#### Lukasz Kaisar (Google Brain Research Scientist) – “Deep Learning (Aug 2018)

#### Chapters

#### Abstract

Deep Learning and Its Impact on Natural Language Processing: An In-Depth Analysis

Lukasz Kaiser, a PhD from the Technical University of Aachen, has made significant contributions to the field of deep learning. His expertise in monadic second-order logic, game quantifiers, and cardinality quantifiers laid the foundation for his research at Google, where he worked on projects like the Tensor2Tensor library and joined the brain team in Mountain View.

Lukasz Kaiser’s Insights on Deep Learning and Neural Networks

Lukasz Kaiser’s insights from his PhD and current research provide a foundation for understanding the theoretical underpinnings of deep learning. While deep learning’s popularity stems from accessible engineering tools, theoretical knowledge enhances its understanding. In introductory “Hello World” models, simple tasks like classifying digits or basic images are utilized.

The remarkable success of deep learning in complex tasks like speech recognition, language translation, image recognition, and facial recognition has propelled its popularity. Its universal applicability to diverse tasks makes it a powerful tool.

The computational intensity of deep learning models requires significant processing power for training. Lukas Kaiser emphasizes the computational capabilities of GPUs, highlighting the advancements from the Kepler and Pascal generations to the current Volta. The increased computational power is comparable to supercomputers, with the latest GPU clusters matching the processing speed of the world’s fastest supercomputer.

The deep learning community embraces an open culture where research and code are extensively shared. This open sharing fosters collaboration, attracts talented individuals, and accelerates innovation. Companies and researchers recognize that knowledge sharing is crucial for the growth of the field, leading to a thriving ecosystem of published papers and publicly available code.

Revolutionizing NLP with Deep Learning

In his PhD Open Programme lecture, Kaiser focused on making deep learning accessible and understandable. His lecture covered the basics of deep learning, particularly emphasizing image classification and sequence models. The distinction between deterministic and probabilistic models in sequence models was a highlight, reflecting Kaiser’s recent work in this area. Furthermore, the course aimed to provide practical skills in deep learning, offering exercises, cloud credits, and the use of Colab for hands-on experience. Kaiser’s interactive approach encouraged participants to engage and customize their learning experience to their interests.

Deep learning has transformed natural language processing (NLP), unifying tasks like speech recognition, machine translation, and image recognition under a single methodological framework. This unification departs from the traditional NLP pipelines that were often rule-based and labor-intensive. Neural networks, capable of learning end-to-end without explicit linguistic programming, have proven effective in various NLP tasks. Notably, neural networks excel in machine translation, parsing, and language modeling, often surpassing traditional methods.

Moreover, deep learning models excel in text generation, capturing long-range dependencies and producing fluent, coherent sentences. Intriguingly, they can comprehend language nuances and demonstrate remarkable proficiency in generating fluent sentences. In machine translation, deep learning models outshine traditional phrase-based systems, achieving human-level performance as measured by the BLEU score. Human evaluators perceive the quality of neural network translations as significantly improved, indicating their ability to generate more natural and accurate translations.__Epochs__: In machine learning, an epoch is one complete pass through the training dataset. The optimal number of epochs is determined based on the evaluation set performance. Training continues until performance improves or time constraints are met. Overfitting can occur before reaching the optimal number of epochs.

Introduction to Tensor Data and Image Processing

The `tensor to tensor` library provides access to various datasets, including the MNIST dataset of handwritten digits. MNIST data consists of 60,000 training and 10,000 dev examples.

MNIST images are initially in a single height by width format with one channel (black and white). To obtain tensors in the desired batch height by channels format, the data is reshaped using `problem.data_set` and iterated over using a queue. The resulting tensors include the image as input and the corresponding label as the target. Matplotlib (`PLT`) is used to display the images for verification.

Batching is essential for machine learning, where gradients are updated for a group of examples. Tensors are grouped into batches using the `batch` method, following the convention of batch height by channels. Repeat functionality ensures that the queue will restart from the beginning after reaching the end of the dataset.

Data dictionaries vary across different datasets. For MNIST, the dictionary contains inputs (images) and targets (labels). A fully connected network is constructed, where the input image is reshaped to a batch size by 28 by 28 tensor, corresponding to the width and height of MNIST images.__Static vs. Dynamic Graphs__: TensorFlow uses static graphs for learning, while competitors use dynamic graphs. Static graphs allow for compilation and optimization, making training more efficient. Dynamic graphs are more natural and easier to write but harder to compile.__Comparison of Keras and tf.layers__: Keras offers a concise way to write models but involves more initialization steps. tf.layers is a wrapper for Keras and calls the same underlying functions. Both approaches can achieve similar code brevity.

The Building Blocks of Neural Networks in Language Processing

Neural networks are the core of deep learning models, capable of capturing complex relationships in data. In language processing, these models excel at understanding the long-term relationships between words and phrases, thereby effectively grasping syntactic and semantic structures.

Neurons, the fundamental units of neural networks, receive input signals, multiply them by weights, sum the products, and apply an activation function (typically ReLU) to generate an output signal. Layers of neurons can be fully connected or organized in a convolutional manner, where local connections are shared across the layer. This layering allows for the extraction of complex features from the input data.

Deep learning models often process data in the form of tensors, multidimensional arrays with dimensions representing batch size, height and width (for images), and the number of channels (representing features or values at each location). Operations in deep learning, including matrix multiplications, convolutions, and pointwise functions, are applied to these tensors, enabling efficient processing of complex data.

TensorFlow, a powerful software package for deep learning, simplifies building and training deep learning models. TensorFlow automatically computes gradients, handles trainable parameters, and enables efficient parallel computation. It is designed for large-scale distributed hardware, allowing for efficient execution on specialized hardware without an operating system. TensorFlow’s data flow graphs, variables, and efficient execution make it a widely adopted tool for deep learning research and development.__Trade-offs in Graph Optimization__: Pure execution style is simpler but less efficient. Building a programming thing allows for optimization but may require rebuilding the graph for changes.

Walkthrough of the MNIST Neural Network Model

The MNIST images are reshaped into a single vector of RGB values. The labels are converted to a one-channel dimension and squeezed to remove any unnecessary dimensions.

The model consists of two hidden layers, both using ReLU activation functions. The first hidden layer has 768 neurons, and the second has 128 neurons. The output layer has 10 neurons, corresponding to the 10 possible digits.

The loss function used is sparse softmax cross-entropy with logits. Accuracy is calculated by comparing the predicted digit (argmax of the output probabilities) to the actual digit in the label.

The model is trained using the Adam optimizer, which adjusts the gradients during the training process. The train operation computes the gradient of the loss function and updates the trainable parameters accordingly. The training loop runs for a specified number of steps, printing the loss and accuracy every 100 steps.

The model starts with an accuracy of about 9%, which is expected for a random classifier. After 100 steps, the accuracy increases to 75%. Within a short time, the model reaches an accuracy of around 90%.__Convergence of Approaches__: Both static and dynamic approaches are widely recognized and supported by frameworks. Torch and TensorFlow now offer both static and dynamic modes.

Training and Evaluating a Digit Recognition Model in TensorFlow

The model achieves high accuracy on the training set, suggesting that it has learned to recognize digits effectively. To assess the model’s generalization capabilities, it is essential to evaluate its performance on a separate evaluation set.

TensorFlow operations accumulate in the default graph, so resetting the graph and session prevents conflicts when building new models. The scope argument allows for the reuse of variables in different models, such as training and evaluation models.

The combined cell includes code for data retrieval, model training, and evaluation, providing a comprehensive workflow. Training and evaluation accuracy metrics are displayed, allowing for easy monitoring of the model’s performance. In larger models, the training accuracy may reach 100%, while the evaluation accuracy decreases, indicating overfitting.__Challenges and Advancements in Machine Learning__: The article addresses challenges in machine learning, such as overfitting and the importance of evaluation accuracy. It explores the concept of epochs in machine learning and compares TensorFlow and Keras, highlighting the differences between static and dynamic graphs. Additionally, TensorFlow’s graph compilation, including optimizations for performance enhancement and memory management, is investigated.

Conclusion

The article underscores the significance of deep learning in transforming NLP and the role of tools like TensorFlow in advancing this field. Lukasz Kaiser’s insights, coupled with the technical exploration of neural networks and TensorFlow, offer a comprehensive understanding of the current state and future prospects of deep learning in natural language processing.

Notes by: ZeusZettabyte