How to 'Learn' Generative AI

Whether you talk about the tech seeping through smartphones, cars having autonomous-driving features, or the tools being used by retailers to connect to their customers, AI can be said to have permeated human lives completely. While it has been around for decades now, it was with the release of ChatGPT in November 2022 that everyone finally woke up to it.

Recent Generative AI applications, such as ChatGPT, Stable Diffusion, GitHub Copilot, and others have been the talk of the town for various reasons. The top one, certainly, is their broad utility. A very large chuck of the population can use these tools, for both personal and professional benefits.

To grasp what the future holds, you must uncover the breakthroughs that have catalyzed the rise of Gen AI. To become a part of this revolution, or to make this revolution a part of your rise, an expert’s guide is needed. And here is one.

What is Generative AI?

What is Generative AI? It is an often asked question and here is an extremely simple definition for you.  Generative AI is a subdivision of machine learning that draws from leading techniques such as reinforcement learning and deep learning. It generates unique outputs, which can be text, music, video, images, etc.

Gen AI can simply be defined as apps generally built by employing foundation models. The said models consist of expansive artificial neural networks (ANNs), which take inspiration from the billions of connected neurons in the human brain. These models are a segment of deep learning (DL), which refers to multiple deep layers found within neural networks.

This is just the beginning of completely understanding gen AI, what it is, what are its true capabilities, and what the future holds. That said, it is a continuous learning process, especially with a technology like gen AI, which itself is still growing.

Generative AI will sway all industry sectors in the coming years.

High tech, life sciences & banking are expected to be the most impacted industries, in a positive sense.

What is the Hype Around Generative AI?

Do you remember having heard about Generative AI until a couple of years ago? Or even if you had, was it as omnipresent as it is today? I’m pretty sure the answer is no.

The sudden hype around gen AI can, without any doubt, be accredited to the rise of ChatGPT and DALL-E. There are plenty of gen AI tools that are available today, some free to use, while some are paid. All of these have certainly made a lot of things easier for a lot of people, both personally and professionally.

The curiosity around Gen AI has suddenly skyrocketed as it holds great potential to accelerate growth while reducing costs. It adds intelligence to data, making it easier for workers to gain a hold of previously unknown capabilities or knowledge.

Generative AI Tools & Their Growing Role in Various Industries

Generative AI tools have entered various industries and are judiciously stepping into roles traditionally played by humans.

1. Content Creation:

Gen AI has become a favorite among writers, content creators, and marketers to streamline workflows and automate content generation, all in a much shorter time. Thus, leaving them with the time to focus on more creative and strategic tasks. The winner in this section is certainly ChatGPT.

2. IT & Software Development:

A lot of change is happening around how code is written, especially since gen AI has entered the sphere. Developers can now generate code snippets, get solutions around coding challenges, and identify over 30% more defects. Leading gen AI tools for software development are GitHub Copilot, Bard, and AlphaCode.

3. Healthcare Industry:

Gen AI has truly been a game-changer in the medical field, as it assists in diagnosing diseases, predicting outcomes, and analyzing medical images. Radiology has benefitted incredibly from Gen AI too. Be it drug discovery, clinical trials, or medical robots, a sharp impact has been witnessed.

4. Design & Art:

Designers and artists are making the most of the impressive gen AI tools out there. Creating unique and unimaginable illustrations, designs, and visual art was never as easy as this. Midjourney and Jasper are text-to-art tools, wherein text input will result in creative output.

5. Banking & Finance:

Financial institutions are making the most of advancements in Generative AI by leveraging it to predict stock movements, optimize trading strategies, and analyze market trends. Hence, a significant increase in trading profitability and better investment decisions.

6. Language Translation:

People are stepping through the language barriers as many gen AI tools with the capability to translate speech and text in real-time have arrived. Seamless communications for global collaboration are no longer a distant dream.

How to Learn Generative AI?

When it comes to finding an answer to how to learn Generative AI from scratch, there are various things that you must consider. One of the key aspects is to judge how good your current knowledge is, and how much you still need to learn about the associated topics. Here are a few technologies, topics, tools, and aspects you need to get started in this field.

1. Learn the Fundamentals

To learn anything, you must be clear about the fundamentals, and the same is true when it comes to figuring out how to learn Generative AI. Here are the key pointers to begin with.

• Linear algebra offers the key mathematical operations to work with tensors, matrices, vectors, and various other multi-dimensional arrays in ML.
• Probability accommodates techniques such as Bayesian statistics, which is employed to make probabilistic predictions and statistical distributions.
• TensorFlow and PyTorch are key open-source libraries to develop and train ML models.
• Calculus is employed to optimize ML algorithms. Gain an understanding of key concepts like curve fitting, multivariate calculus, derivatives, interaction, and gradients.
• Python is the widely preferred programming language for ML. This aspect includes control flow, tools like Pandas and NumPy, object-oriented design, and data structures.
• Statistics offers the techniques to analyze ML data. Hence, you must learn descriptive statistics, regression, statistical reasoning, inference, and significance testing.

2. Understand Machine Learning

A subset of artificial intelligence, machine learning, or ML focuses on building systems with the ability to learn from data sets and improve with time. The top ML approaches to learn about are.

• Supervised Learning means training of the algorithm on labeled data sets, offering instances of the input and the intended output. The famous supervised learning algorithms incorporate decision trees, support vector machines, linear regression, k-nearest neighbors, and logistic regression. In addition, Cybersecurity skills can help in both AI/ML.
• Unsupervised Learning means analyzing data sets with just the inputs and no labeled responses. The aim is to uncover interest structure and relationships within the data.
• Artificial Neural Networks are inspired by biological neural networks and are made up of multiple layers of interconnected nodes like neurons. This knowledge is important to ensure fluent working.
• Reinforcement Learning means training algorithms for maximizing the trial-and-error interactions with the environment they are in. Training the data sets is not required as the agent learns by achieving their goals by getting feedback on their actions about which one is the better one.

3. Convolutional Neural Networks (CNNs)

A specialized kind of neural network, CNNs are apt for image processing. Convolutional layers are utilized to preserve spatial relationships within pixels. It is followed by pooling layers to further reduce dimensionality. It is important to learn about CNN architectures such as YOLO, ResNet, and VGG for Generative image modelling.

4. Generative Models

Generative models refer to a class of ML algorithms with the ability to generate new data samples, such as audio, text, or images. There are two popular Generative models.

• Generative Adversarial Networks or GANs are composed of a discriminator neural network and a generator neural network, competing against one another. The task of the generator is to create synthetic data samples, whereas the task of the discriminator is to distinguish between the real and the fake one. You must gain knowledge of Sequence GANs for text generation, WaveGANs for audio generation, and Deep Convolutional GANs for image generation.

  
  

• Variational Autoencoders are generative models that use autoencoding to learn latent representations of data. It comprises an encoder network and a decoder network. The former compresses data into a latent space vector, while the latter reconstructs data from the latent vector. You should learn key VAE architectures like Recurrent VAEs, VQ-VAEs, and Convolutional VAEs.

  
  

5. Master Associated Tools & Libraries

There are a plethora of tools and libraries, which require your proficiency in them. Here are some of the most influential tools and libraries associated with generative AI to look into.

• TensorFlow is a popular, open-source ML library, offering a comprehensive framework to build and train different generative models. It is a preferred library for deep learning tasks, such as text generation, style transfer, and image generation.
• Keras is a neural network API running atop TensorFlow and other backends. Both experienced and beginner developers prefer it, as it simplifies building and training DL models.
• Hugging Face Transformers is a widely preferred library as it facilitates working with natural language processing (NLP) models, such as BERT and GPT-2.
• GANLib is a popular Python library, which has specifically been designed for Generative Adversarial Networks (GANs).
• OpenAI Gym, an open-source toolkit, is designed to both develop and compare reinforcement learning algorithms.
• PyTorch is a prominent DL library that provides dynamic computation graphs, highly preferred by researchers due to its flexibility.

6. Take Generative AI Courses

Taking Generative AI courses is one of the best ways to get started on your journey to how to learn Generative AI from scratch. Enrolling in Generative AI training online will help you learn from those who have many years of experience in related fields. While you can get started with Generative AI tutorials, online videos, and blogs, nothing can compare to the effectiveness of a Generative AI certification program.

7. Get Hands-on Experience

Once you decide to make a career in this field or expand the horizons of your career with Generative AI, then you must get your hands engaged in gaining practical experience. Here are a few ways to get started.

• Complete online projects, courses, and tutorials
• Recreate projects and seminal papers
• Participate in GitHub projects and other competitions
• Become a part of the community
• Build a portfolio
• Attend seminars and sessions

8. Stay Up-To-Date

Nothing you ever learn in association with the tech world will remain the same over time. Hence, you have to constantly read, work, and practice to stay updated.

• Read studies and papers
• Attend meetups and conferences
• Follow forums and blogs
• Listen to videos and podcasts

Conclusion

Understanding Generative AI is an endless sea to search and learn. The more you read about machine learning, artificial intelligence, and robotics, the more curious you will get. Hence, the best way to learn Generative AI is with the aid of experts, who will be able to guide you in the best way possible. Follow this expert guide to become a pro in this emerging and growing technology.