- Generative AI Introduction and Setup
- Generative AI - Course Introduction
- Generative AI - Environment Setup
- Generative AI Advanced Neural Network Architectures
- Generative AI - Advanced CNNs
- Generative AI - Implementing ResNet for Image Classification Tasks from Scratch
- Generative AI - RNNs and LSTMs
- Generative AI - Understanding RNNs
- Generative AI - Code Sample for RNN
- Generative AI - Understanding LSTMs
- Generative AI - Code Sample for LSTM
- Generative AI - Understanding GRUs
- Generative AI - Code Sample for GRU
- Generative AI GANs
- Generative AI - Understanding the Architecture of GANs
- Generative AI -Role of the Generator and Discriminator
- Generative AI - Implementing a Simple GAN for Image Generation
- Generative AI - Advanced GAN Techniques
- Generative AI - Implementing a Conditional GAN for MNIST Digit Generation
- Generative AI - Wasserstein GANs (WGANs)
- Generative AI - Implementing a WGAN for MNIST Digit Generation
- Generative AI - CycleGANs for image translation
- Generative AI - Implementing a CycleGAN for Image Translation
- Generative AI VAEs
- Generative AI - Understanding the Architecture and Functioning of VAEs
- Generative AI - The Concept of Latent Space and Reconstruction
- Generative AI - Practical Coding: Implementing VAEs
- Generative AI - Enhancing the VAE
- Generative AI Transformer Models
- Generative AI - Architecture of the Transformer model
- Generative AI - The Attention Mechanism and Its Importance
- Generative AI - Practical Coding: Implementing Transformers
- Generative AI -Implementing a Transformer for Text generation
- Generative AI Applications
- Generative AI - Fine-tuning GPT-3 for Custom Text Generation Tasks
- Generative AI - Enhancing Fine Tuning with More Training Data
- Generative AI - Implementing Neural Style Transfer
- Generative AI - Using GANs for High Resolution Image Synthesis
- Generative AI - Music and Audio Generation
- Generative AI Optimization and Fine-Tuning
- Generative AI - Techniques for Optimizing Model Performance
- Generative AI - Using Grid Search and Random Search for Hyperparameter Tuning
- Generative AI - Transfer Learning with BERT Model
- Generative AI Real-World Projects
- Generative AI - Generating Realistic Faces with GANs
- Generative AI - Building a Chatbot with Transformer Models
- Generative AI Best Practices and Advanced Tips
- Generative AI - Model Evaluation and Validation
- Generative AI - Measurements of Quantitative Importance for Text Generation
- Generative AI - Deploying Generative Models
- Generative AI Future Trends and Research Directions
- Generative AI - Emerging Trends
- Generative AI - Ethical Considerations
- Conclusion
Generative AI - Environment Setup
Environment Setup
Everything in your environment has to be prepared before you can begin our course. Make sure Python is already installed on your PC before proceeding. Go to the Python website, download the most recent version, and then install it according to your operating system's instructions.
The next step is to set up the necessary libraries, such as PyTorch and TensorFlow. Launch the command prompt or terminal and execute these commands:
pip install tensorflow
pip install torch
pip install torchvisionThe following instructions will install the necessary libraries for image-related tasks: TensorFlow, PyTorch, and torchvision.
The next step is to set up Jupyter Notebook. You can create and share documents with live code, equations, graphs, and narrative prose with Jupyter Notebook, an open-source online tool. Follow these steps to install Jupyter Notebook:
pip install notebookJust type jupyter notebook into your terminal to launch Jupyter Notebook when installation is complete. In your usual web browser, a new tab will come up where you can manage and create your notes.
You can use Google Colab as a substitute if you don't have a strong GPU. One free cloud service that works well for training big models is Google Colab, which includes GPU computation. Enter your Google credentials at the Google Colab login page to begin using Google Colab. Launch the notebook creation menu, then go to "Runtime" > "Change runtime type" > "Hardware accelerator." From there, you can choose a graphics processing unit (GPU) to speed up your calculations.
If you install these programs on your computer, you'll be ready to use generative AI to its maximum potential and succeed in this course's practical assignments.
If you are using Jupyter notebook, the steps to install these libraries are the same. In my opinion, jupyter notebook will be best to use.
