Top 5 popular AI tools i.e TensorFlow, PyTorch, GPT-3, Keras

Top 5 popular AI tools i.e TensorFlow, PyTorch, GPT-3, Keras

TensorFlow

TensorFlow is an open-source software library for dataflow and differentiable programming across a range of tasks. It is a symbolic math library, and is also used for machine learning applications such as neural networks. It was developed by the Google Brain team and is used in many of Google's products and services.

Example of TensorFlow

Here is an example of a simple neural network in TensorFlow for the MNIST dataset classification:

import tensorflow as tf

# Define input data

X = tf.constant([[1], [2], [3], [4]], dtype=tf.float32)

y = tf.constant([[2], [4], [6], [8]], dtype=tf.float32)

# Define the model

model = tf.keras.Sequential()

model.add(tf.keras.layers.Dense(units=1, input_shape=[1]))

# Compile the model

model.compile(optimizer=tf.keras.optimizers.SGD(learning_rate=0.01),

              loss='mean_squared_error')

# Train the model

model.fit(X, y, epochs=100)

# Evaluate the model

print(model.predict([[5]]))

PyTorch

PyTorch is an open-source machine learning library based on the Torch library. It is primarily used for natural language processing but also has popular applications in computer vision and generative models. PyTorch provides two high-level features: tensor computation with strong GPU acceleration and deep neural networks built on a tape-based autograd system. It was developed by Facebook's AI Research lab.

Example of PyTorch

Here is an example of a simple neural network in PyTorch for the MNIST dataset classification:

import torch

import torch.nn as nn

import torch.optim as optim

# Define input data

X = torch.tensor([[1], [2], [3], [4]], dtype=torch.float32)

y = torch.tensor([[2], [4], [6], [8]], dtype=torch.float32)

# Define the model

model = nn.Linear(in_features=1, out_features=1)

# Define loss function and optimizer

criterion = nn.MSELoss()

optimizer = optim.SGD(model.parameters(), lr=0.01)

# Train the model

for epoch in range(100):

    # Forward pass

    y_pred = model(X)

    loss = criterion(y_pred, y)

   

    # Backward pass and optimization

    optimizer.zero_grad()

    loss.backward()

    optimizer.step()

# Evaluate the model

print(model(torch.tensor([[5]], dtype=torch.float32)))

What is Keras with exmaple

Keras is a high-level neural network API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano. It was developed to make building deep learning models as fast and easy as possible.

Example:

Here is a simple example of building a multi-layer perceptron (MLP) for binary classification using Keras:

 from keras.models import Sequential

from keras.layers import Dense

model = Sequential()

model.add(Dense(32, activation='relu', input_dim=100))

model.add(Dense(1, activation='sigmoid'))

model.compile(optimizer='rmsprop',

              loss='binary_crossentropy',

              metrics=['accuracy'])

from keras.models import Sequential

from keras.layers import Dense

model = Sequential()

model.add(Dense(32, activation='relu', input_dim=100))

model.add(Dense(1, activation='sigmoid'))

model.compile(optimizer='rmsprop',

              loss='binary_crossentropy',

              metrics=['accuracy'])

GPT-3

GPT-3 (Generative Pretrained Transformer-3) is an advanced language generation model developed by OpenAI. It is one of the largest language models ever built, with 175 billion parameters. It can perform a wide range of language tasks, including text generation, question-answering, language translation, and more. The model is trained on a massive amount of text data and can generate human-like text outputs. GPT-3 has received significant attention in the research community and industry due to its impressive language generation capabilities and its potential to be used for various NLP applications.

Example of GPT-3

Input: The weather today is expected to be

Output (generated by GPT-3): sunny and warm with a high of 80 degrees.

Another example is using GPT-3 for question answering. Here's an example of asking a question and getting an answer from GPT-3:

Input: Who is the CEO of Tesla?

Output (generated by GPT-3): Elon Musk is the CEO of Tesla.