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. Canlı Ders 6: Ödev Çözümü

    57. Chapter 5: Enhancing Neural Network: Hyperparameter Optimization

    58. Initialization Methods

    59. Application: Initialization Methods

    60. Layers, Units, Dropout

    61. Random Search

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

    63. All Together

    64. Final Model

    65. Assignment: Hyperparameter Optimization

    66. Assignment Solution: Hyperparameter Optimization

    67. Ödev Yükleme: Hyperparameter Optimization

    68. Exercises

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

    70. Bonus: Momentum

    71. Bonus: RMSprop

    72. Bonus: Adam

    73. Recap

    74. Case Study: Telco Churn Prediction

    75. Functions

    76. Data Preparation

    77. Base Model with Binary Log Loss

    78. Monitoring with AUC

    79. Hyperparameter Optimization

    80. Retrain All Dataset

    81. Prediction Lecture Materials

    82. Prediction I

    83. Prediction II

    84. Prediction II

    85. Case Study: House Price Prediction

    86. Data Preparation

    87. Modelling

    88. Inverse Prediction

    89. Hyperparameter Optimization

    90. Error Analysis I

    91. Error Analysis II

    92. Assignment Lecture Files

    93. Assignment: New Patients Prediction

    94. Ödev Yükleme: New Patients Prediction

    95. Assignment Solution: New Patients Prediction

    96. Canlı Ders 7: Konu Anlatımı

    97. Canlı Ders 8: Ödev Çözümü

    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. Ödev Yükleme: Image Classification

    23. Assigment Solution: Image Classification

    24. Case Study: Garbage Classification

    25. Case Study: Dataset Drive

    26. Case Study: Data Augmentation

    27. GoogleNet

    28. Exercises

    29. Canlı Ders 9: Konu Anlatımı

    30. Canlı Ders 10: Ödev Çözümü

    31. Chapter 4: CNN Architectures

    32. Model Sources

    33. Data Sets

    34. LeNet

    35. AlexNet

    36. VGG

    37. Application: VGG

    38. Application: Xception

    39. ResNet (Residual Network)

    40. Application: ResNet (Residual Network)

    41. Application: InceptionResNetV2

    42. More Models (MobileNet, DenseNet, EfficientNet)

    43. Application: MobileNet, DenseNet, EfficientNet

    44. Bonus: Compare All Models

    45. Bonus: PyTorch

    46. Assignment: Using Pretrained Model

    47. Assignment Solution: Using Pretrained Model

    48. Ödev Yükleme: Using Pretrained Model

    49. Assignment Solution: Using Pretrained Model

    50. Exercises

    51. Chapter 5: Transfer Learning and Fine Tuning

    52. Frozen Layers: MobileNet Backbone

    53. Layers

    54. Training

    55. Model Performance

    56. Predictions

    57. Full Network Fine Tuning

    58. Full Network Training

    59. Full Network Prediction

    60. Assignment: Fine Tuning

    61. Ödev Yükleme: Fine Tuning

    62. Assignment Solution: Fine Tuning

    63. Assignment Solution: Fine Tuning

    64. Exercises

    65. Chapter 6: Object Detection

    66. Sliding Window

    67. R-CNN

    68. Fast R-CNN

    69. Faster R-CNN

    70. Understanding the Object Detection Process: Data Structures

    71. Understanding the Object Detection Process: Training

    72. Understanding the Object Detection Process: Prediction

    73. Application: Faster R-CNN

    74. Prediction

    75. Detect Objects

    76. Mask R-CNN

    77. Application: Mask R-CNN

    78. SSD (Single Shot Multibox Detector)

    79. Application: SSD (Single Shot Multibox Detector)

    80. RetinaNet

    81. Application: RetinaNet

    82. YOLO (You Only Look Once)

    83. Application: YOLO v8

    84. Application: YOLO v3

    85. Application: Object Tracking with YOLO v3

    86. Application: Object Tracking with YOLO v8

    87. Application: Object Counting with YOLO v8

    88. Application: Real Time Object Detection

    89. Canlı Ders 11: Konu Anlatımı

    90. Canlı Ders 12: Ödev Çözümü

    91. 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. Canlı Ders 13: Konu Anlatımı

    29. Canlı Ders 14: Ödev Çözümü

    30. Learning Process in LSTM

    31. Weather Forecasting with LSTM

    32. Weather Forecasting for All Data

    33. Hyperparameter Optimization

    34. Return Sequences

    35. Random Search

    36. Gated Recurrent Unit (GRU)

    37. Application: GRU

    38. Assignment: Weather Forecasting

    39. Assignment Solution: Weather Forecasting

    40. Chapter 3: Natural Language Processing (NLP)

    41. Mathematical Representations

    42. Bag of Words

    43. Word Embeddings

    44. Word2Vec & GloVe & FastText

    45. Download and Loading GloVe Vectors

    46. Cosine Similarity for Love & Hate

    47. Similarity Between Global Keywords and Countries

    48. King - Man + Woman = ?

    49. Exercises

    50. Emotion Classification I: Data Understanding

    51. Emotion Classification II: Text Preprocessing

    52. Emotion Classification III: Modeling with Keras Embeddings

    53. Emotion Classification IV: Play With Embeddings

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

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

    56. Emotion Classification with GloVe & LSTM II: Modeling

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

    58. Emotion Classification with FastText & LSTM I: Modeling

    59. Emotion Classification with FastText & LSTM II: Prediction

    60. Assignment: Word Embeddings

    61. Assignment Solution: Word Embeddings

    62. Ödev Yükleme: Word Embeddings

    63. Tebrikler!

    1. Kurs Metaryelleri

    2. Üretken Yapay Zeka vs Klasik Yapay Zeka

    3. Çekişmeli Üretici Ağlar (GANS)

    4. Transformer Mimarisi_1

    5. Transformer Mimarisi_1

    6. Büyük Dil Modelleri (LLMs)

    7. Büyük Dil Modelleri Sözlüğü

    8. Token ve Tokenization

    9. Bağlam Penceresi

    10. Parametreler

    11. Modellerin Karşılaştırılması

    12. Ölçekleme İlkeleri

    13. Dil Modelleri Genel Değerlendirme

    14. Difüzyon Modelleri

    15. Difüzyon Modelleri Genel Değerlendirme

    16. Canlı Ders 15: Konu Anlatımı

    17. Canlı Ders 16: Ödev Çözümü

    1. Case Study

    2. Ödev Yükleme

About this course

  • 380 ders
  • 21 saat video içeriği