Learning Deep Learning
Learning Deep Learning
I get obsessed with things easily.
Typically, I lose interest just as easily, unless I can map this new found love back to helping me achieve my goals. I have gone into the deep end of deep learning and I am not coming back.
You are going to become obsessed with the view of the future you get with this family of techniques, too.
Deep learning is a subfield of machine learning that utilizes artificial neural networks (ANNs) sometimes called neural nets (NN), which are inspired by the structure and function of the human brain, to learn from data.
These networks consist of interconnected nodes, organized in layers, that process and transmit information. The "deep" in deep learning refers to the use of multiple hidden layers within these networks, allowing them to learn complex patterns and representations from vast amounts of data.
Why Learn Deep Learning?
Being able to predict likely future outcomes, even if the answer is in integers and not visualized in a crystal ball, is a super power.
You can take steps to change those numbers, you can take steps to benefit from those numbers changing, you can win big in business, on Wall St and all points in between. You can model how your current interventions will change how your future interventions will likely need to go in order for you to succeed.
To see the future you need a map of how reality “worked out” in the past, and then extend that map into the future with mathematics.
This is the domain of deep learning.
Deep learning excels in supervised learning, tasks where the goal is to learn a function that maps input data (x) to corresponding output labels (y).
The proliferation of digital devices and the internet has led to an explosion in the amount of data generated, providing ample fuel for training deep learning models, which thrive on large datasets. Innovations in deep learning algorithms have significantly improved training speed and efficiency. The availability of powerful hardware like GPUs and specialized hardware like TPUs (Tensor Processing Units), combined with advancements in parallel computing techniques, has incomprehensibly accelerated the training process of deep learning models.
Deep learning models have demonstrated the capability to achieve and even surpass human-level performance in various tasks, including computer vision and speech recognition.
Deep networks with multiple hidden layers can learn hierarchical representations of data, gradually building up from simple features to complex concepts. This ability to learn complex functions with relatively fewer hidden units compared to shallow networks contributes to their effectiveness.
Deep learning models can analyze structured datasets for applications like real estate price prediction, online advertising, and autonomous driving.
Deep learning has significantly advanced the ability of computers to understand unstructured data like audio and images, opening up new possibilities in fields like speech recognition and computer vision.
Building the Vocabulary
There are some key concepts, techniques and vocabulary that you’ll need to begin to grasp the power of deep learning.
Hyperparameter Tuning
Deep learning models involve numerous hyperparameters that influence their learning process and performance. Finding optimal values for these hyperparameters is crucial for achieving good results. Techniques like grid search and random search are commonly employed for this purpose. These are the control surfaces on a airplane.
Gradient Descent
An iterative optimization algorithm that adjusts the network's parameters to minimize the difference between predicted and actual outputs.
Overfitting
When your model is so specialized on training data it loses general ability to predict the real world — aka test or validation data. That map of how reality worked out is too rigid to match the past vs predict the future.
Regularization
Techniques to help prevent overfitting, improving the model's ability to generalize to unseen data.
Forward Propagation
The process of feeding input data through the network to generate predictions.
Backward Propagation
This step calculates gradients of the loss function with respect to the network's parameters, enabling parameter updates during training.
Batch Normalization
A technique that normalizes the inputs of each layer, stabilizing and accelerating the training process, particularly for deep networks.
Activation Functions
These functions introduce non-linearity into the network, allowing it to learn complex patterns. A widely-used activation function is ReLU (Rectified Linear Unit), which addresses the vanishing gradient problem by mitigating the issue of gradients approaching zero during training.
As you start working with bigger and bigger data the emphasis becomes efficiency. That’s when you’ll start working with vectorization which eliminates the need for explicit for loops, enabling faster processing of large datasets.
Before we add any more complexity to the plot, let’s introduce the players.
Meet the Models
Feedforward Neural Network (FFNN): The Straight Shooter
Imagine a one-way street where information only flows forward. That's our trusty FFNN. It's the most basic type of neural network, with data moving from input to output through layers of connected nodes (neurons).
Each connection has a weight, and the network learns by adjusting those weights to make better predictions.
Convolutional Neural Network (CNN): The Image Expert
If FFNNs are one-way streets, CNNs are multi-lane highways built for image processing. They use special filters that slide over an image, extracting features like edges and textures.
This helps them learn patterns and recognize objects, making them the backbone of image recognition and computer vision applications.
Recurrent Neural Network (RNN): The Memory Keeper
RNNs are like storytellers, remembering what happened before to understand what's happening now. They have loops that allow information to persist, making them ideal for processing sequences of data like text or music.
However, they can have trouble remembering things that happened long ago.
Long Short-Term Memory (LSTM): The Enhanced Memory Keeper
Think of LSTMs as RNNs with a better memory. They have special gates that control what information to keep and what to forget, allowing them to remember important details even over long sequences.
This makes them great for tasks like language translation and speech recognition.
Generative Adversarial Network (GAN): The Creative Duo
GANs are like two neural networks competing in a game. One network, the generator, tries to create realistic data (like images or text). The other, the discriminator, tries to tell if the data is real or fake.
As they compete, the generator gets better at creating realistic data, and the discriminator gets better at spotting fakes. This has led to amazing applications like generating realistic images of people or creating deepfakes.
Ok now you’re armed with the language of deep learning, a basic understanding of the model architecture and some insight into how these machines learn.
Now let’s talk about the process of seeing the future.
Using Deep Learning for Pattern Recognition
Patterns are life playing out.
When you can recognize patterns you can model their sequences and piece together an understanding of likely future patterns.
That’s sometimes called the future.
Yes, combining pattern recognition with sequence modeling can give you the ability to predict likely future outcomes.
Pattern recognition involves identifying recurring structures, trends, or relationships within data. Think of it as recognizing the shapes and patterns in a puzzle.
Sequence modeling focuses on understanding the order and dependencies within data over time or in a specific arrangement. Imagine observing how the puzzle pieces fit together to form a larger picture. When you combine these two:
Pattern Recognition helps you identify the building blocks of potential future outcomes. For example, in a financial time series, pattern recognition might reveal recurring market cycles or price patterns.
Sequence Modeling then helps you understand how these building blocks typically evolve over time. In the financial example, sequence modeling might show how those market cycles or price patterns tend to unfold in a specific order.
By combining the insights from both, you can make informed predictions about what might happen next in the sequence.
You know.. see the future.
Meteorologists use pattern recognition to identify weather patterns and sequence modeling to predict how they'll move and change over time. Financial analysts use pattern recognition to identify market trends and sequence modeling to forecast future stock prices. LLMs use pattern recognition to understand the structure of language and sequence modeling to generate text that makes sense in context. This layering of predictive analysis atop descriptive analysis creates powerful capabilities.
These are ideal candidates for machine learning, especially deep learning, given the inherent complexity and complication of these systems and tasks.
Deep learning uses artificial neural networks to learn complex functions from input data, enabling it to recognize patterns. These networks get deeper as they add multiple hidden layers, allowing them to progressively learn more complex features. For instance, in image recognition, earlier layers might detect simple features like edges, while deeper layers combine these edges to identify more complex patterns like shapes, eventually recognizing specific objects.
Here's a breakdown of the process:
Data Input and Representation → The input data, such as images or text, is fed into the neural network. Each data point is represented numerically, for instance, images are broken down into pixel values, and text can be represented using techniques like word embeddings.
Feature Learning through Layers → The neural network consists of interconnected layers of nodes. Each layer learns to detect specific features or patterns in the input data. The initial layers might learn simple features, and subsequent layers learn more complex features by combining the outputs from previous layers.
Training with Backpropagation → During training, the network learns to map input data to desired outputs, like classifying images or translating languages. The network's predictions are compared to the actual target values, and the error is backpropagated through the network, adjusting the weights of the connections between nodes to minimize future errors. This iterative process allows the network to learn the underlying patterns in the data.
The performance of deep learning models is influenced by factors like network architecture, training data, and hyperparameters.
Researchers continue to develop new and improved deep learning techniques and architectures, further advancing the field of pattern recognition.
ML in general is a rapidly evolving surface area of new architectures, techniques, patterns, schema and language. Take for instance the rising use of Ensemble models. I use these in my applied studies now.
Rise of the Ensemble
Ensemble models are a powerful machine learning technique where multiple individual models (often referred to as base learners or weak learners) are combined to create a more robust and accurate predictive model. Instead of relying on a single model's prediction, ensemble methods aggregate the predictions of various models to arrive at a final decision. Think of it as a team of experts collaborating to solve a complex problem, where each expert brings unique skills and perspectives.
Why Are Ensemble Models Used?
Improved Accuracy: Ensemble models often outperform individual models in terms of accuracy. This is because they reduce the risk of overfitting (where a model performs well on training data but poorly on new data) and leverage the strengths of different models.
Reduced Variance: Each individual model has its own biases and limitations. By combining predictions from different models, ensemble methods can reduce the overall variance of the model, making it less sensitive to fluctuations in the data.
Robustness: Ensemble models are more robust to outliers and noise in the data. Even if one model makes a mistake, other models can compensate for it, leading to a more reliable prediction.
Most importantly, ensembles can generalize better to unseen data by combining different learning algorithms or models trained on different subsets of the data. This helps the model capture a wider range of patterns and relationships in the data.
Let’s walk through the most popular ensemble techniques:
Bagging (Bootstrap Aggregating)
In bagging, multiple models of the same type (e.g., decision trees) are trained on different random subsets of the training data. The final prediction is made by averaging the predictions of all the individual models. This helps reduce variance and overfitting. A popular example is the Random Forest algorithm, where an ensemble of decision trees is used for classification or regression tasks.
Boosting
Boosting is an iterative process where each new model focuses on correcting the errors made by the previous models. This is done by giving more weight to misclassified samples in subsequent iterations. Popular boosting algorithms include AdaBoost, Gradient Boosting, and XGBoost. Boosting can significantly improve the accuracy of models but may be prone to overfitting if not carefully tuned.
Stacking (Stacked Generalization)
Stacking combines the predictions of multiple base models (of different types) using a meta-model. The base models are trained on the original data, and their predictions are then used as input features for the meta-model. The meta-model learns how to best combine the predictions of the base models to make the final prediction. This method can often achieve higher accuracy than individual models or simple averaging.
Voting
Voting is a simple ensemble method where the final prediction is determined by majority vote (for classification) or averaging (for regression) of the predictions of different models. This approach is straightforward to implement and can be effective when the individual models are diverse and have reasonable accuracy.
Ensemble models are particularly useful when:
You have a large and diverse dataset.
You need a model with high accuracy and robustness.
You want to reduce overfitting and improve generalization.
If this sounds like an obvious choice, remember that in engineering there are always tradeoffs — here’s what we trade when we leverage ensemble:
Complexity: Ensemble models can be more complex to build and train than individual models.
Interpretability: The increased complexity can make it harder to interpret the results of ensemble models.
Computational Cost: Training and deploying multiple models can be computationally expensive.
Despite these considerations, the benefits of ensemble models in terms of accuracy, robustness, and generalization often outweigh the drawbacks, making them a popular choice for many machine learning applications.
I am currently ensembling in this ML competition:
Learning Plan for Deep Learning
Over the coming months I am going to publish a series of posts to help us all level-up in our machine learning capabilities.
Going to focus on real-world tactical applications, but they will need to be simplified to ensure we connect with a broad audience.
The first step is to build a solid foundation.
I am not the greatest at math but we’ll work together to understand the mathematical underpinnings of deep learning, mastering linear algebra and calculus. These tools will help you understand how neural networks, the building blocks of deep learning, operate. You'll then explore probability and statistics, crucial for comprehending how models make predictions. Along the way, you'll hone your Python programming skills, the language of choice for deep learning practitioners. Finally, you'll grasp the fundamentals of machine learning, the broader field from which deep learning emerged.
With the foundation laid, you'll dive into the world of neural networks. You'll start with artificial neural networks, learning how they mimic the human brain's structure and function. You'll discover how convolutional neural networks have revolutionized image processing and computer vision. Recurrent neural networks will unveil their prowess in natural language processing and time series analysis. You'll encounter autoencoders, adept at dimensionality reduction and anomaly detection, and generative adversarial networks, capable of producing stunning images and videos.
Finally, we’ll explore the wild world of attention mechanisms, the driving force behind state-of-the-art natural language understanding models like Transformers.
As your knowledge grows, you'll transition to practical implementation. You'll learn to wield powerful deep learning frameworks like PyTorch, TensorFlow, and Keras, empowering you to build, train, and deploy your models. Eventually we will learn the art of hyperparameter optimization, fine-tuning our models for peak performance. Transfer learning, the practice of leveraging pre-trained models, will become a valuable tool in your arsenal too.
Remember, the field of deep learning is ever-evolving. Continuous learning is not just a recommendation — it's a necessity.
Engage with online communities, attend conferences, and never stop experimenting. With dedication, perseverance, and a thirst for knowledge, you'll unlock pattern recognition, sequence modeling and so much more.
Super powers are one training run away.
👋 Thank you for reading Life in the Singularity.
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