🧩 Part 1/15

✔ What is Deep Learning?
✔ Why Learn Deep Learning?
✔ Applications of Deep Learning
✔ Challenges of Deep Learning
✔ How to Learn Deep Learning?
✔ Prerequisites for Deep Learning
✔ Resources for Deep Learning
✔ How to Set Up Your Environment for Deep Learning?
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🧩 Part 2/15

✔ Linear Regression: Theory and Implementation
✔ What is Linear Regression?
✔ How to Implement Linear Regression with TensorFlow?
✔ Dataset: Boston Housing Prices
✔ Data Preprocessing and Visualization
✔ Model Training and Evaluation
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🧩 Part 3/15

✔ Logistic Regression
✔ The Sigmoid Function
✔ The Cost Function
✔ The Gradient Descent Algorithm
✔ TensorFlow Basics
✔ Tensors and Operations
✔ Variables and Placeholders
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🧩 Part 4/15

✔ What is a Neural Network?
✔ Neurons and Layers
✔ Activation Functions
✔ Network Architecture
✔ How to Train a Neural Network?
✔ Loss Function
✔ Gradient Descent
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🧩 Part 5/15

✔ What is a Convolutional Neural Network?
✔ Convolutional Layer
✔ Pooling Layer
✔ Fully Connected Layer
✔ How to Implement a Convolutional Neural Network with TensorFlow?
✔ Importing Libraries and Data
✔ Preprocessing Data
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🧩 Part 6/15

✔ What is a Recurrent Neural Network?
✔ The Basic Structure of an RNN
✔ The Challenges of Training an RNN
✔ How to Implement an RNN with TensorFlow
✔ Preparing the Data
✔ Building the Model
✔ Training and Evaluating the Model
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🧩 Part 7/15

✔ What is an Autoencoder?
✔ Encoder
✔ Decoder
✔ Loss Function
✔ Building an Autoencoder with TensorFlow
✔ Importing Libraries and Data
✔ Defining the Model Architecture
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🧩 Part 8/15

✔ What are Generative Adversarial Networks?
✔ The Generator
✔ The Discriminator
✔ The Training Process
✔ How to Implement GANs in TensorFlow
✔ Define the Model Architecture
✔ Define the Loss Functions and Optimizers
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🧩 Part 9/15

✔ TensorFlow Basics for RL
✔ Installing and Importing TensorFlow
✔ Building and Training a Neural Network Model
✔ RL Concepts and Terminology
✔ RL Algorithms and Methods
✔ Value-Based Methods
✔ Policy-Based Methods
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🧩 Part 10/15

✔ What is Transfer Learning?
✔ Types of Transfer Learning
✔ Benefits and Challenges of Transfer Learning
✔ How to Use Transfer Learning with TensorFlow
✔ Load and Preprocess the Data
✔ Choose a Pretrained Model
✔ Fine-Tune the Model
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🧩 Part 11/15

✔ What is Attention Mechanism?
✔ Types of Attention Mechanism
✔ Benefits of Attention Mechanism
✔ How to Implement Attention Mechanism with TensorFlow
✔ Encoder-Decoder Architecture
✔ Attention Layer
✔ Attention Vector
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🧩 Part 12/15

✔ Transformer Model Architecture
✔ Encoder and Decoder
✔ Self-Attention and Multi-Head Attention
✔ Positional Encoding and Feed-Forward Network
✔ TensorFlow Implementation
✔ Building the Model
✔ Preparing the Data
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🧩 Part 13/15

✔ What are Graphs and Graph Neural Networks?
✔ Graphs and their properties
✔ Graph Neural Networks and their applications
✔ How to Implement a Graph Neural Network with TensorFlow
✔ Installing and importing TensorFlow and other libraries
✔ Loading and preprocessing a graph dataset
✔ Defining and creating a graph convolution layer
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🧩 Part 14/15

✔ What is Meta-Learning?
✔ Types of Meta-Learning
✔ Benefits and Challenges of Meta-Learning
✔ What is Few-Shot Learning?
✔ Types of Few-Shot Learning
✔ Metrics and Benchmarks for Few-Shot Learning
✔ How to Implement Meta-Learning with TensorFlow
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🧩 Part 15/15

✔ Current Challenges of Deep Learning
✔ Data Quality and Availability
✔ Explainability and Interpretability
✔ Scalability and Efficiency
✔ Future Trends of Deep Learning
✔ Self-Supervised Learning
✔ Generative Adversarial Networks
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