Nothing Special   »   [go: up one dir, main page]
Everand Logo

Welcome to Everand!

  • Discover millions of ebooks, audiobooks, and so much more with a free trial

    Only $11.99/month after trial. Cancel anytime.

    Generative AI with Deep Learning: The Complete Guide to Modern Techniques and Best Practice
    Generative AI with Deep Learning: The Complete Guide to Modern Techniques and Best Practice
    Generative AI with Deep Learning: The Complete Guide to Modern Techniques and Best Practice
    Ebook150 pages1 hour

    Generative AI with Deep Learning: The Complete Guide to Modern Techniques and Best Practice

    By Anand Vemula

    ()

    Read preview

    About this ebook

    This book provides an in-depth exploration of the foundational concepts, advanced techniques, and practical applications of generative AI, all powered by deep learning.

    The journey begins with a solid introduction to generative models, explaining their significance in AI and how they differ from discriminative models. It then covers the foundational elements of deep learning, including neural networks, backpropagation, activation functions, and optimization methods, laying the groundwork for understanding complex generative architectures.

    The book progresses to detailed discussions on various generative models such as Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and Diffusion Models. Each model is presented with its mathematical foundations, architecture, and step-by-step coding tutorials, making it accessible to both beginners and advanced practitioners.

    Real-world applications of these models are explored in depth, showcasing how generative AI is transforming industries like healthcare, finance, and creative arts. The book also addresses the challenges associated with training generative models, offering practical solutions and optimization techniques.

    Ethical considerations are a critical component, with dedicated sections on bias in generative models, deepfakes, and the implications of AI-generated content on intellectual property. The book concludes with a forward-looking discussion on future trends in generative AI, including the integration of AI with quantum computing and its role in promoting sustainability.

    With a balanced mix of theory, hands-on exercises, case studies, and practical examples, this book equips readers with the knowledge and tools to implement generative AI models in real-world scenarios, making it an essential resource for AI enthusiasts, researchers, and professionals.

    LanguageEnglish
    PublisherAnand Vemula
    Release dateAug 20, 2024
    ISBN9798227868251
    Generative AI with Deep Learning: The Complete Guide to Modern Techniques and Best Practice

    Read more from Anand Vemula

    Related to Generative AI with Deep Learning

    Related ebooks

    Technology & Engineering For You

    View More
    View More

    Related podcast episodes

    Related articles

    • Article

      Intelligently Yours Truly

      Mar 25, 2023

      Intelligently Yours Truly
      1 min read

    • Article

      The Secret Of Smart Plugins

      May 18, 2022

      Now well into the 2020s, we’re awash with ways to make release-quality music at home, via user-friendly DAWs, expansive sample libraries and fine-tuning mix plugins. But, despite access to tools that enhance audio and tackle the mixing and mastering

      3 min read

    • Article

      The Secret Of Smart Plugins

      Apr 18, 2023

      The Secret Of Smart Plugins
      3 min read

    • Article

      A.i. Coding

      Aug 22, 2023

      A.i. Coding
      16 min read

    • Article

      ChatGPT Is Already Obsolete

      May 19, 2023

      ChatGPT Is Already Obsolete
      5 min read

    • Article

      Deep Fakes

      Jan 7, 2019

      Deep Fakes
      2 min read

    • Article

      Bots And Robbers What Is AI, And Will It Make Us All Redundant?

      Nov 3, 2023

      What is artificial intelligence? The term was coined in 1955 by a team including Harvard computer scientist Marvin Minsky. With no strict definition of the phrase, almost anything more complex than a calculator has been called artificial intelligence

      3 min read

    • Article

      Tales For Makers

      Oct 3, 2022

      Tales For Makers
      4 min read

    • Article

      Tech the Halls

      Jun 2, 2023

      Tech the Halls
      5 min read

    • Article

      The Best Of Emerging Tech

      May 2, 2022

      The Best Of Emerging Tech
      3 min read

    • Article

      How Does Generative AI Affect Photo Imaging?

      May 30, 2024

      Expect to hear a lot more about generative AI this year, not only in association with computer-generated texts and translations, but also for images, voice recordings and even video clips. AI-based tools are increasingly relevant for photo-imaging. S

      6 min read

    • Article

      Decoding The Impact Of AI

      May 5, 2023

      Decoding The Impact Of AI
      6 min read

    • Article

      Getting The edge

      Feb 25, 2021

      Getting The edge
      7 min read

    • Article

      Forward Thinking

      Feb 4, 2022

      Forward Thinking
      8 min read

    • Article

      Nvidia Is Building A Giant Virtual ‘Metaverse’ Of The World, With ‘Digital Twins’ Of Cars, Cities, And People

      Apr 21, 2021

      Jensen Huang, Nvidia’s chief executive, says the company’s next step is creating a ‘metaverse’, artificially created environments where companies can simulate the future before acting on it. Mr Huang said the company wanted to “create the future” by

      4 min read

    • Article

      AI And Design: Questions Of Ethics

      Mar 4, 2024

      Artificial intelligence (AI) is a very old idea, but the term AI and the field of AI as it relates to modern programmable digital computing have taken their contemporary forms in the past 70 years.1Today, we interact with AI technologies constantly,

      5 min read

    • Article

      India Needs To Invest In AI Now

      Jan 6, 2024

      A year ago, Artificial Intelligence (AI) was a deeply technical subject discussed in computer science and technology circles. ChatGPT was released in November 2022, sparking a change in the world as we know it. Suddenly, everyone from a fifth grader

      5 min read

    Related categories

    Reviews for Generative AI with Deep Learning

    0 ratings

    0 ratings0 reviews

    What did you think?

    Tap to rate

    Review must be at least 10 words

      Book preview

      Generative AI with Deep Learning - Anand Vemula

      1. Introduction to Generative AI

      Generative AI represents a subset of artificial intelligence focused on creating new content, be it images, text, music, or even data structures, that mimics the style or characteristics of the input data. Unlike traditional AI, which might classify or predict based on existing patterns, generative AI models learn these patterns and then generate new instances that reflect those learned features.

      1.1 What is Generative AI?

      Generative AI involves using models to generate new data similar to the input data they were trained on. These models are designed to understand the underlying distribution of data and can generate novel outputs that are indistinguishable from the real data. A simple example is a text-generating model trained on Shakespeare’s plays, which can then generate new, Shakespeare-like text.

      Example: Consider a neural network trained on thousands of paintings from the Renaissance period. After training, the model can generate new paintings that resemble the works of masters like Leonardo da Vinci or Michelangelo, even though the generated paintings are entirely new and original.

      Code Example: Here’s a Python code snippet using a simple Variational Autoencoder (VAE) to generate new handwritten digits after training on the MNIST dataset:

      python

      Copy code

      import tensorflow as tf

      from tensorflow.keras.layers import Dense, Flatten, Reshape, Input, Lambda

      from tensorflow.keras.models import Model

      from tensorflow.keras.losses import binary_crossentropy

      import numpy as np

      # Encoder

      inputs = Input(shape=(28, 28, 1))

      x = Flatten()(inputs)

      x = Dense(256, activation='relu')(x)

      z_mean = Dense(2)(x)

      z_log_var = Dense(2)(x)

      # Sampling

      def sampling(args):

      z_mean, z_log_var = args

      epsilon = tf.random.normal(shape=(tf.shape(z_mean)[0], 2))

      return z_mean + tf.exp(0.5 * z_log_var) * epsilon

      z = Lambda(sampling)([z_mean, z_log_var])

      # Decoder

      decoder_input = Input(shape=(2,))

      x = Dense(256, activation='relu')(decoder_input)

      x = Dense(28 * 28, activation='sigmoid')(x)

      outputs = Reshape((28, 28, 1))(x)

      decoder = Model(decoder_input, outputs)

      outputs = decoder(z)

      # VAE model

      vae = Model(inputs, outputs)

      vae.compile(optimizer='adam', loss=binary_crossentropy)

      # Training on MNIST dataset

      (x_train, _), (x_test, _) = tf.keras.datasets.mnist.load_data()

      x_train = np.expand_dims(x_train, -1) / 255.0

      x_test = np.expand_dims(x_test, -1) / 255.0

      vae.fit(x_train, x_train, epochs=10, batch_size=128, validation_data=(x_test, x_test))

      # Generating new digits

      import matplotlib.pyplot as plt

      z_sample = np.array([[0.5, -0.5]])

      generated_digit = decoder.predict(z_sample)

      plt.imshow(generated_digit.reshape(28, 28), cmap='gray')

      plt.show()

      This code snippet demonstrates how a generative model can create new data—in this case, handwritten digits that are similar to those found in the MNIST dataset.

      1.2 History and Evolution of Generative Models

      The concept of generative AI isn't new; it has been evolving for decades. Early models like Gaussian Mixture Models (GMMs) and Hidden Markov Models (HMMs) laid the groundwork by modeling data distributions and sequences. However, these models had limitations in complexity and scalability.

      Evolutionary Milestones:

      1980s-1990s: Introduction of foundational concepts like GMMs and HMMs. These models were used in speech recognition and simple image synthesis.

      2006: The rise of deep learning began with the introduction of deep belief networks (DBNs) by Geoffrey Hinton. DBNs opened the door for more complex generative models.

      2014: Ian Goodfellow introduced Generative Adversarial Networks (GANs), revolutionizing the field by enabling the generation of highly realistic images, text, and audio.

      2017: The introduction of the Transformer architecture by Vaswani et al. set the stage for models like GPT (Generative Pre-trained Transformer), which excelled in generating human-like text.

      2020s: Diffusion models and improvements in GANs, VAEs, and transformers have led to generative AI systems capable of creating hyper-realistic media, including deepfakes.

      Case Study: The evolution of GANs from their inception in 2014 to the creation of StyleGAN in 2019 showcases the rapid development within the field. StyleGAN, developed by NVIDIA, can generate high-resolution, photorealistic images of human faces that do not exist in reality. The model's success lies in its ability to control and manipulate different aspects of the image generation process, such as facial features, hairstyle, and even background elements.

      1.3 Key Applications of Generative AI

      Generative AI has found applications across various industries, proving its versatility and impact.

      1. Art and Creativity: Generative AI models are widely used to create art, music, and even literature. Artists and creators leverage these models to explore new forms of creativity. For example, AI-generated art has been sold at auctions, with one piece fetching over $400,000 at Christie's in 2018.

      2. Healthcare: In healthcare, generative AI is used to create synthetic medical data, which helps in training other AI models, protecting patient privacy while ensuring that models are robust and accurate. Additionally, it is used in drug discovery, where generative models can hypothesize new molecular structures with potential therapeutic benefits.

      3. Fashion and Design: Generative models are employed in fashion design to create new clothing patterns and designs, providing designers with innovative ideas that combine existing styles in novel ways. This has led to collaborations between AI and fashion brands to co-create collections.

      4. Autonomous Systems: In autonomous systems, especially in simulation environments, generative AI is used to create diverse and realistic scenarios for training self-driving cars, drones, and other autonomous machines. These scenarios help in better preparing these systems for real-world challenges.

      Use Case: OpenAI’s GPT-3, a state-of-the-art language model, is a prime example of generative AI applied in content creation. GPT-3 can generate human-like text, enabling its use

      Enjoying the preview?
      Page 1 of 1