وب سایت تخصصی شرکت فرین
دسته بندی دوره ها
4

Python for Deep Learning and Artificial Intelligence

سرفصل های دوره

Neural Networks, TensorFlow, ANN, CNN, RNN, LSTM, Transfer Learning and Much More


1. Course Setup
  • 1.1 python-for-deep-learning-and-ai.zip
  • 1. Jupyter Notebook Introduction

  • 2. Python for Deep Learning
  • 1. Python Introduction Part 1
  • 2. Python Introduction Part 2
  • 3. Python Introduction Part 3
  • 4. Numpy Introduction Part 1
  • 5. Numpy Introduction Part 2
  • 6. Pandas Introduction
  • 7. Matplotlib Introduction Part 1
  • 8. Matplotlib Introduction Part 2
  • 9. Seaborn Introduction Part 1
  • 10. Seaborn Introduction Part 2

  • 3. Introduction to Machine Learning
  • 1. Classical Machine Learning Introduction
  • 2. Logistic Regression
  • 3. Support Vector Machine - SVM
  • 4. Decision Tree
  • 5. Random Forest
  • 6. L2 Regularization
  • 7. L1 Regularization
  • 8. Model Evaluation
  • 9. ROC-AUC Curve
  • 10. Code Along in Python Part 1
  • 11. Code Along in Python Part 2
  • 12. Code Along in Python Part 3
  • 13. Code Along in Python Part 4

  • 4. Introduction to Deep Learning and TensorFlow
  • 1. Machine Learning Process Introduction
  • 2. Types of Machine Learning
  • 3. Supervised Learning
  • 4. Unsupervised Learning
  • 5. Reinforcement Learning
  • 6. What is Deep Learning and ML
  • 7. What is Neural Network
  • 8. How Deep Learning Process Works
  • 9. Application of Deep Learning
  • 10. Deep Learning Tools
  • 11. MLops with AWS

  • 5. End to End Deep Learning Project
  • 1. What is Neuron
  • 2. Multi-Layer Perceptron
  • 3. Shallow vs Deep Neural Networks
  • 4. Activation Function
  • 5. What is Back Propagation
  • 6. Optimizers in Deep Learning
  • 7. Steps to Build Neural Network
  • 8. Customer Churn Dataset Loading
  • 9. Data Visualization Part 1
  • 10. Data Visualization Part 2
  • 11. Data Preprocessing
  • 12. Import Neural Networks APIs
  • 13. How to Get Input Shape and Class Weights
  • 14. Neural Network Model Building
  • 15. Model Summary Explanation
  • 16. Model Training
  • 17. Model Evaluation
  • 18. Model Save and Load
  • 19. Prediction on Real-Life Data

  • 6. Introduction to Computer Vision with Deep Learning
  • 1. Introduction to Computer Vision with Deep Learning
  • 2. 5 Steps of Computer Vision Model Building
  • 3. Fashion MNIST Dataset Download
  • 4. Fashion MNIST Dataset Analysis
  • 5. Train Test Split for Data
  • 6. Deep Neural Network Model Building
  • 7. Model Summary and Training
  • 8. Discovering Overfitting - Early Stopping
  • 9. Model Save and Load for Prediction

  • 7. Introduction to Convolutional Neural Networks [Theory and Intuitions]
  • 1. What is Convolutional Neural Network
  • 2. Working Principle of CNN
  • 3. Convolutional Filters
  • 4. Feature Maps
  • 5. Padding and Strides
  • 6. Pooling Layers
  • 7. Activation Function
  • 8. Dropout
  • 9. CNN Architectures Comparison
  • 10. LeNet-5 Architecture Explained
  • 11. AlexNet Architecture Explained
  • 12. GoogLeNet (Inception V1) Architecture Explained
  • 13. RestNet Architecture Explained
  • 14. MobileNet Architecture Explained
  • 15. EfficientNet Architecture Explained

  • 8. Horses vs Humans Classification with Simple CNN
  • 1. Overview of Image Classification using CNNs
  • 2. Introduction to TensorFlow Datasets (TFDS)
  • 3. Download Humans or Horses Dataset Part 1
  • 4. Download Humans or Horses Dataset Part 2
  • 5. Use of Image Data Generator
  • 6. Data Display in Subplots Matrix
  • 7. CNN Introduction
  • 8. Building CNN Model
  • 9. CNN Parameter Calculation
  • 10. CNN Parameter Calculations Part 2
  • 11. CNN Parameter Calculations Part 3
  • 12. Model Training
  • 13. Model Load and Save
  • 14. Image Class Prediction

  • 9. Building Cats and Dogs Classifier with Regularized CNN
  • 1. What is Overfitting
  • 2. L1, L2 and Early Stopping Regularization
  • 3. How Dropout and Batch Normalization Prevents Overfitting
  • 4. What is Data Augmentation [Theory]
  • 5. Sample Data Load with ImageDataGenerator for Augmentation
  • 6. Random Rotation Augmentation
  • 7. Random Shift Augmentation
  • 8. Other Types of Data Augmentation
  • 9. All Types of Augmentation at Once
  • 10. TensorFlow TFDS and Cats vs Dogs Data Download
  • 11. Store Data in Local Directory
  • 12. Load Dataset for Baseline Classifier
  • 13. Building Baseline CNN Classifier
  • 14. How to Calculate Size of Output Layers of CNN and MaxPool
  • 15. How to Calculate Number of Parameters in CNN and FCN
  • 16. Model Training and Layers Analysis
  • 17. Model Training and Validation Accuracy Plot
  • 18. Building Dataset for Regularized CNN
  • 19. Regularized CNN Model Building and Training
  • 20. Training Log Analysis
  • 21. Load Model and Do the Prediction
  • 22. CNN Model Visualization

  • 10. Flowers Classification with Transfer Learning and CNN
  • 1. Transfer Learning Introduction
  • 2. Load Flowers Dataset for Classification
  • 3. Download Flowers Data
  • 4. Flowers Data Visualization
  • 5. Preparing Data with Image Data Generator
  • 6. Baseline CNN Model Building
  • 7. How to Calculate Number of Parameters in CNN
  • 8. Baseline CNN Model Training
  • 9. Train Model with TFDS Data Without Saving Locally Part 1
  • 10. Train Model with TFDS Data Without Saving Locally Part 2
  • 11. import VGG16 from Keras
  • 12. Data Augmentation for Training
  • 13. Make CNN Model with VGG16 Transfer Learning
  • 14. Model Training for Better Accuracy
  • 15. Train Any Model for Transfer Learning
  • 16. Save and Load Model with Class Names
  • 17. Online Prediction of Flowers Classes

  • 11. Introduction to NLP
  • 1. Introduction to NLP
  • 2. What are Key NLP Techniques
  • 3. Overview of NLP Tools
  • 4. Common Challenges in NLP
  • 5. Bag of Words - The Simples Word Embedding Technique
  • 6. Term Frequency - Inverse Document Frequency (TF-IDF)
  • 7. Load Spam Dataset
  • 8. Text Preprocessing
  • 9. Feature Engineering
  • 10. Pair Plot
  • 11. Train Test Split
  • 12. TF-IDF Vectorization
  • 13. Model Evaluation and Prediction on Real Data
  • 14. Model Load and Store
  • 139,000 تومان
    بیش از یک محصول به صورت دانلودی میخواهید؟ محصول را به سبد خرید اضافه کنید.
    خرید دانلودی فوری

    در این روش نیاز به افزودن محصول به سبد خرید و تکمیل اطلاعات نیست و شما پس از وارد کردن ایمیل خود و طی کردن مراحل پرداخت لینک های دریافت محصولات را در ایمیل خود دریافت خواهید کرد.

    ایمیل شما:
    تولید کننده:
    شناسه: 17201
    حجم: 7101 مگابایت
    مدت زمان: 1025 دقیقه
    تاریخ انتشار: ۱۲ مرداد ۱۴۰۲
    طراحی سایت و خدمات سئو

    139,000 تومان
    افزودن به سبد خرید