When I started learning Machine Learning (ML), I thought I was already halfway into understanding how AI reads and understands text. But NO, Machine Learning is the engine, and Deep Learning is the turbo boost 🤯 that makes things like voice assistants, chatbots, and even GPT possible.
Even though my main focus is NLP and LLMs, taking time to understand and practice the building blocks of Machine Learning and Deep Learning has made my NLP learning less abstract.
💡 What is Deep Learning (DL)?
Deep Learning is a type of Machine Learning that uses Artificial Neural Networks to learn from large amounts of data.
These Neural Networks are inspired by how the human brain works, with lots of interconnected neurons passing signals around, but in reality, it’s just clever mathematics and matrices doing the heavy lifting 😎.
Traditional ML can struggle with raw, unstructured data like images, audio, and text. Deep Learning shines here because it can automatically learn features from raw data without you handpicking them.
📌 Why Deep Learning is Key to NLP
Language is messy.
We say I dey go in Pidgin, I am going in English, and many more language translation for same context, that mean the same thing.
Deep Learning models can handle this complexity with ease. They learn patterns, context, and relationships in words far better than traditional ML methods.
📌 Core Building Blocks of Deep Learning
- Neurons: Basic units that receive, process, and pass information.
- Layers: Groups of neurons working together. More layers = deeper learning.
- Weights and Biases: Adjustable numbers that the model learns to get better at predictions.
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Activation Functions: Decide if a neuron should
fire(ReLU, Sigmoid). - Forward Propagation: Sending data forward through the network to get predictions.
- Loss Function: Measures how wrong the model is.
- Backpropagation: The process of adjusting weights to reduce errors.
- Optimizer: The algorithm that tweaks weights efficiently (Adam, SGD).
- Epochs, Batches, Iterations: How you feed and loop through your data.
📌 Deep Learning Architectures in NLP
- RNN (Recurrent Neural Networks): Good for sequences but can forget long-term context.
- LSTM (Long Short-Term Memory): Solves the forgetting problem of RNNs.
- GRU (Gated Recurrent Unit): Similar to LSTM but faster.
- Transformer: The modern king. Powers GPT, BERT, and most state-of-the-art NLP systems.
📌 Where You See Deep Learning in Real Life
- Computer Vision: Facial recognition, medical scans, object detection in self-driving cars.
- Natural Language Processing (NLP): Chatbots, translation, summarization, sentiment analysis.
- Recommendation Systems: Netflix, YouTube, Spotify.
- Speech Recognition: Siri, Alexa, transcription tools.
📌 Tools for Deep Learning
- TensorFlow (with Keras): Powerful but with a steeper learning curve.
- PyTorch: Flexible and beginner-friendly for experimentation.
- Keras: High-level API for quick prototyping.
- Hugging Face Transformers: For pre-trained NLP models like BERT, GPT, RoBERTa.
📌 Why This Matters for NLP
Understanding Deep Learning means I am not just using NLP models but I understand the foundations they are built on. When you know what’s happening under the hood, you can fine-tune, troubleshoot, and even experiment with new architectures.
I will be sharing my journey as I go deeper into NLP and LLMs, but trust me, mastering these basics is like learning your alphabet before writing poetry.
📌 Example: Sentiment Analysis with Deep Learning
Imagine building a system that reads Amazon reviews and predicts if they are positive, neutral, or negative.
With traditional ML, you need to manually extract features like word counts or sentiment scores.
With Deep Learning, you can feed the raw text (after tokenizing) into an LSTM or Transformer, and it learns to spot patterns by itself.
📌 My Learning Path
Here’s how I am approaching Deep Learning as the bridge to NLP:
- Understand Neural Networks: basics of layers, weights, activation functions.
- Practice with simple projects: text classification, sentiment analysis, name entity recognition.
- Explore Transformers: with Hugging Face.
- Integrate into web apps: making my models useful in real life.
Summary
For practice, I built my first Deep Learning project using a dataset on dog breed classification:
🔗 End-to-End Dog Vision with TensorFlow
Next, I will be writing about Natural Language Processing itself because that is where Deep Learning meets the magic of human language 😜.
Happy coding!!!
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