Introduction
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Introduction to Deep Learning (2:59)
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Considerations for Deep Learning on Microcontrollers (4:24)
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Download Keil uVision 5 (1:16)
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Installing Keil uVision 5 (6:23)
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Installing Packs (4:19)
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Overview of Keil uVision (10:00)
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Changing the Compiler (1:53)
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Installin ARM Compiler 5
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Coding : Setting Up a UART Driver (32:11)
Building Blocks of Neural Networks
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Source Code Download
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The Single Input Single Output Neural Network (1:05)
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Installing Tera Term (2:56)
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Coding : The Single Input Single Output Neural Network (13:45)
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The Multiple Input Single Output Neural Network (2:39)
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Coding : The Multiple Input Single Output Neural Network (16:58)
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Coding : Single Input Multiple Output Neural Network (14:31)
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The Multiple Input Multiple Output Neural Network (2:49)
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Coding : The Multiple Input - Multiple Output Neural Network (21:47)
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The Hidden Layer Neural Network (2:37)
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Coding : The Hidden Layer Neural Network (18:37)
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Comparing and Finding Error (1:52)
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Coding : Finding Error (7:50)
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Understanding data representation in Machine Learning (1:18)
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Understanding the "Learning" in Machine Learning (4:21)
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Coding : Brute-force Learning (16:34)
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Introduction to Gradient Descent (3:16)
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Functional Description of a Biological Neuron (2:08)
Introduction to Neural Network (Part 2)
Logistic Regression
Deep Neural Networks
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Internals of a 2 layer Neural Network (3:01)
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Activation Functions (3:41)
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Understanding Computational Graphs (8:50)
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Updating Parameters Effectively (3:33)
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Understanding the Importance of Vectorization (9:05)
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Summary of Back-propagation and Forward-propagation (0:39)
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Initializing Parameters Effectively (0:38)
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Understanding Layers and Units (1:11)
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Understanding the Shapes (3:12)
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Understanding Broadcasting in Programming (1:18)
Improving Neural Networks with Regularization Techniques
Building A Logistic Regression Model
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Coding : Installing Python (3:51)
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Coding : Installing Python Packages (5:29)
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Coding : Setting up our project (5:02)
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Coding : Creating a Helper script (8:29)
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Coding : Inspecting our dataset (10:31)
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Coding : Inspecting the dataset Dimensions (9:34)
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Coding : Pre-processing our dataset (9:23)
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Coding : Implementing Forward and Backward Propagation (15:59)
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Coding : Implementing Gradient Descent (5:48)
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Coding : Implementing the Predictor function (5:00)
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Coding : Training our Model (27:23)
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Coding : Testing our Model (16:59)
Building Deep Neural Networks From Scratch
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Coding : Building A Deep Neural Network Library (Version 1) (51:40)
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Coding : Implementing a Two-Layer Neural Network (Inspecting the Dataset) (15:18)
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Coding : Implementing a Two-Layer Neural Network ( Pre-processing the Dataset) (4:52)
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Coding : Implementing a Two-Layer Neural Network ( Building the Model ) (26:00)
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Coding : Implementing a Two-Layer Neural Network ( Testing the Model) (37:26)
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Coding : Building A Deep Neural Network Library (Version 2) (19:38)
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Coding : Implementing a Neural Network with an arbitrary number of Layers (12:28)
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Coding : Testing the Multi-Layer Neural Network (7:56)
Convolutional Neural Networks (CNN)
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Introduction to Convolution (6:03)
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Introduction to 2D Convolution (5:05)
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Describing ConvNet Layers (1:05)
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Understanding Padding (4:26)
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Understanding Striding (1:06)
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Convolution Over Volume (2:35)
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Single Layer of a Convolutional Neural Network (2:35)
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Examining a Complete Convolutional Neural Network (1:48)
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Understanding the Pooling Layer (1:03)
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Examining a Complete Convolutional Neural Network with a Pooling Layer (1:24)
CubeMX 5 & CubeIDE Primer
CubeMX AI
Case Study : Deploying the MNIST Handwriting Recognition Model on ARM MCUs
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Coding : Setting up our project (28:29)
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Coding : Cleaning Up our Project (17:42)
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Coding : Implementing the User Interface (31:13)
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Coding : Implementing the Touch Sensor (16:28)
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Coding : Scaling the Input Image (12:50)
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Coding : Deploying our Neural Network (Part 1) (32:14)
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Coding : Deploying our Neural Network (Part 2) (23:26)
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Coding : Updating our Model (27:41)
Closing
