해당 커리큘럼 목록
<TensorFlow Object Detection API Guide Part 1 - Object Detection by Modifying 10 Lines of Code> Section 1
- Introduction to the Object Detection Problem Domain
- Object Detection Metric - IoU, mAP
- Object Detection Datasets – Pascal VOC, MS COCO, KITTI, Open Images
<TensorFlow Object Detection API Guide Part 1 - Detecting Objects by Modifying 10 Lines of Code> Section 3
- Introduction to TensorFlow Object Detection API
<TensorFlow Object Detection API Guide Part 1 - Object Detection with 10 Lines of Code Change> Section 4
- R-CNN(Regions with CNN)
- Fast R-CNN
- Faster R-CNN
- Non-Maximum Suppression (NMS)
- SSD(Single Shot MultiBox Detector)
- RetinaNet
- CenterNet
<TensorFlow Object Detection API Guide Part1 - Object Detection with 10 Lines of Code Modification> Section 5
- Object Detection using a Pre-Trained Model
<Introduction to Deep Learning with TensorFlow 2.0> Section 1
- Artificial Intelligence, Machine Learning, Deep Learning & Supervised Learning, Unsupervised Learning, Reinforcement Learning
- Deep Learning and TensorFlow Application Fields
- A Brief Overview of the History of Deep Learning
<Introduction to Deep Learning with TensorFlow 2.0> Section 3
- Basic Process of Machine Learning - Defining Hypothesis, Defining Loss Function, Defining Optimization
- Implementing Linear Regression Algorithm using TensorFlow 2.0
- Batch Gradient Descent, Stochastic Gradient Descent, Mini-Batch Gradient Descent
- Training Data, Validation Data, Test Data & Overfitting
- Softmax Regression & Cross-Entropy Loss Function & One-hot Encoding & MNIST
- TensorFlow 2.0 Keras Subclassing
- Implementing an MNIST Digit Classifier using TensorFlow 2.0 and Softmax Regression
<Introduction to Deep Learning with TensorFlow 2.0> Section 4
- Multi-Layer Perceptron MLP
- Implementing an MNIST Digit Classifier using TensorFlow 2.0 and ANN
<Introduction to Deep Learning with TensorFlow 2.0> Section 5
- Concept of AutoEncoder
- MNIST Data Reconstruction Using TensorFlow 2.0 and Autoencoders
<Introduction to Deep Learning with TensorFlow 2.0> Section 6
- The Challenges of Computer Vision Problems and the Advent of the CNN-based Computer Vision Era
- Core Concepts of Convolutional Neural Networks - Convolution, Pooling
- Implementing CNN for MNIST Digit Classification Using TensorFlow 2.0
- Dropout
- Implementing CNN for CIFAR-10 Image Classification Using TensorFlow 2.0
<Introduction to Deep Learning with TensorFlow 2.0> Section 7
- Recurrent Neural Network (RNN)
- Vanishing Gradient Problem & LSTM & GRU
- Concept of Embedding & Char-RNN
- Implementing Char-RNN using TensorFlow 2.0
<Introduction to Deep Learning with TensorFlow 2.0> Section 8
- Saving and loading parameters using the tf.train.CheckpointManager API
- Visualizing the learning process using TensorBoard
<Introduction to Deep Learning with TensorFlow 2.0> Section 9
- Introduction to various Computer Vision problem domains
- Introduction to Various Natural Language Processing (NLP) Problem Domains