Hammad Abbasi

Software Engineer | Machine Learning |Artificial Intelligence | Computer Vision|
NLP | Python | Crypto | Cricket & 👱

[Hammad Abbasi @_hammadabbasi]

🤖 AI Enthusiast | 🧠 Data Science Explorer | 🌐 NLP Enthusiast | 📊 Machine Learning Aficionado | 🔍 Computer Vision Explorer | Join me on an exciting journey through the realms of technology and innovation! 🚀🔥 #AI #DataScience #ML #NLP #ComputerVision

[Hammad Abbasi @_hammadabbasi]

@VivekGRamaswamy @what this post is about? What happened to charlie kirk.

[Hammad Abbasi @_hammadabbasi]

@iluminatibot @grok what does this mean is this correct?,

[Hammad Abbasi @_hammadabbasi]

@EndWokeness @grok what is this matter explain

[Hammad Abbasi @_hammadabbasi]

@laibaooe @stats_feed Why are you desperate to know?😂

[Hammad Abbasi @_hammadabbasi]

@Parul_Gautam7 Send please

[Akshay 🚀 @akshay_pachaar]

Every Python developer must know the LEGB rule!

It's crucial for avoiding major bugs!

And today, I will provide a clear explanation of this rule.

Let's go! 🚀

[Sasi 📊📈 @freest_man]

Black-box models are ML models that are too complicated for a human to understand.

These models make predictions based on input data.

However, the relationships and features they use to make those predictions are not readily apparent.

Black-box models are complex models that require external techniques to explain how they arrived at a prediction.

They are also called "explainable models"

Here are a few examples of black-box models:

Deep Neural Networks (DNNs):

Deep learning models, especially those with many layers, can be considered black-box models.

Understanding how each layer contributes to the final prediction can be challenging due to the complex interactions.

Random Forests:

While Random Forests are powerful for prediction, they are often considered black-box models.

The combination of many decision trees makes it difficult to interpret the exact reasoning behind a specific prediction.

Support Vector Machines (SVMs):

SVMs work by finding a hyperplane that best separates different classes in the feature space. 4

However, understanding the influence of each feature on the decision boundary can be complex in high-dimensional spaces.

Gradient Boosting Machines (GBM):

Models like XGBoost or LightGBM are excellent for predictive tasks but can be challenging to interpret due to the ensemble of weak learners and the complex combination of features.

The challenge with black-box models lies in their lack of transparency.

It can be a drawback in applications where interpretability and understanding the decision-making process are crucial, such as in certain regulatory or ethical contexts.

However, they often excel in predictive accuracy, making them valuable in various machine-learning scenarios.

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That’s all for now!

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[Akshaya Venky @venkyakshaya]

Being an adult is realising that home is never going to be one constant place anymore. 🥺

[Alex Hormozi @AlexHormozi]

You have to learn to do things you hate in order to do the things you love.

No payoffs come without sacrifice.

[Philosophy On Ice @PhilosophyOI]

"During the first period of a man's life the greatest danger is not to take the risk."
Soren Kierkegaard

[Daily Stoic @dailystoic]

“It's not things that upset us but our judgments about things.” — Epictetus

[Daily Stoic @dailystoic]

"Sometimes even to live is an act of courage." — Seneca

[Bindu Reddy @bindureddy]

Diffusion Models - The Magic Behind Stable Diffusion and MidJourney

Watching an AI model generating a photorealistic image from a text prompt feels like magic. Image generation models like Stable Diffusion and MidJourney have made us all prompt artists.

Here is how the magic happens. Stable Diffusion is a latent diffusion model. Deep learning-based diffusion models use neural networks to iteratively add and then remove noise from data, such as images, to generate new samples.

Essentially you start with a clean, target image and add noise in a series of steps - you go from a complex high-dimensional space of an image vector to a "noisy" space. You then "reconstruct" the image using a neural network trained to go from the noisy vector back to the image. As the model removes noise iteratively, it learns the essential features of the image and learns how to "reconstruct" the image and this is where the generative aspect comes into play.

The process of adding and removing noise allows for exploration of the data space and the noise acts like a form or regularization, preventing the model from overfitting and generalizing. Reconstructing or reversing the noise process helps the model generate similar data.

Once trained, you can start with random noise and end up with a generated image that has the characteristics of the training set.

Stabe Diffusion is a latent diffusion model. It largely operates in a low-dimension space by compressing the image into a much smaller lower-dimensional representation called the latent space. This makes for great efficiency and speed as you are dealing with a simplified version of the data

You start with a text prompt that is converted into a high-dimensional encoding using a Transformer-based encoder. This encoder maps a sequence of input tokens to a sequence of latent text embeddings, which are then used to condition the latent space for image generation.

These embeddings are then used to condition the latent space of the diffusion model in a process called latent space conditioning. Essentially the text embedding is used to modify or "condition" the initial random noise in the latent space and guides the diffusion model on what features to focus on and what the final image should be like. The diffusion process then iteratively refines this conditioned latent space, gradually transforming the random noise into a coherent image.

This allows for customization based on the text prompt and efficiency as we are operating in a low-dimensional space

This is of course an oversimplification of the process and there is a lot more detail to this process but hopefully should give a high-level understanding of the process by which text prompts get converted into beautiful images!

[Crypto Rover @cryptorover]

BlackRock aims to reduce the price of #Bitcoin, enabling them to purchase at a more favorable rate.

Once they've sufficiently acquired their desired amount, the Spot #Bitcoin ETF will be introduced, marking the onset of a Bull Market.

This is expected to happen in 2024.

[Crypto Rover @cryptorover]

The US dollar is doomed to collapse if it isn't backed by any scarce asset.

BRICS is on the rise.

The only way to save the US dollar is by backing it with #Bitcoin.

[Hammad Abbasi @_hammadabbasi]

Electronic cash can't be controlled that's why it is hated.#BTC #Crypto #BITCOIN

[Hammad Abbasi @_hammadabbasi]

Financial institutions are not mediators anymore, they are controllers.#BTC #BITCOIN

[Bindu Reddy @bindureddy]

Types of Neural Networks - Evolution Of Deep Learning Architectures.

Oppenheimer, the movie, has all of us thinking about the 40s and WW2. Believe it or not, the first neural networks (NN) were invented around the same time, in 1943!

Warren McCulloch and Walter Pitts the founding fathers of NNs, were intrigued by how biological neurons worked and proposed a mathematical model for a NN

It was not until 1958, that Frank Rosenblatt invented the "Perceptron" which was basically a computer program designed to learn from its mistakes. It ran on a very big machine and essentially did binary classification. While there was a lot of excitement around these baby NNs they required a lot of compute and data, which meant that they needed some serious funding.

In 1969, a paper titled "Perceptrons" by Minsky and Papert, killed almost all innovation in NNs. The paper proved that the single perceptron, couldn't solve simple problems including the XOR problem, and was severely limiting and all funding stopped. At the same time, algorithms like Support Vector Machines (SVMs) start taking off and NNs took a back seat.

Multi-layer perceptrons (MLPs) were viewed as a way to address the issues that single-layer perceptrons had, but training these MLPs proved to be very difficult. Not until 1986, did we see the resurgence NNs. Rumelhart, Hinton, and Williams introduced the backpropagation algorithm, and suddenly training multi-layer NNs became tractable. Computers were becoming more powerful and more data become available. NNs were back in business.

In the late 80s, Yann LeCunn introduced CNNs, The convolutional layers of a CNN can model the spatial hierarchy of images and NNs started to become useful in image-processing applications. Still, SVMs were the cool kids and NNs were being used for niche tasks like handwriting recognition.

Only in the 2000s, did we see a true renaissance of NNs. Geoff Hinton introduced Deep Belief Networks and the term deep learning (DL) begin to take off.

In 2012, Deep Learning had a seminal breakthrough with a CNN called AlexNet that outperformed all other algorithms in image classification. Since then we have seen an explosion in NN architectures.

Recurrent Neural Networks (RNNs) and Long Short-Term Memory Networks were useful in understanding patterns in sequential data. In 2015, ResNets helped solve the vanishing gradient problem (another pesky issue with DL training), and DL research was exploding.

In 2014, generative NNs had a big moment - Generative Adversarial Networks (GANs) were invented by Ian Goodfellow et. al. GANs were really good at generating realistic images. The first deep fake was born :)

Finally, in 2017, Vaswani et al introduced Transformers. Transformers, with their self-attention mechanism allowed the model to weigh the importance of each word in relation to others and better understand language.

BERT in 2018, was a specific implementation on Transformers and can look and understand text in both directions. BERT is pre-trained on massive amounts of data (e.g. Wikipedia) and can be adapted to specific tasks with fine-tuning. BERT can be adapted to multiple tasks like Q/A and text classification

Just a few months earlier, also in 2018, OpenAI introduced the GPT models. These were unidirectional but also were trained on massive amounts of data. Unlike BERT, GPTs are fine-tuned for generation or next-word prediction. Since 2018, we have seen better and more sophisticated versions of the GPT series...with GPT-4 released in 2023, being capable of human-level cognition, generation, and basic reasoning!!

So what started almost 80 years ago is now finally beginning to take over and transform the world completely!! 🤯

[Hammad Abbasi @_hammadabbasi]

We can always walk one step from our last step

[Hammad Abbasi @_hammadabbasi]

Don’t waste time focusing on things you can’t change.