Create README.md for examples/ (#1221)

* Create README.md

added all PyT examples
Signed-off-by: Santosh Bhavani <santosh.bhavani@live.com>

* Update README.md

- Added JAX, PaddlePaddle, and third-party examples
- Fixed DL framework links
- Removed issue request for new PRs
Signed-off-by: Santosh Bhavani <sbhavani@nvidia.com>

---------
Signed-off-by: Santosh Bhavani <santosh.bhavani@live.com>
Signed-off-by: Santosh Bhavani <sbhavani@nvidia.com>
Co-authored-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com>

examples/README.md

+# Examples
+
+We provide a variety of examples for deep learning frameworks including [PyTorch](https://github.com/pytorch/pytorch), [JAX](https://github.com/jax-ml/jax), and [PaddlePaddle](https://github.com/PaddlePaddle/Paddle). 
+Additionally, we offer [Jupyter notebook tutorials](https://github.com/NVIDIA/TransformerEngine/tree/main/docs/examples) and a selection of [third-party examples](#third-party). Please be aware that these third-party examples might need specific, older versions of dependencies to function properly.
+
+# PyTorch
+
+- [Accelerate Hugging Face Llama models with TE](https://github.com/NVIDIA/TransformerEngine/blob/main/docs/examples/te_llama/tutorial_accelerate_hf_llama_with_te.ipynb)
+  - Provides code examples and explanations for integrating TE with the LLaMA2 and LLaMA2 models.
+- [PyTorch FSDP with FP8](https://github.com/NVIDIA/TransformerEngine/tree/main/examples/pytorch/fsdp)
+  - **Distributed Training**: How to set up and run distributed training using PyTorch’s FullyShardedDataParallel (FSDP) strategy.
+  - **TE Integration**: Instructions on integrating TE/FP8 with PyTorch for optimized performance.
+  - **Checkpointing**: Methods for applying activation checkpointing to manage memory usage during training.
+- [Attention backends in TE](https://github.com/NVIDIA/TransformerEngine/blob/main/docs/examples/attention/attention.ipynb)
+  - **Attention Backends**: Describes various attention backends supported by Transformer Engine, including framework-native, fused, and flash-attention backends, and their performance benefits.
+  - **Flash vs. Non-Flash**: Compares the flash algorithm with the standard non-flash algorithm, highlighting memory and computational efficiency improvements.
+  - **Backend Selection**: Details the logic for selecting the most appropriate backend based on availability and performance, and provides user control options for backend selection.
+- [Overlapping Communication with GEMM](https://github.com/NVIDIA/TransformerEngine/tree/main/examples/pytorch/comm_gemm_overlap)
+  - Training a TE module with GEMM and communication overlap, including various configurations and command-line arguments for customization.
+- [Performance Optimizations](https://github.com/NVIDIA/TransformerEngine/blob/main/docs/examples/advanced_optimizations.ipynb)
+  - **Multi-GPU Training**: How to use TE with data, tensor, and sequence parallelism.
+  - **Gradient Accumulation Fusion**: Utilizing Tensor Cores to accumulate outputs directly into FP32 for better numerical accuracy.
+  - **FP8 Weight Caching**: Avoiding redundant FP8 casting during multiple gradient accumulation steps to improve efficiency.
+- [Introduction to FP8](https://github.com/NVIDIA/TransformerEngine/blob/main/docs/examples/fp8_primer.ipynb)
+  - Overview of FP8 datatypes (E4M3, E5M2), mixed precision training, delayed scaling strategies, and code examples for FP8 configuration and usage.
+- [TE Quickstart](https://github.com/NVIDIA/TransformerEngine/blob/main/docs/examples/quickstart.ipynb)
+  - Introduction to TE, building a Transformer Layer using PyTorch, and instructions on integrating TE modules like Linear and LayerNorm.
+- [Basic MNIST Example](https://github.com/NVIDIA/TransformerEngine/tree/main/examples/pytorch/mnist)
+
+# JAX
+- [Basic Transformer Encoder Example](https://github.com/NVIDIA/TransformerEngine/tree/main/examples/jax/encoder)
+  - Single GPU Training: Demonstrates setting up and training a Transformer model using a single GPU.
+  - Data Parallelism: Scale training across multiple GPUs using data parallelism.
+  - Model Parallelism: Divide a model across multiple GPUs for parallel training.
+  - Multiprocessing with Model Parallelism: Multiprocessing for model parallelism, including multi-node support and hardware affinity setup.
+- [Basic MNIST Example](https://github.com/NVIDIA/TransformerEngine/tree/main/examples/jax/mnist)
+ 
+# PaddlePaddle
+- [Basic MNIST Example](https://github.com/NVIDIA/TransformerEngine/tree/main/examples/paddle/mnist)
+
+# Third party
+- [Hugging Face Accelerate + TE](https://github.com/huggingface/accelerate/tree/main/benchmarks/fp8/transformer_engine)
+  - Scripts for training with Accelerate and TE. Supports single GPU, and multi-GPU via DDP, FSDP, and DeepSpeed ZeRO 1-3.