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Repository for EEC2003, a graduate course on PPgEEC about Fundamentals of Deep Learning

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## Universidade Federal do Rio Grande do Norte
### Programa de Pós-Graduação em Engenharia Elétrica e de Computação PPgEEC

#### EEC2003 - Special Topics in AI

- Lesson #01 Introduction
	- Course Outline & Presentation
	- [Google Colab Introduction](https://www.loom.com/share/8a4f0d34b3cb4d9ea04b6dcf0b3d1aca)
	- [Google Colab Cont.](https://www.loom.com/share/d96cb0af7d9c4416bfe8145c93248a11)
- Lesson #02 Fundamentals
	- [Introduction to Deep Learning and TensorFlow - Outline](https://www.loom.com/share/caeb19f6f7694bfdba3687a46b37298d)
	- [The perceptron](https://www.loom.com/share/bccf2bc2c7f24652b7b3b73825e0100f)
	- [Building Neural Networks](https://www.loom.com/share/f0ca49107b52458699210cbda8d3cb76)
	- [Matrix Dimension](https://www.loom.com/share/31862a3448f6427097e16adc773592a1)
	- [Applying Neural Networks](https://www.loom.com/share/f5ef63a357604bcebb577458cbfe85f6)
	- [Training a Neural Network](https://www.loom.com/share/38f251f7949d4d3c99097395ab9e3b74)
	- [Backpropagation with Pencil & Paper](https://www.loom.com/share/7093fed68d7342b189ef2f9b85e93b2d)
	- [Learning rate & Batch Size](https://www.loom.com/share/183248cfec9f46a5bc0ae7ec410aa291)
	- [Exponentially Weighted Average](https://www.loom.com/share/b84b1452ab5d4193b63481910d9323b1)
	- [Adam, Momentum, RMSProp, Learning Rate Decay](https://www.loom.com/share/101a5956c6f04d31843f37c4be089978)
- Lesson #03 Better Generalization vs Better Learning
	- Better Generalization
		- [Better Deep Learning - Outline](https://www.loom.com/share/33ceae6510ca4321b95425efc7c7828e)
		- [Spliting Data](https://www.loom.com/share/436be4492b0549baba57c52d40941cc3)
		- [Bias vs Variance](https://www.loom.com/share/9cc90385906d458b9baafc19c686cc8e)
		- [Weight Regularization](https://www.loom.com/share/6f8e8101bee243318302cb3742fbdb8c)
		- [Weight Constraint](https://www.loom.com/share/b65c8294dcda4746a0a9a9c9ea3b5cb4)
		- [Dropout](https://www.loom.com/share/c32f0a35d56b426ca988e05926787936)
		- [Promote Robustness with Noise](https://www.loom.com/share/ed48470b14a3460eac572dcf9d8838c9)
		- [Early Stopping](https://www.loom.com/share/c738eed439a34794a6b99c555b99afad)
	- Better Learning
		- [Data scaling](https://www.loom.com/share/7008b640440d412498578e27b8557471)
		- [Vanishing/Exploding Gradient](https://www.loom.com/share/fb427d71b7a74e2dab226445941d2d41)
		- [Fix Vanishing Gradient with Relu](https://www.loom.com/share/0cdfb9ba531540fca075444f7d732fc6)
		- [Fix Exploding Gradient with Gradient Clipping](https://www.loom.com/share/2b560922401442b7b078faf06801a3ad)
- Lesson #04 Hyperparameter Tuning & Batch Normalization
	- [Outline](https://www.loom.com/share/0c5ccb3514ae4dc8b674ea5789f26645)
	- [Hyperparameter Tuning Fundamentals](https://www.loom.com/share/19920a5abadd4ce0bea8eadc26c778ee)
	- [Keras Tuner and Weights and Biases](https://www.loom.com/share/f27dcb1d5779432a906e19db7a834c65)
	- [Wandb - Part #01](https://www.loom.com/share/fed7cfc2a5414ad58637244f84add9b8)
	- [Wandb - Part #02](https://www.loom.com/share/16255ea534b34691a90801fe1d34ce6d)
	- [Batch Normalization Fundamentals](https://www.loom.com/share/adf8e445186d44caa79a83d0f3af97d8)
	- [Batch Normalization Math Details](https://www.loom.com/share/b2dad925916e4ae58d6a3cf3223be945)
	- [Batch Normalization Case Study](https://www.loom.com/share/d8113419cd56463eab0094df1a687cf0)
- Lesson #05 Fundamentals of Convolutional Neural Networks (CNN)
	- [Outline](https://www.loom.com/share/3b88a9f39080434e8ee9ec87135ea9b8)
	- [CNN Introduction & Motivation](https://www.loom.com/share/bb17b639264c4b079b375b63c4c16085)
	- [Convolutional Layer](https://www.loom.com/share/dd6b3d3ae5b3430ead7ed09174dd1b4c)
	- [Case Study of Convolutional Layer](https://www.loom.com/share/48b2566fae7b4c9794526e41ffbd624c)
	- [Pooling Layer](https://www.loom.com/share/055e32dcd2564b13890ce19d72daba32)
	- [Fully Connected Layer](https://www.loom.com/share/7912c7d6e1a54a97b39c15efb5a9fc88)
	- [Case Study - Signs Dataset](https://www.loom.com/share/2ea8f406635b489faa9f7f5ec675de73) 
- Lesson #06 Convolutional Neural Networks (CNN) Architecture I
	- [Outline](https://www.loom.com/share/c218c4ac06684b13aa07049b39f4b4df)
	- [Typical CNN Architecture](https://www.loom.com/share/9aaf99dc6e75403b96e17b08da935313)
	- [Best practices when building your own CNN](https://www.loom.com/share/f1016707d00f4fd99c9dfb6295072554)
	- [LeNet-5](https://www.loom.com/share/ddaf75c76e6b4ab39b3741f43a655764)
	- [Training LeNet-5 using MNIST dataset](https://www.loom.com/share/59ca5879a00242b4814f822fa3c6a430)
	- [ImageNet & ILSVRC](https://www.loom.com/share/c4f94499da3a4769bd751c2970c3d6ab)
	- [AlexNet](https://www.loom.com/share/bc8475491fcf49d1b17305a0441843f7)
	- [Dogs vs Cats Challenge + HDF5](https://www.loom.com/share/ba0bfb5f5f6044fead11ebebee3eedd3)
	- [CNN Architectures - Hands on Part #01](https://www.loom.com/share/068d3145cd9c4b0291e5d2a83a2c494b)
	- [CNN Architectures - Hands on Part #02](https://www.loom.com/share/b847f834f62c47faaaa936137d163e08)
- Lesson #07 Convolutional Neural Networks (CNN) Architecture II
	- [Outline](https://www.loom.com/share/12dabb12f4df48f29802976dedf28a93)
	- [VGG](https://www.loom.com/share/54f9a9a0c7164752a0daa2e11ab94304)
	- [VGG case study using CIFAR-10](https://www.loom.com/share/f8543faa8a9544f690e8d83493d053ff)
	- [How to use 1x1 convolutions](https://www.loom.com/share/4fa05c8a49814494a658e9ad337a8ef6)
	- [GoogLeNet](https://www.loom.com/share/3f6e276e64b845f390174e86b45a9e5f)
	- [MiniGoogleLeNet using CIFAR-10](https://www.loom.com/share/62f243ae46ec4796bb767a7e6cbd78c9)
	- [DeeperGoogLeNet and Tiny ImageNet Challenge](https://www.loom.com/share/af4eee545ac24cc59dae0482443efa67)
- Lesson #08 ResNet
- Lesson #09 Transfer Learning
	- [Outline](https://www.loom.com/share/a748317324724d029678eb33c877b82f)
	- [Feature Extractors](https://www.loom.com/share/e19e7431012c42aaa3957ebf0e756e8b)
	- [Feature Extractors - Case Study](https://www.loom.com/share/7492b62692b24617a3c87f3635af8942)
	- [Fine-Tuning](https://www.loom.com/share/eaf9c878e7474a1b864bd5e967ae2b05)
	- [Fine-Tuning Case Study I - Flowers 17](https://www.loom.com/share/57830f6349e84360a71ceac147e0ac41)
	- [Fine-Tuning Case Study II - Cats and Dogs](https://www.loom.com/share/9253be684b06472ea9705eb1cf7cf321)

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