TensorFlow: The Hidden Gem of Data Science.

Author(s): Stephen Chege-Tierra Insights

Originally published on Towards AI.

I have been reading a lot of publications regarding data science for the past 3 years or so, and I honestly think that tensor flow is not getting the attention it deserves. This could be my own biased opinion, but try and look up or read on tensorflow and any other library like pandas, then compare and contrast the results. You will find more content on pandas than tensor flow for machine learning.

Yes! They have different tasks, but I think tensor flow should receive equal, if not more, hype than Pandas, Matplotlib, and other Python libraries for its capabilities; while Pandas undeniably plays a crucial role in data preprocessing and exploratory data analysis, TensorFlow empowers data scientists to take their analyses to the next level by impeccably transitioning from data manipulation to model development and deployment. With TensorFlow, users can not only clean and preprocess data but also build, train, and evaluate sophisticated machine learning models, all within a unified framework.

Uncovering Tensor Flow

TensorFlow promotes itself as an end-to-end open-source machine learning data framework on its website. This course focuses on using a specific TensorFlow API to create and train machine learning models, even though TensorFlow prides itself on being the most powerful second-to-none system for controlling all parts of a machine learning life cycle.

A team of engineers and researchers from the Google Brain team within the Google AI Company created the library. They desired a library that offers robust assistance for deep learning, machine learning, and sophisticated numerical calculations in several scientific fields.

The first stable version of TensorFlow was published on February 11, 2017, after it was first made available to the general public in 2015. Both its creation and upkeep are attributed to Google. Since then, it has developed into a relatively popular framework for applications involving deep learning and machine learning. It offers a vast library for large-scale numerical computing and machine learning.

Since its first release, TensorFlow has undergone multiple revisions, each bringing new features, improvements, and optimizations. TensorFlow is kept at the forefront of deep learning and machine learning research and development through regular revisions.

Tensorflow code snippets

import tensorflow as tf
mnist = tf.keras.datasets.mnist

(x_train, y_train),(x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0

model = tf.keras.models.Sequential([
 tf.keras.layers.Flatten(input_shape=(28, 28)),
 tf.keras.layers.Dense(128, activation='relu'),
 tf.keras.layers.Dropout(0.2),
 tf.keras.layers.Dense(10, activation='softmax')
])

model.compile(optimizer='adam',
 loss='sparse_categorical_crossentropy',
 metrics=['accuracy'])

model.fit(x_train, y_train, epochs=5)
model.evaluate(x_test, y_test)

import tensorflow as tf
import numpy as np

# Generate random input data
np.random.seed(0)
X = np.random.rand(100, 1)
y = 2 * X + 1 + np.random.randn(100, 1) * 0.1 # Adding some noise

# Define the linear regression model
model = tf.keras.Sequential([
 tf.keras.layers.Dense(units=1, input_shape=[1]) # Single dense layer for linear regression
])

# Compile the model
model.compile(optimizer='sgd', loss='mse') # Using stochastic gradient descent and mean squared error loss

# Train the model
model.fit(X, y, epochs=50, verbose=0) # Training for 50 epochs

# Predict using the trained model
predictions = model.predict(X)

# Print the learned weights (should be close to 2) and bias (should be close to 1)
print("Learned weights:", model.layers[0].get_weights()[0])
print("Learned bias:", model.layers[0].get_weights()[1])

What makes it Effective?

A variety of tasks, including natural language processing, image identification, handwriting recognition, and various computationally-based simulations like partial differential equations, can be modelled using TensorFlow which is very useful for developers.

TensorFlow has several advantages, including production-level scalability, automatic gradient computation, interoperable graph exporting, and the ability to perform low-level operations across multiple acceleration platforms. It’s always simple to write code easily because TensorFlow offers eager execution as an alternative to the dataflow paradigm and Keras as a high-level API.

A huge advantage of TensorFlow is that it can be used to deploy machine learning models on embedded and mobile devices. Production can immediately use pre-trained models. Also, TensorFlow supports various platforms, including CPUs, GPUs, and Google’s custom-built Tensor Processing Units (TPUs). This allows users to leverage hardware acceleration for faster training and inference.

Python, NumPy, SciPy, and other widely used frameworks and technologies are just a few of the many that TensorFlow easily networks with. Data preprocessing, model evaluation, and integration with current software systems are made easier by this compatibility.

It is worth noting the dedicated community- researchers and developers who help Tensorflow through code, documentation, tutorials, articles and support forums such as Stackoverflow, GitHub and Google developers. However, I reckon that it still lags behind other libraries when it comes to a vibrant online presence, having a core base of developers and researchers is essential for a software library.

Where Is It Being Applied?

Airbnb, Google, Intel, Instagram, Palantir, Coca-Cola, GE Healthcare, and Twitter are some of the top companies using TensorFlow. As it is an open-source library and developed by Google, it has wide acceptance across companies. NVIDIA and Intel have collaborated with the TensorFlow community to optimize models for their respective processors through TensorRT and OpenVINO Toolkit.

According to Forbes, For Google, TensorFlow is an important project. Millions of dollars have been spent on research and development to enhance machine learning and include such features in TensorFlow. TensorFlow is used by Google in a number of its services and products, such as Android, Nest, Google Assistant, and others. One of the main features that sets the Google Cloud AI platform apart is TensorFlow, which provides end-to-end capabilities in the form of managed ML PaaS and cognitive services.

Why is it Being Shunned By Developers?

Despite its incredible power, TensorFlow is sometimes overlooked or undervalued in the context of deep learning and machine learning frameworks. Its apparent complexity, its primary link with deep learning tasks, competition from other frameworks, and possible performance difficulties could all contribute to this underestimation. TensorFlow’s marketing and publicity efforts might also fall short of those of its rivals, which would limit the public’s knowledge of its advantages and success stories, this is upon Google to push it out there.

Additionally, some users could favor competing libraries’ higher-level APIs for quick prototyping, unintentionally ignoring TensorFlow’s advantages in terms of scalability, versatility, and support for distributed computing. Many beginners can find it intimidating and complex if they are new to programming; the learning curve may discourage some users from exploring its full potential, leading to a lack of appreciation for its capabilities.

But it’s crucial to acknowledge that TensorFlow is still a strong and adaptable technology with a thriving community and structure and that it deserves more credit and recognition for what it has contributed to the data science community.

To conclude, I see Tensor flow gaining more popularity with the AI spring in full force and demand for AI models chatbots like Bard and chatgpt. TF’s strong features and adaptableness put it in a position to lead artificial intelligence innovation, enabling researchers and developers to build ground-breaking products that improve our everyday lives and revolutionize how we engage with technology.

As humans continue to push the boundaries of what AI can attain, TensorFlow will stand ready to play a pivotal role in shaping the future of technology and society only if it keeps up with the changing times and withstands competition.

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Published via Towards AI