ALT Mastering Data Preparation for Transformer Training Embark on a guided journey into the intricate world of data preparation for training Transformers! In this tutorial, we will explore the critical steps required to transform raw text data into a format suitable for training a Transformer model, with a focus on sequence-to-sequence tasks. Table of Content: 🎯 Set the Stage: Introduction to Data Preparation for Transformers. 🔧 Crafting Custom Tokenization: Building a Flexible CustomTokenizer Class. 📚 Creating Language-Specific Tokenizers: Spanish and English Instances. 🔗 Establishing a Data Pipeline: Utilizing the DataProvider Class. Prepare to unlock the power of data manipulation for Transformer training. As we delve into creating custom tokenizers, organizing data pipelines, and streamlining preprocessing steps, you'll gain the tools to set your NLP projects on a path to success. Text Version Tutorial: https://pylessons.com/transformers-nlp-data

ALT Dive into Transformers: Building Blocks in NLP | Encoder and Decoder Layers 🚀 Embark on a transformative journey through the heart of Natural Language Processing (NLP) with Transformers! In this tutorial, we delve into the core elements of the Transformer architecture, focusing on crafting the fundamental Encoder and Decoder layers. 🎯 Grasp the Concept of Encoder and Decoder in Transformers. 🔧 Construct EncoderLayer: GlobalSelfAttention & FeedForward in Action. 🔑 Decode the Magic: Implementing DecoderLayer with CrossAttention. 💻 Test the Layer's Harmony with Realistic Input Sequences. Embark on this empowering voyage into the realm of Transformers, enriching your toolkit with the skills to create intricate Encoder and Decoder layers. Join me for a dynamic learning experience and prepare to wield Transformers for a multitude of captivating NLP challenges! 🌟 Text Version Tutorial: https://pylessons.com/build-transformer

ALT 🚀 Introduction to Transformers Attention Layers in NLP 🚀 Discover the powerful world of Transformers in Natural Language Processing (NLP)! In this tutorial, we'll continue exploring the main components of the Transformer architecture, focusing on the essential Attention layers. 🎯 Understand the main blocks of Transformers. 🎓 Implement Attention layers and Understand its Significance. 💡 Build a Custom "BaseAttention" and other layers in TensorFlow. 💻 Test these Layers with Random Input Sequences. Unleash the potential of Transformers in NLP and level up your machine-learning skills. Subscribe now for more exciting tutorials on building practical Transformer models for real-world NLP tasks! 🌟 Text Version Tutorial: https://pylessons.com/transformer-attention #Transformers #NLP #MachineLearning #BaseAttention #TensorFlow

ALT Introduction to Transformers in NLP 🚀 Discover the powerful world of Transformers in Natural Language Processing (NLP)! In this tutorial, you will explore the fundamental components of the Transformer architecture, with a focus on the essential "PositionalEmbedding" layer. You will: 🎯 Learn the Advantages and Limitations of Transformers; 🎓 Implement Positional Encoding and Understand its Significance; 💡 Build a Custom "PositionalEmbedding" Layer in TensorFlow; 💻 Test the Layer with Random Input Sequences. Unleash the potential of Transformers in NLP and level up your machine-learning skills. Follow now for more exciting tutorials on building practical Transformer models for real-world NLP tasks! Link to the tutorial: https://pylessons.com/transformers-introduction #tensorflow, #NLP, #Transformers, #machinelearning, #deeplearning

ALT Have you ever heard what Generative Adversarial Networks are? I wrote a tutorial where I cover the basics of GANs in TensorFlow, step-by-step, using the MNIST dataset. Following the tutorial, you will: ✔Learn to generate new fake data almost identical to the original data; ✔Get code snippets that show how to load and preprocess the dataset; ✔Learn to implement custom TensorFlow generator and discriminator models used in GANs; ✔Learn to use TensorFlow callbacks to track the training process and actual results. By the end of this tutorial, you will have a better understanding of GANs and be able to apply them to more challenging tasks. So, get ready to dive into the world of GANs and create your own generated images! Link to the tutorial: https://pylessons.com/gan-introduction

ALT Have you ever tried to train your custom OCR system? Don't worry if you haven't. I wrote a step-by-step foundation to gain experience with handwritten text recognition from images! On the link below, you will: ✔ Overview the IAM Dataset and handwritten text recognition; ✔ Download, extract, and preprocess the dataset; ✔ Cover data augmentation techniques to improve model performance; ✔ Dive into PyTorch model training with custom CTC loss function and callbacks; ✔ Cover the evaluation metrics like CER and WER to monitor training progress; ✔ Save and export the trained PyTorch model in ONNX format. If you already have experience in this field, please, share your insights with us! Link to the tutorial: https://pylessons.com/handwriting-recognition-pytorch

ALT New tutorial: Handwritten sentence recognition with TensorFlow. Check it out! https://pylessons.com/handwritten-sentence-recognition