Regression Model (12:44)
08 Train The Model (10:09)
09 Test The Model (2:33)
10 Visualize Results (5:38)
Source Files
Machine Learning: Beginners - Gradient Boosted Classification
00 What Is Gradient Boosting (1:54)
01 Prepare Data For Gradient Boosted Classification (7:19)
02 Build Binary Classes (6:12)
03 How To Shape Data For Classification (2:58)
04 Shape Data For Classification (7:06)
05 How To Build A Boosted Trees Classifier (4:03)
06 Build A Boosted Trees Classifier (4:37)
Source Files
Machine Learning: Beginners - Gradient Boosted Regression
00 Build Input Functions (3:55)
01 Build A Boosted Trees Regressor (3:02)
02 Train And Evaluate The Model (4:07)
Source Files
Machine Learning Intermediate - Supervised Learning Introduction
00 What You'll Learn (8:47)
01 What Is Supervised Learning (14:41)
02 Build Models On The Web (5:06)
Source Files
00 Course Intro
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