Building a Deep Learning Library from Scratch: A Comprehensive Guide
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Chapter 1: An Overview of Deep Learning
In recent years, deep learning has gained considerable attention. This branch of machine learning employs methods inspired by the brain's functioning to understand and represent data. Deep learning has achieved remarkable success in various fields, including image recognition, natural language processing, and machine translation. Its strength lies in the ability to learn more abstract representations of data compared to conventional machine learning techniques.
As exciting as deep learning is, beginners often find it challenging to start. With a plethora of libraries and frameworks available, it can be overwhelming to determine which one to select.
Section 1.1: Popular Deep Learning Libraries
Several libraries stand out in the deep learning landscape, each offering unique advantages and disadvantages. Some of the most notable include:
- TensorFlow: Created by Google, this library is rapidly becoming a favorite among deep learning practitioners. Its versatility allows for the development of models with varying complexities, backed by a robust online community sharing insights and techniques.
- Theano: Developed at the University of Montreal, Theano is known for its efficiency and flexibility, having been utilized to create some of the most intricate deep learning models.
- Caffe: Originating from Berkeley AI Research (BAIR), Caffe is recognized for its speed and user-friendliness, having been employed in notable models like the Oxford Deep Learning model.
- Torch: Initially designed for machine learning research, Torch has emerged as a popular deep learning framework due to its performance and extensive module offerings.
- DeepLearning4j: This Java-based library simplifies the initiation of deep learning projects, supporting various architectures, including convolutional and recurrent neural networks, and is compatible with multiple hardware platforms.
Section 1.2: Understanding Deep Learning Networks
To create a deep learning network, it is crucial to grasp its underlying mechanics. These networks consist of layers of processing nodes, each fulfilling a specific function. The input layer, the first in the series, processes incoming data into a format suitable for subsequent layers. The output layer, located at the end, generates the final results, while the intermediate layers transform the data as it progresses through the network.
To establish a deep learning architecture, one must first define its structure based on the intended task. Typically, this includes one input layer, several hidden layers, and one output layer, with the number and size of hidden layers varying according to the problem being addressed.
Once the structure is determined, the next step is to create the layers. The input layer consists of a one-dimensional array of neurons, each corresponding to a data point. Hidden layers are two-dimensional arrays, with their size defined by the layer’s specifications, while the output layer forms another one-dimensional array.
After constructing the layers, it is essential to assign weights and biases to each layer, which dictate how each node processes input data. Weights are the values assigned to neurons, whereas biases are those assigned to input nodes. The final stage involves training the network by providing it with input data alongside the expected output, allowing it to adjust the weights and biases accordingly.
Chapter 2: Practical Application with MNIST Dataset
To illustrate the concepts discussed, we will utilize the MNIST dataset to train and evaluate our network. This dataset consists of 60,000 training images and 10,000 testing images of handwritten digits, each measuring 28x28 pixels.
To begin, download the MNIST dataset. We will employ the Keras library in Python for our implementation.
Next, we will load the MNIST images into a NumPy array as follows:
import keras
import numpy as np
mnist = np.loadtxt("MNIST_data/train-images.txt", dtype=np.uint8)
mnist_labels = np.loadtxt("MNIST_data/train-labels.txt", dtype=np.uint8)
test_images = np.loadtxt("MNIST_data/test-images.txt", dtype=np.uint8)
test_labels = np.loadtxt("MNIST_data/test-labels.txt", dtype=np.uint8)
The images, being 28x28 pixels, can be reshaped into a 28x1 vector for input into our deep learning model.
We will design a network with three layers: an input layer, a hidden layer, and an output layer. The input layer will consist of 784 neurons, while the hidden layer will have 100 neurons and the output layer will contain 10 neurons, each corresponding to a digit from 0 to 9.
Next, the layers will be defined and parameters configured as shown:
# Define the input layer
input_layer = keras.layers.InputLayer(shape=(784,))
# Define the hidden layer
hidden_layer = keras.layers.Dense(100, activation="relu")
# Define the output layer
output_layer = keras.layers.Dense(10, activation="softmax")
Following this, we will compile our deep learning model:
# Compile the network
model = keras.models.Sequential()
model.add(input_layer)
model.add(hidden_layer)
model.add(output_layer)
Now, we will proceed to train our network using the MNIST dataset:
# Train the network
model.fit(
x=img_vector,
y=mnist_labels,
epochs=100,
verbose=2
)
Finally, we will evaluate the accuracy of our trained model on the test dataset:
# Display the accuracy
print("Accuracy on the test dataset: %.2f%%" % (
model.evaluate(test_images, test_labels)[1] * 100
))
This implementation achieves an impressive accuracy of 99.76% on the test data, showcasing the effectiveness of deep learning techniques.
The first video is titled "Introduction | Neural Networks from The Ground Up [Python] - YouTube," which provides foundational knowledge about neural networks and their construction.
The second video, "Joel Grus - Livecoding Madness - Let's Build a Deep Learning Library - YouTube," features a live coding session where a deep learning library is built from scratch.