Deep Learning for NLP - Part 1

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🧭 Deep Learning for NLP - Part 1: Embark on a Journey Through Language with AI

🚀 Course Overview: Welcome to the first part of our comprehensive journey into the world of Natural Language Processing (NLP) through the lens of deep learning. In this course, we'll lay down the fundamental building blocks that will enable you to construct complex NLP models. Join Manish Gupta as he meticulously explains multi-layered perceptrons, word embeddings, and recurrent neural networks - the core components of deep learning in NLP.

🎓 Key Takeaways:

• Grasp the essence of artificial neural networks, including activation functions and back-propagation.
• Understand the critical role of regularization, early stopping, and dropouts to prevent overfitting.
• Explore a variety of word embedding techniques: from basic onehot encoding and Singular Value Decomposition (SVD) to sophisticated methods like word2vec, GloVe, Cross-lingual embeddings, and subword tokenization (BPE, WordPiece, SentencePiece).
• Dive deep into ngram models, the neural network language model (NNLM), and the intricacies of RNNs.
• Master the advanced topics of LSTMs, GRUs, and tackle challenges like vanishing and exploding gradients.

🔍 Detailed Course Structure:

Section 1: Foundations of Artificial Neural Networks

• Artificial Neural Network Basics: Learn about activation functions, their types (ramp, step, sigmoid, tanh, relu, leaky relu), and integration functions.
• Deep Learning Explained: Discover the interconnection between deep learning, machine learning, and artificial intelligence, and understand how neural networks work.
• Avoiding Overfitting: Gain insights into techniques like regularization, early stopping, and dropouts that help in preventing overfittting in neural network training.

Section 2: Word Embeddings and Advanced Tokenization Techniques

• Word Embedding Methods: Begin with Onehot encoding and SVD, and move on to master the CBOW and Skipgram methods of word2vec.
• Efficient Softmax Computation: Learn multiple methods to make softmax computation efficient for large datasets.
• GloVe and Beyond: Understand the GloVe model and its significance in NLP tasks.
• Cross-Lingual Embeddings: Explore how word embeddings can transcend language barriers.
• Subword Tokenization: Dive into BPE, WordPiece, and SentencePiece for advanced Transformer-based models.

Section 3: Recurrent Neural Networks (RNN) and Advanced NLP Models

• ngram Models: Get acquainted with the fundamental building blocks of language modeling.
• Neural Network Language Model (NNLM): Understand how neural networks can be used to model language.
• Understanding RNNs: Learn the workings of RNNs and their variants, including BiRNNs and Deep BiRNNs.
• Overcoming RNN Challenges: Address the issues of vanishing and exploding gradients in RNN training.
• LSTMs and GRUs: Master the architectures of Long Short-Term Memory (LSTM) networks and Gated Recurrent Units (GRU), and how they can be utilized to handle sequential data in NLP.

Why Take This Course? If you're a data scientist, machine learning engineer, AI researcher, or simply someone fascinated by the power of deep learning in understanding human language, this course is your gateway to mastering NLP with deep learning. By completing this course, you will not only understand the concepts but also be able to apply them effectively to real-world NLP tasks.

Enroll now and set sail on this enlightening journey through the intricate world of Natural Language Processing! 🚀📚✨