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

    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. Chapter 4: Enhancing Neural Network: Overfitting

    34. Train, Validation and Test Set

    35. Regularization

    36. Dropout

    37. Batch Normalization

    38. Early Stopping

    39. Data Augmentation

    40. Structured Data Modeling: Overview

    41. Structured Data Modeling I: Data Preparation

    42. Structured Data Modeling II: Modeling

    43. Structured Data Modeling III: Callbacks

    44. Structured Data Modeling IV: Training

    45. Structured Data Modeling V: Evaluation

    46. Structured Data Modeling VI: Loading Best Model

    47. Structured Data Modeling VII: Prediction

    48. Structured Data Modeling VIII: Hyperparameters

    49. Assignment: Checking the Overfitting

    50. Assignment Solution: Checking the Overfitting

    51. Ödev Yükleme: Checking the Overfitting

    52. Exercises

    53. Chapter 5: Enhancing Neural Network: Hyperparameter Optimization

    54. Initialization Methods

    55. Application: Initialization Methods

    56. Layers, Units, Dropout

    57. Random Search

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

    59. All Together

    60. Final Model

    61. Assignment: Hyperparameter Optimization

    62. Assignment Solution: Hyperparameter Optimization

    63. Ödev Yükleme: Hyperparameter Optimization

    64. Exercises

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

    66. Bonus: Momentum

    67. Bonus: RMSprop

    68. Bonus: Adam

    69. Recap

    70. Case Study: Telco Churn Prediction

    71. Functions

    72. Data Preparation

    73. Base Model with Binary Log Loss

    74. Monitoring with AUC

    75. Hyperparameter Optimization

    76. Retrain All Dataset

    77. Prediction Lecture Materials

    78. Prediction I

    79. Prediction II

    80. Prediction II

    81. Case Study: House Price Prediction

    82. Data Preparation

    83. Modelling

    84. Inverse Prediction

    85. Hyperparameter Optimization

    86. Error Analysis I

    87. Error Analysis II

    88. Assignment Lecture Files

    89. Assignment: New Patients Prediction

    90. Ödev Yükleme: New Patients Prediction

    91. 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. 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. Chapter 4: CNN Architectures

    30. Model Sources

    31. Data Sets

    32. LeNet

    33. AlexNet

    34. VGG

    35. Application: VGG

    36. Application: Xception

    37. ResNet (Residual Network)

    38. Application: ResNet (Residual Network)

    39. Application: InceptionResNetV2

    40. More Models (MobileNet, DenseNet, EfficientNet)

    41. Application: MobileNet, DenseNet, EfficientNet

    42. Bonus: Compare All Models

    43. Bonus: PyTorch

    44. Assignment: Using Pretrained Model

    45. Assignment Solution: Using Pretrained Model

    46. Ödev Yükleme: Using Pretrained Model

    47. Assignment Solution: Using Pretrained Model

    48. Exercises

    49. Chapter 5: Transfer Learning and Fine Tuning

    50. Frozen Layers: MobileNet Backbone

    51. Layers

    52. Training

    53. Model Performance

    54. Predictions

    55. Full Network Fine Tuning

    56. Full Network Training

    57. Full Network Prediction

    58. Assignment: Fine Tuning

    59. Ödev Yükleme: Fine Tuning

    60. Assignment Solution: Fine Tuning

    61. Assignment Solution: Fine Tuning

    62. Exercises

    63. Chapter 6: Object Detection

    64. Sliding Window

    65. R-CNN

    66. Fast R-CNN

    67. Faster R-CNN

    68. Understanding the Object Detection Process: Data Structures

    69. Understanding the Object Detection Process: Training

    70. Understanding the Object Detection Process: Prediction

    71. Application: Faster R-CNN

    72. Prediction

    73. Detect Objects

    74. Mask R-CNN

    75. Application: Mask R-CNN

    76. SSD (Single Shot Multibox Detector)

    77. Application: SSD (Single Shot Multibox Detector)

    78. RetinaNet

    79. Application: RetinaNet

    80. YOLO (You Only Look Once)

    81. Application: YOLO v8

    82. Application: YOLO v3

    83. Application: Object Tracking with YOLO v3

    84. Application: Object Tracking with YOLO v8

    85. Application: Object Counting with YOLO v8

    86. Application: Real Time Object Detection

    87. 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. Ödev Yükleme: Word Embeddings

    61. 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

    1. Case Study

    2. Ödev Yükleme

About this course

  • 362 ders
  • 21 saat video içeriği