Course curriculum

    1. Motivation

    2. Introduction Course Materials

    3. What is Deep Learning?

    4. Deep Learning Libraries

    5. CPU vs GPU vs TPU

    6. Working Environments

    7. Colab

    8. Setting Up Working Environments

    9. Notebook

    10. Face Recognition

    11. Face Recognition Files

    12. Canlı Tanışma: Kick-Off

    13. How To Use Course Materials

    14. Working With Zip Files

    15. Colab & Drive Connection

    16. Application: Age Detection (Prediction)

    17. Application: Gender & Race & Emotion Detection

    18. Assignment: Age, Gender, Race, Emotion Detection from Files

    19. Ödev Yükleme: Age, Gender, Race, Emotion Detection from Files

    20. Assignment Solution

    21. Bonus: Deep Learning History

    22. Deep Learning - Kurs Değerlendirme

    23. Canlı Ders 1: Introduction to Deep Learning

    24. Canlı Ders 2: Ödev Çözümü

    1. Course Materials

    2. Introduction

    3. Chapter 1: Basic Concepts of Artificial Neural Network

    4. Artificial Neuron

    5. Neural Network

    6. Activation Functions

    7. Cost Functions

    8. Application: Basic Concepts

    9. Exercises

    10. Chapter 2: Learning Process in Artificial Neural Network

    11. Initialization of Weights and Biases

    12. Forward Propagation

    13. Calculation of the Loss Function

    14. Backpropagation

    15. Update Weights I

    16. Update Weights II

    17. Common Problems in Learning Process

    18. Application: Learning Process in ANN

    19. Training

    20. Exercises

    21. Chapter 3: Tensorflow & Keras

    22. Install Tensorflow and Keras

    23. Data Preparation

    24. Building the Model

    25. Tensor & Tensorflow Dataset

    26. Using Tensorflow Dataset

    27. Data Preparation Using Tensorflow Dataset

    28. Model Evaluation

    29. Assignment: Tensorflow Dataset & Modeling

    30. Assignment Solution: Tensorflow Dataset & Modeling

    31. Ödev Yükleme: Tensorflow Dataset & Modeling

    32. Exercises

    33. Canlı Ders 3: Konu Anlatımı

    34. Canlı Ders 4: Ödev Çözümü

    35. Chapter 4: Enhancing Neural Network: Overfitting

    36. Train, Validation and Test Set

    37. Regularization

    38. Dropout

    39. Batch Normalization

    40. Early Stopping

    41. Data Augmentation

    42. Structured Data Modeling: Overview

    43. Structured Data Modeling I: Data Preparation

    44. Structured Data Modeling II: Modeling

    45. Structured Data Modeling III: Callbacks

    46. Structured Data Modeling IV: Training

    47. Structured Data Modeling V: Evaluation

    48. Structured Data Modeling VI: Loading Best Model

    49. Structured Data Modeling VII: Prediction

    50. Structured Data Modeling VIII: Hyperparameters

    51. Assignment: Checking the Overfitting

    52. Assignment Solution: Checking the Overfitting

    53. Ödev Yükleme: Checking the Overfitting

    54. Exercises

    55. Canlı Ders 5: Konu Anlatımı

    56. Chapter 5: Enhancing Neural Network: Hyperparameter Optimization

    57. Initialization Methods

    58. Application: Initialization Methods

    59. Layers, Units, Dropout

    60. Random Search

    61. Batch Size, Activation Functions, Learning Rate, Regularization

    62. All Together

    63. Final Model

    64. Assignment: Hyperparameter Optimization

    65. Assignment Solution: Hyperparameter Optimization

    66. Ödev Yükleme: Hyperparameter Optimization

    67. Exercises

    68. Chapter 6: Bonus: Optimization Algorithms (Batch, Mini Batch, SGD)

    69. Bonus: Momentum

    70. Bonus: RMSprop

    71. Bonus: Adam

    72. Recap

    73. Case Study: Telco Churn Prediction

    74. Functions

    75. Data Preparation

    76. Base Model with Binary Log Loss

    77. Monitoring with AUC

    78. Hyperparameter Optimization

    79. Retrain All Dataset

    80. Prediction Lecture Materials

    81. Prediction I

    82. Prediction II

    83. Prediction II

    84. Case Study: House Price Prediction

    85. Data Preparation

    86. Modelling

    87. Inverse Prediction

    88. Hyperparameter Optimization

    89. Error Analysis I

    90. Error Analysis II

    91. Assignment Lecture Files

    92. Assignment: New Patients Prediction

    93. Ödev Yükleme: New Patients Prediction

    94. Assignment Solution: New Patients Prediction

    1. Course Materials

    2. Chapter 1: Motivation & Introduction

    3. Introduction

    4. Pixels

    5. Gray Scale Images

    6. Color Images

    7. Exercises

    8. Chapter 2: Basic Concepts & Learning Process

    9. Feature Extraction

    10. What is Convolution?

    11. Convolution Layers

    12. Pooling, Flatten, Fully Connected Layers

    13. Learning Process

    14. Cost Function (Softmax, Cross Entropi, MacroAUC, MicroAUC)

    15. Exercises

    16. Chapter 3: Application Image Classification With CNN

    17. Data Preparation

    18. Create Model

    19. Model Compile

    20. Model Training

    21. Assigment: Image Classification

    22. Assigment Solution: Image Classification

    23. Case Study: Garbage Classification

    24. Case Study: Dataset Drive

    25. Case Study: Data Augmentation

    26. GoogleNet

    27. Exercises

    28. Chapter 4: CNN Architectures

    29. Model Sources

    30. Data Sets

    31. LeNet

    32. AlexNet

    33. VGG

    34. Application: VGG

    35. Application: Xception

    36. ResNet (Residual Network)

    37. Application: ResNet (Residual Network)

    38. Application: InceptionResNetV2

    39. More Models (MobileNet, DenseNet, EfficientNet)

    40. Application: MobileNet, DenseNet, EfficientNet

    41. Bonus: Compare All Models

    42. Bonus: PyTorch

    43. Assignment: Using Pretrained Model

    44. Assignment Solution: Using Pretrained Model

    45. Assignment Solution: Using Pretrained Model

    46. Exercises

    47. Chapter 5: Transfer Learning and Fine Tuning

    48. Frozen Layers: MobileNet Backbone

    49. Layers

    50. Training

    51. Model Performance

    52. Predictions

    53. Full Network Fine Tuning

    54. Full Network Training

    55. Full Network Prediction

    56. Assignment: Fine Tuning

    57. Assignment Solution: Fine Tuning

    58. Assignment Solution: Fine Tuning

    59. Exercises

    60. Chapter 6: Object Detection

    61. Sliding Window

    62. R-CNN

    63. Fast R-CNN

    64. Faster R-CNN

    65. Understanding the Object Detection Process: Data Structures

    66. Understanding the Object Detection Process: Training

    67. Understanding the Object Detection Process: Prediction

    68. Application: Faster R-CNN

    69. Prediction

    70. Detect Objects

    71. Mask R-CNN

    72. Application: Mask R-CNN

    73. SSD (Single Shot Multibox Detector)

    74. Application: SSD (Single Shot Multibox Detector)

    75. RetinaNet

    76. Application: RetinaNet

    77. YOLO (You Only Look Once)

    78. Application: YOLO v8

    79. Application: YOLO v3

    80. Application: Object Tracking with YOLO v3

    81. Application: Object Tracking with YOLO v8

    82. Application: Object Counting with YOLO v8

    83. Application: Real Time Object Detection

    84. Exercises

    1. Course Materials

    2. Chapter 1: Sequence Models and RNN

    3. What is Recurrent Neural Network?

    4. Basic Consepts of RNN

    5. Unfolded Graph

    6. How to Calculate Hidden State?

    7. Learning Process in RNN

    8. Exercises

    9. Long Term Dependencies

    10. Application: Weather Forecasting

    11. Data Preparation

    12. Time Series Visualisation

    13. Correlation HeatMap

    14. Anomaly Detection

    15. Time Based Splitting

    16. Scaling

    17. Time Window

    18. Model

    19. Model Performance

    20. Plot Predictions

    21. Chapter 2: Long Short-Term Memory (LSTM)

    22. Motivation

    23. Memory Cell

    24. Forget Gate

    25. Input Gate

    26. Output Gate

    27. Exercises

    28. Learning Process in LSTM

    29. Weather Forecasting with LSTM

    30. Weather Forecasting for All Data

    31. Hyperparameter Optimization

    32. Return Sequences

    33. Random Search

    34. Gated Recurrent Unit (GRU)

    35. Application: GRU

    36. Assignment: Weather Forecasting

    37. Assignment Solution: Weather Forecasting

    38. Chapter 3: Natural Language Processing (NLP)

    39. Mathematical Representations

    40. Bag of Words

    41. Word Embeddings

    42. Word2Vec & GloVe & FastText

    43. Download and Loading GloVe Vectors

    44. Cosine Similarity for Love & Hate

    45. Similarity Between Global Keywords and Countries

    46. King - Man + Woman = ?

    47. Exercises

    48. Emotion Classification I: Data Understanding

    49. Emotion Classification II: Text Preprocessing

    50. Emotion Classification III: Modeling with Keras Embeddings

    51. Emotion Classification IV: Play With Embeddings

    52. Emotion Classification V: 2D Visualization of Emotion Vectors

    53. Emotion Classification with GloVe & LSTM I: Data Preparation

    54. Emotion Classification with GloVe & LSTM II: Modeling

    55. Emotion Classification with GloVe & LSTM III: Play with Embeddings

    56. Emotion Classification with FastText & LSTM I: Modeling

    57. Emotion Classification with FastText & LSTM II: Prediction

    58. Assignment: Word Embeddings

    59. Assignment Solution: Word Embeddings

    60. Tebrikler!

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