🧩 Part 1/12

✔ What is Keras and why use it?
✔ What is TensorFlow and how does it work?
✔ The core concepts of TensorFlow
✔ The benefits and challenges of TensorFlow
✔ How to install Keras and TensorFlow on your machine?
✔ Installing Keras and TensorFlow on Windows
✔ Installing Keras and TensorFlow on Linux
✔ Installing Keras and TensorFlow on Mac OS
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🧩 Part 2/12

✔ What is a Neural Network?
✔ Basic Concepts and Terminology
✔ Types of Neural Networks
✔ How to Install Keras and TensorFlow
✔ How to Create a Simple Neural Network with Keras
✔ Define the Model Architecture
✔ Compile the Model
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🧩 Part 3/12

✔ What are Activation Functions and Optimizers?
✔ Activation Functions
✔ Optimizers
✔ How to Choose the Right Activation Function and Optimizer?
✔ How to Implement Different Activation Functions and Optimizers in Keras and TensorFlow?
✔ How to Evaluate the Performance of Different Activation Functions and Optimizers?
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🧩 Part 4/12

✔ What are Convolutional Neural Networks?
✔ Convolutional Layers
✔ Pooling Layers
✔ Fully Connected Layers
✔ How to Use Keras and TensorFlow for Image Processing?
✔ Loading and Preprocessing Images
✔ Building and Training a CNN Model
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🧩 Part 5/12

✔ What are Recurrent Neural Networks?
✔ The Basic RNN Cell
✔ The Long Short-Term Memory (LSTM) Cell
✔ The Gated Recurrent Unit (GRU) Cell
✔ How to Use Keras and TensorFlow to Build and Train RNNs
✔ Preparing the Data
✔ Defining the Model Architecture
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🧩 Part 6/12

✔ Audio Processing Basics
✔ Sampling Rate and Bit Depth
✔ Waveform and Spectrogram
✔ Feature Extraction and Normalization
✔ Keras and TensorFlow for Audio and Speech Recognition
✔ Loading and Preprocessing Audio Data
✔ Building and Training Speech Recognition Models
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🧩 Part 7/12

✔ What is Time Series and Why is it Important?
✔ How to Prepare Data for Time Series Analysis
✔ How to Build and Train Time Series Models with Keras and TensorFlow
✔ Linear Regression
✔ Recurrent Neural Networks
✔ Convolutional Neural Networks
✔ Transformer Networks
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🧩 Part 8/12

✔ Generative Models and Adversarial Networks: Concepts and Applications
✔ What are Generative Models and Adversarial Networks?
✔ Why are they useful and what are some examples?
✔ Setting up the Environment: Installing Keras and TensorFlow
✔ Building a Generative Model: Variational Autoencoder (VAE)
✔ What is a VAE and how does it work?
✔ How to implement a VAE in Keras and TensorFlow?
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🧩 Part 9/12

✔ Reinforcement Learning Basics
✔ What is Reinforcement Learning?
✔ The Reinforcement Learning Problem
✔ Types of Reinforcement Learning Algorithms
✔ Q-Learning: A Simple but Powerful Algorithm
✔ What is Q-Learning?
✔ How Q-Learning Works
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🧩 Part 10/12

✔ What is Model Deployment and Serving?
✔ How to Deploy and Serve Models with TensorFlow Serving
✔ How to Deploy and Serve Models with TensorFlow Lite
✔ How to Deploy and Serve Models with TensorFlow.js
✔ Comparison and Best Practices of Different Deployment and Serving Options
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🧩 Part 11/12

✔ Testing and Debugging with Keras
✔ Using Callbacks and Checkpoints
✔ Handling Errors and Exceptions
✔ Testing and Debugging with TensorFlow Debugger
✔ Installing and Running TensorFlow Debugger
✔ Debugging Common Issues with TensorFlow Debugger
✔ Testing and Debugging with TensorFlow Profiler
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🧩 Part 12/12

✔ Choosing the Right Framework
✔ Keras vs TensorFlow
✔ TensorFlow 2.0 and Keras Integration
✔ Optimizing Data Processing and Loading
✔ Using tf.data API
✔ Applying Data Augmentation
✔ Building and Training Models
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