#lstm-rnn

[israbelle]

AGI means artificial general intelligence, the theoretical "computer with a soul" who crosses the borders of being alive so that would definitely cause more confusion lmaoo

[girlwithmanyproblems]

guys how to apply regression and then lstm ?

[neurospring]

History and Basics of Language Models: How Transformers Changed AI Forever - and Led to Neuro-sama

I have seen a lot of misunderstandings and myths about Neuro-sama's language model. I have decided to write a short post, going into the history of and current state of large language models and providing some explanation about how they work, and how Neuro-sama works! To begin, let's start with some history.

Before the beginning

Before the language models we are used to today, models like RNNs (Recurrent Neural Networks) and LSTMs (Long Short-Term Memory networks) were used for natural language processing, but they had a lot of limitations. Both of these architectures process words sequentially, meaning they read text one word at a time in order. This made them struggle with long sentences, they could almost forget the beginning by the time they reach the end.

Another major limitation was computational efficiency. Since RNNs and LSTMs process text one step at a time, they can't take full advantage of modern parallel computing harware like GPUs. All these fundamental limitations mean that these models could never be nearly as smart as today's models.

The beginning of modern language models

In 2017, a paper titled "Attention is All You Need" introduced the transformer architecture. It was received positively for its innovation, but no one truly knew just how important it is going to be. This paper is what made modern language models possible.

The transformer's key innovation was the attention mechanism, which allows the model to focus on the most relevant parts of a text. Instead of processing words sequentially, transformers process all words at once, capturing relationships between words no matter how far apart they are in the text. This change made models faster, and better at understanding context.

The full potential of transformers became clearer over the next few years as researchers scaled them up.

The Scale of Modern Language Models

A major factor in an LLM's performance is the number of parameters - which are like the model's "neurons" that store learned information. The more parameters, the more powerful the model can be. The first GPT (generative pre-trained transformer) model, GPT-1, was released in 2018 and had 117 million parameters. It was small and not very capable - but a good proof of concept. GPT-2 (2019) had 1.5 billion parameters - which was a huge leap in quality, but it was still really dumb compared to the models we are used to today. GPT-3 (2020) had 175 billion parameters, and it was really the first model that felt actually kinda smart. This model required 4.6 million dollars for training, in compute expenses alone.

Recently, models have become more efficient: smaller models can achieve similar performance to bigger models from the past. This efficiency means that smarter and smarter models can run on consumer hardware. However, training costs still remain high.

How Are Language Models Trained?

Pre-training: The model is trained on a massive dataset to predict the next token. A token is a piece of text a language model can process, it can be a word, word fragment, or character. Even training relatively small models with a few billion parameters requires trillions of tokens, and a lot of computational resources which cost millions of dollars.

Post-training, including fine-tuning: After pre-training, the model can be customized for specific tasks, like answering questions, writing code, casual conversation, etc. Certain post-training methods can help improve the model's alignment with certain values or update its knowledge of specific domains. This requires far less data and computational power compared to pre-training.

The Cost of Training Large Language Models

Pre-training models over a certain size requires vast amounts of computational power and high-quality data. While advancements in efficiency have made it possible to get better performance with smaller models, models can still require millions of dollars to train, even if they have far fewer parameters than GPT-3.

The Rise of Open-Source Language Models

Many language models are closed-source, you can't download or run them locally. For example ChatGPT models from OpenAI and Claude models from Anthropic are all closed-source.

However, some companies release a number of their models as open-source, allowing anyone to download, run, and modify them.

While the larger models can not be run on consumer hardware, smaller open-source models can be used on high-end consumer PCs.

An advantage of smaller models is that they have lower latency, meaning they can generate responses much faster. They are not as powerful as the largest closed-source models, but their accessibility and speed make them highly useful for some applications.

So What is Neuro-sama?

Basically no details are shared about the model by Vedal, and I will only share what can be confidently concluded and only information that wouldn't reveal any sort of "trade secret". What can be known is that Neuro-sama would not exist without open-source large language models. Vedal can't train a model from scratch, but what Vedal can do - and can be confidently assumed he did do - is post-training an open-source model. Post-training a model on additional data can change the way the model acts and can add some new knowledge - however, the core intelligence of Neuro-sama comes from the base model she was built on. Since huge models can't be run on consumer hardware and would be prohibitively expensive to run through API, we can also say that Neuro-sama is a smaller model - which has the disadvantage of being less powerful, having more limitations, but has the advantage of low latency. Latency and cost are always going to pose some pretty strict limitations, but because LLMs just keep getting more efficient and better hardware is becoming more available, Neuro can be expected to become smarter and smarter in the future. To end, I have to at least mention that Neuro-sama is more than just her language model, though we have only talked about the language model in this post. She can be looked at as a system of different parts. Her TTS, her VTuber avatar, her vision model, her long-term memory, even her Minecraft AI, and so on, all come together to make Neuro-sama.

Wrapping up - Thanks for Reading!

This post was meant to provide a brief introduction to language models, covering some history and explaining how Neuro-sama can work. Of course, this post is just scratching the surface, but hopefully it gave you a clearer understanding about how language models function and their history!

[lastscenecom]

畳み込みニューラル ネットワーク (CNN) CNN は、コンピューター ビジョンの世界における大きな武器です。彼らは、特殊なレイヤーのおかげで、画像内の空間パターンを認識する才能を持っています。この能力により、画像を認識し、その中にある物体を見つけ、見たものを分類することが得意になります。これらのおかげで、携帯電話で写真の中の犬と猫を区別できるのです。 リカレント ニューラル ネットワーク (RNN) RNN はある種のメモリを備えているため、文章、DNA シーケンス、手書き文字、株式市場の動向など、データのシーケンスが関係するあらゆるものに最適です。情報をループバックして、シーケンス内の以前の入力を記憶できるようにします。そのため、文中の次の単語を予測したり、話し言葉を理解したりするなどのタスクに優れています。 Long Short-Term Memory Networks (LSTM) LSTM は、長期間にわたって物事を記憶するために構築された特別な種類の RNN です。これらは、RNN が長いシーケンスにわたって内容を忘れてしまうという問題を解決するように設計されています。段落の翻訳や TV シリーズで次に何が起こるかを予測するなど、情報を長期間保持する必要がある複雑なタスクを扱う場合は、LSTM が最適です。 Generative Adversarial Networks (GAN) 2 つの AI のイタチごっこを想像してください。1 つは偽のデータ (画像など) を生成し、もう 1 つは何が偽物で何が本物かを捕まえようとします。それがGANです。この設定により、GAN は信じられないほどリアルな画像、音楽、テキストなどを作成できます。彼らはニューラル ネットワークの世界の芸術家であり、新しい現実的なデータをゼロから生成します。

ニューラル ネットワークの背後にある数学 |データサイエンスに向けて

[sunburstsoundlab]

The Role of AI in Music Composition

Artificial Intelligence (AI) is revolutionizing numerous industries, and the music industry is no exception. At Sunburst SoundLab, we use different AI based tools to create music that unites creativity and innovation. But how exactly does AI compose music? Let's dive into the fascinating world of AI-driven music composition and explore the techniques used to craft melodies, rhythms, and harmonies.

How AI Algorithms Compose Music

AI music composition relies on advanced algorithms that mimic human creativity and musical knowledge. These algorithms are trained on vast datasets of existing music, learning patterns, structures and styles. By analyzing this data, AI can generate new compositions that reflect the characteristics of the input music while introducing unique elements.

Machine Learning Machine learning algorithms, particularly neural networks, are crucial in AI music composition. These networks are trained on extensive datasets of existing music, enabling them to learn complex patterns and relationships between different musical elements. Using techniques like supervised learning and reinforcement learning, AI systems can create original compositions that align with specific genres and styles.

Generative Adversarial Networks (GANs) GANs consist of two neural networks – a generator and a discriminator. The generator creates new music pieces, while the discriminator evaluates them. Through this iterative process, the generator learns to produce music that is increasingly indistinguishable from human-composed pieces. GANs are especially effective in generating high-quality and innovative music.

Markov Chains Markov chains are statistical models used to predict the next note or chord in a sequence based on the probabilities of previous notes or chords. By analyzing these transition probabilities, AI can generate coherent musical structures. Markov chains are often combined with other techniques to enhance the musicality of AI-generated compositions.

Recurrent Neural Networks (RNNs) RNNs, and their advanced variant Long Short-Term Memory (LSTM) networks, are designed to handle sequential data, making them ideal for music composition. These networks capture long-term dependencies in musical sequences, allowing them to generate melodies and rhythms that evolve naturally over time. RNNs are particularly adept at creating music that flows seamlessly from one section to another.

Techniques Used to Create Melodies, Rhythms, and Harmonies

Melodies AI can analyze pitch, duration and dynamics to create melodies that are both catchy and emotionally expressive. These melodies can be tailored to specific moods or styles, ensuring that each composition resonates with listeners. Rhythms AI algorithms generate complex rhythmic patterns by learning from existing music. Whether it’s a driving beat for a dance track or a subtle rhythm for a ballad, AI can create rhythms that enhance the overall musical experience. Harmonies Harmony generation involves creating chord progressions and harmonizing melodies in a musically pleasing way. AI analyzes the harmonic structure of a given dataset and generates harmonies that complement the melody, adding depth and richness to the composition. -----------------------------------------------------------------------------

The role of AI in music composition is a testament to the incredible potential of technology to enhance human creativity. As AI continues to evolve, the possibilities for creating innovative and emotive music are endless.

Explore our latest AI-generated tracks and experience the future of music. 🎶✨

[xaltius]

Detecting Malicious URLs Using LSTM and Google’s BERT Models

In the sprawling, interconnected world of the internet, URLs are the fundamental addresses that guide us. But not all addresses lead to safe destinations. Phishing scams, malware distribution, drive-by downloads, and spam sites lurk behind seemingly innocent links, posing a constant and evolving threat to individuals and organizations alike.

Traditional methods of detecting these malicious URLs – relying on blacklists, simple heuristics, or pattern matching – are often reactive and easily bypassed by cunning attackers. As cyber threats become more sophisticated, so too must our defenses. This is where the formidable power of deep learning, specifically Long Short-Term Memory (LSTM) networks and Google’s BERT models, steps in to build more proactive and accurate detection systems.

The Evolving Threat: Why URL Detection is Hard

Attackers are masters of disguise and evasion. Malicious URLs are challenging to detect for several reasons:

Obfuscation: Using URL shorteners, encoding, or deceptive characters.

Polymorphism: Malicious URLs constantly change to avoid detection.

Short Lifespans: Phishing sites often last only hours before being taken down, making blacklisting ineffective.

Typo-squatting & Brand Impersonation: Subtle alterations of legitimate domain names (e.g., paypa1.com instead of paypal.com).

Zero-Day Threats: Entirely new attack patterns that haven't been seen before.

Why Deep Learning? Beyond Simple Rules

Traditional methods struggle because they rely on predefined rules or known bad patterns. Deep learning, however, can learn complex, non-linear patterns directly from raw data, enabling it to identify suspicious characteristics that human engineers might miss or that change too rapidly for manual updates.

Let's explore how LSTMs and BERT contribute to this advanced detection.

LSTM: Capturing the Sequence of URL Characters

Imagine a URL as a sequence of characters, like a sentence. LSTMs are a special type of Recurrent Neural Network (RNN) particularly adept at understanding sequences and remembering dependencies over long stretches of data.

How it Works: LSTMs excel at identifying subtle patterns in character order. For instance, they can learn the common structural patterns of legitimate domains (e.g., www.example.com/page?id=123) versus the chaotic or oddly structured nature of some malicious ones (e.g., 192.168.1.1/long_random_string/execute.exe). They can detect if a domain name has too many hyphens, unusual character repetitions, or resembles known Domain Generation Algorithm (DGA) outputs.

Why it's Powerful: LSTMs are excellent for recognizing syntactic and structural anomalies. They can flag URLs that look suspicious even if their individual components aren't overtly malicious. They learn a "fingerprint" of typical URL structures.

Limitation: While great for structure, LSTMs might not fully grasp the meaning of the words within the URL.

Google’s BERT: Understanding the Semantics of URL Components

BERT (Bidirectional Encoder Representations from Transformers) is a pre-trained language model that revolutionized Natural Language Processing. Unlike LSTMs that read sequentially, BERT processes text bidirectionally, understanding the context of each word based on all the other words around it.

How it Works: For URLs, BERT can treat different components (subdomains, domain names, path segments, query parameters) as "words" or tokens. It can then understand the semantic meaning and relationship between these components. For example:

Detecting brand impersonation: login.bank-of-america.security-update.com – BERT can understand that "security-update" or "login" might be semantically suspicious when combined with "bank-of-america."

Identifying malicious keywords: Flagging URLs containing words like "free-download," "crack," "giveaway," or "urgent-notice" in unusual contexts.

Understanding the intent behind query parameters that might carry exploits.

Why it's Powerful: BERT excels at semantic and contextual understanding. It can spot URLs that sound suspicious or attempt to mimic legitimate sites through clever wording, even if their structure appears normal. This is crucial for detecting sophisticated phishing.

Limitation: BERT is computationally heavier and requires careful tokenization of URL components.

Combining Forces: The Ensemble Power of LSTM + BERT

The true strength lies in a synergistic combination of these two powerful models.

The Hybrid Approach:

An LSTM branch can analyze the URL as a raw character sequence to capture structural anomalies and low-level patterns.

A BERT branch can analyze tokenized components of the URL (e.g., domain words, path segments) to understand their semantic meaning and contextual relationships.

The insights (feature vectors) from both models are then fed into a final classification layer (e.g., a neural network) which makes the ultimate decision: Malicious or Benign.

Superior Detection: This ensemble approach leverages the best of both worlds:

LSTM: Catches the weirdly structured, character-level obfuscated threats.

BERT: Uncovers the cunningly crafted, semantically deceptive phishing attempts. The result is a more robust, accurate, and adaptive detection system capable of identifying a wider spectrum of malicious URLs, even zero-day variants, with fewer false positives.

Training & Deployment Considerations

Building such a system requires:

Vast Datasets: Millions of both benign and malicious URLs are needed for training, often requiring sophisticated data collection and labeling techniques.

Computational Resources: Training BERT and large LSTMs requires significant GPU power.

Real-time Performance: Models must be optimized for low-latency inference to scan URLs as they are accessed.

Continuous Learning: The threat landscape changes daily. The models need mechanisms for continuous retraining and adaptation to new attack patterns.

The Future of URL Security

The battle against malicious URLs is a never-ending arms race. As attackers leverage AI to create more sophisticated threats, so too must our defenses. The combination of LSTMs for structural integrity and BERT for semantic intelligence represents a powerful frontier in cybersecurity. It's a proactive, intelligent defense that moves beyond mere pattern matching, enabling us to detect, respond to, and mitigate threats faster than ever before, ensuring a safer digital experience for everyone.

[shakshi09]

How is TensorFlow used in neural networks?

TensorFlow is a powerful open-source library developed by Google, primarily used for building and training deep learning and neural network models. It provides a comprehensive ecosystem of tools, libraries, and community resources that make it easier to develop scalable machine learning applications.

In the context of neural networks, TensorFlow enables developers to define and train models using a flexible architecture. At its core, TensorFlow operates through data flow graphs, where nodes represent mathematical operations and edges represent the multidimensional data arrays (tensors) communicated between them. This structure makes it ideal for deep learning tasks that involve complex computations and large-scale data processing.

TensorFlow’s Keras API, integrated directly into the library, simplifies the process of creating and managing neural networks. Using Keras, developers can easily stack layers to build feedforward neural networks, convolutional neural networks (CNNs), or recurrent neural networks (RNNs). Each layer, such as Dense, Conv2D, or LSTM, can be customized with activation functions, initializers, regularizers, and more.

Moreover, TensorFlow supports automatic differentiation, allowing for efficient backpropagation during training. Its optimizer classes like Adam, SGD, and RMSprop help adjust weights to minimize loss functions such as categorical_crossentropy or mean_squared_error.

TensorFlow also supports GPU acceleration, which drastically reduces the training time for large neural networks. Additionally, it provides utilities for model saving, checkpointing, and deployment across platforms, including mobile and web via TensorFlow Lite and TensorFlow.js.

TensorFlow’s ability to handle data pipelines, preprocessing, and visualization (via TensorBoard) makes it an end-to-end solution for neural network development from experimentation to production deployment.

For those looking to harness TensorFlow’s full potential in AI development, enrolling in a data science machine learning course can provide structured and hands-on learning.

[aystkgz]

🧠 Brewing Language with AI: From RNN to GPT ☕

Imagine sipping your morning coffee while AI brews human-like conversations in the background. From the early days of RNNs to today's powerful Transformers like BERT, GPT, and T5, the evolution of language models is nothing short of magical.

In my latest blog post, we dive deep into:

🌀 How RNN, LSTM & GRU paved the way for NLP

⚡ Why Transformer architecture changed everything

🧩 BERT’s deep contextual understanding

✍️ GPT’s text generation mastery

🔁 T5’s versatility in turning everything into text

🔗 Read the full article: https://www.aibrewlab.site/2025/06/decoding-language-brewing-language.html ☕ Let's distill the art of language understanding together.

🎨 Image prompt: A coffee cup morphing into neural networks and LLMs like BERT and GPT, in a sci-fi brewing lab setup. (Visual created via Microsoft Bing Image Creator)

[moonstone987]

Machine Learning Training in Kochi: Building Smarter Futures Through AI

In today’s fast-paced digital age, the integration of artificial intelligence (AI) and machine learning (ML) into various industries is transforming how decisions are made, services are delivered, and experiences are personalized. From self-driving cars to intelligent chatbots, machine learning lies at the core of many modern technological advancements. As a result, the demand for professionals skilled in machine learning is rapidly rising across the globe.

For aspiring tech professionals in Kerala, pursuing machine learning training in Kochi offers a gateway to mastering one of the most powerful and future-oriented technologies of the 21st century.

What is Machine Learning and Why Does it Matter?

Machine learning is a subfield of artificial intelligence that focuses on enabling computers to learn from data and improve over time without being explicitly programmed. Instead of writing code for every task, machine learning models identify patterns in data and make decisions or predictions accordingly.

Real-World Applications of Machine Learning:

Healthcare: Predicting disease, personalized treatments, medical image analysis

Finance: Fraud detection, algorithmic trading, risk modeling

E-commerce: Product recommendations, customer segmentation

Manufacturing: Predictive maintenance, quality control

Transportation: Route optimization, self-driving systems

The scope of ML is vast, making it a critical skill for modern-day developers, analysts, and engineers.

Why Choose Machine Learning Training in Kochi?

Kochi, often referred to as the commercial capital of Kerala, is also evolving into a major technology and education hub. With its dynamic IT parks like Infopark and the growing ecosystem of startups, there is an increasing need for trained professionals in emerging technologies.

Here’s why best machine learning training in Kochi is an excellent career investment:

1. Industry-Relevant Opportunities

Companies based in Kochi and surrounding regions are actively integrating ML into their products and services. A well-trained machine learning professional has a strong chance of landing roles in analytics, development, or research.

2. Cost-Effective Learning

Compared to metro cities like Bangalore or Chennai, Kochi offers more affordable training programs without compromising on quality.

3. Tech Community and Events

Tech meetups, hackathons, AI seminars, and developer communities in Kochi create excellent networking and learning opportunities.

What to Expect from a Machine Learning Course?

A comprehensive machine learning training in Kochi should offer a well-balanced curriculum combining theory, tools, and hands-on experience. Here’s what an ideal course would include:

1. Mathematics & Statistics

A solid understanding of:

Probability theory

Linear algebra

Statistics

Optimization techniques

These are the foundational pillars for building effective ML models.

2. Programming Skills

Python is the dominant language in ML.

Students will learn how to use libraries like NumPy, Pandas, Scikit-Learn, TensorFlow, and Keras.

3. Supervised & Unsupervised Learning

Algorithms like Linear Regression, Decision Trees, Random Forest, SVM, KNN, and Naive Bayes

Clustering techniques like K-means, DBSCAN, and Hierarchical Clustering

4. Deep Learning

Basics of neural networks

CNNs for image recognition

RNNs and LSTMs for sequential data like text or time series

5. Natural Language Processing (NLP)

Understanding text data using:

Tokenization, stemming, lemmatization

Sentiment analysis, spam detection, chatbots

6. Model Evaluation & Deployment

Confusion matrix, ROC curves, precision/recall

Deploying ML models using Flask or cloud services like AWS/GCP

7. Real-World Projects

Top training institutes ensure that students work on real datasets and business problems—be it predicting house prices, classifying medical images, or building recommendation engines.

Career Scope After Machine Learning Training

A candidate completing machine learning training in Kochi can explore roles such as:

Machine Learning Engineer

Data Scientist

AI Developer

NLP Engineer

Data Analyst

Business Intelligence Analyst

These positions span across industries like healthcare, finance, logistics, edtech, and entertainment, offering both challenging projects and rewarding salaries.

How to Choose the Right Machine Learning Training in Kochi

Not all training programs are created equal. To ensure that your investment pays off, look for:

Experienced Faculty: Instructors with real-world ML project experience

Updated Curriculum: Courses must include current tools, frameworks, and trends

Hands-On Practice: Projects, case studies, and model deployment experience

Certification: Recognized certificates add weight to your resume

Placement Assistance: Support with resume preparation, mock interviews, and job referrals

Zoople Technologies: Redefining Machine Learning Training in Kochi

Among the many institutions offering machine learning training in Kochi, Zoople Technologies stands out as a frontrunner for delivering job-oriented, practical education tailored to the demands of the modern tech landscape.

Why Zoople Technologies?

Industry-Aligned Curriculum: Zoople’s training is constantly updated in sync with industry demands. Their machine learning course includes real-time projects using Python, TensorFlow, and deep learning models.

Expert Trainers: The faculty includes experienced professionals from the AI and data science industry who bring real-world perspectives into the classroom.

Project-Based Learning: Students work on projects like facial recognition systems, sentiment analysis engines, and fraud detection platforms—ensuring they build an impressive portfolio.

Flexible Batches: Weekend and weekday batches allow both students and working professionals to balance learning with other commitments.

Placement Support: Zoople has an active placement cell that assists students in resume building, interview preparation, and job placement with reputed IT firms in Kochi and beyond.

State-of-the-Art Infrastructure: Smart classrooms, AI labs, and an engaging online learning portal enhance the student experience.

With its holistic approach and strong placement track record, Zoople Technologies has rightfully earned its reputation as one of the best choices for machine learning training in Kochi.

Final Thoughts

Machine learning is not just a career path; it’s a gateway into the future of technology. As companies continue to automate, optimize, and innovate using AI, the demand for trained professionals will only escalate.

For those in Kerala looking to enter this exciting domain, enrolling in a well-rounded machine learning training in Kochi is a wise first step. And with institutes like Zoople Technologies leading the way in quality training and real-world readiness, your journey into AI and machine learning is bound to be successful.

So, whether you're a recent graduate, a software developer looking to upskill, or a data enthusiast dreaming of a future in AI—now is the time to start. Kochi is the place, and Zoople Technologies is the partner to guide your transformation.

[linguistlist-blog]

Calls: Deep Phonology: Doing phonology with deep learning (AMP 2025 Special Session)

Call for Papers: On Saturday, September 27, 2025, following the main AMP session held on September 25-26, 2025, there will be a special session on "Deep Phonology: Doing phonology with deep learning" held on the UC Berkeley campus. Phonology has been modeled using rules, constraints, finite state machines, exemplars, and many other approaches. Recent advances in deep learning have prompted researchers to explore how deep neural architectures (e.g., seq2seq models, transformers, RNNs, LSTMs http://dlvr.it/TK46sk

[girlwithmanyproblems]

3rd July 2024

Goals:

Watch all Andrej Karpathy's videos

Watch AWS Dump videos

Watch 11-hour NLP video

Complete Microsoft GenAI course

GitHub practice

Topics:

1. Andrej Karpathy's Videos

Deep Learning Basics: Understanding neural networks, backpropagation, and optimization.

Advanced Neural Networks: Convolutional neural networks (CNNs), recurrent neural networks (RNNs), and LSTMs.

Training Techniques: Tips and tricks for training deep learning models effectively.

Applications: Real-world applications of deep learning in various domains.

2. AWS Dump Videos

AWS Fundamentals: Overview of AWS services and architecture.

Compute Services: EC2, Lambda, and auto-scaling.

Storage Services: S3, EBS, and Glacier.

Networking: VPC, Route 53, and CloudFront.

Security and Identity: IAM, KMS, and security best practices.

3. 11-hour NLP Video

NLP Basics: Introduction to natural language processing, text preprocessing, and tokenization.

Word Embeddings: Word2Vec, GloVe, and fastText.

Sequence Models: RNNs, LSTMs, and GRUs for text data.

Transformers: Introduction to the transformer architecture and BERT.

Applications: Sentiment analysis, text classification, and named entity recognition.

4. Microsoft GenAI Course

Generative AI Fundamentals: Basics of generative AI and its applications.

Model Architectures: Overview of GANs, VAEs, and other generative models.

Training Generative Models: Techniques and challenges in training generative models.

Applications: Real-world use cases such as image generation, text generation, and more.

5. GitHub Practice

Version Control Basics: Introduction to Git, repositories, and version control principles.

GitHub Workflow: Creating and managing repositories, branches, and pull requests.

Collaboration: Forking repositories, submitting pull requests, and collaborating with others.

Advanced Features: GitHub Actions, managing issues, and project boards.

Detailed Schedule:

Wednesday:

2:00 PM - 4:00 PM: Andrej Karpathy's videos

4:00 PM - 6:00 PM: Break/Dinner

6:00 PM - 8:00 PM: Andrej Karpathy's videos

8:00 PM - 9:00 PM: GitHub practice

Thursday:

9:00 AM - 11:00 AM: AWS Dump videos

11:00 AM - 1:00 PM: Break/Lunch

1:00 PM - 3:00 PM: AWS Dump videos

3:00 PM - 5:00 PM: Break

5:00 PM - 7:00 PM: 11-hour NLP video

7:00 PM - 8:00 PM: Dinner

8:00 PM - 9:00 PM: GitHub practice

Friday:

9:00 AM - 11:00 AM: Microsoft GenAI course

11:00 AM - 1:00 PM: Break/Lunch

1:00 PM - 3:00 PM: Microsoft GenAI course

3:00 PM - 5:00 PM: Break

5:00 PM - 7:00 PM: 11-hour NLP video

7:00 PM - 8:00 PM: Dinner

8:00 PM - 9:00 PM: GitHub practice

Saturday:

9:00 AM - 11:00 AM: Andrej Karpathy's videos

11:00 AM - 1:00 PM: Break/Lunch

1:00 PM - 3:00 PM: 11-hour NLP video

3:00 PM - 5:00 PM: Break

5:00 PM - 7:00 PM: AWS Dump videos

7:00 PM - 8:00 PM: Dinner

8:00 PM - 9:00 PM: GitHub practice

Sunday:

9:00 AM - 12:00 PM: Complete Microsoft GenAI course

12:00 PM - 1:00 PM: Break/Lunch

1:00 PM - 3:00 PM: Finish any remaining content from Andrej Karpathy's videos or AWS Dump videos

3:00 PM - 5:00 PM: Break

5:00 PM - 7:00 PM: Wrap up remaining 11-hour NLP video

7:00 PM - 8:00 PM: Dinner

8:00 PM - 9:00 PM: Final GitHub practice and review

[genaideepneuron]

Introduction to Transformers (Learn GEN AI contact +91 90432 35205

Introduction to Transformers

Teacher: Alright, class! Today, we’re going to talk about one of the most powerful architectures in AI—Transformers! 🚀

So, what is a Transformer?

Let’s take a step back. Imagine you’re reading a book. Do you read each word one by one, in order and remember only the last word? No, right? You scan the whole sentence and understand the meaning based on context.

That’s what Transformers do! Unlike older models like RNNs (which process words sequentially), Transformers look at all words at once and figure out how they relate to each other.

💡 Key Idea: Transformers understand context by paying attention to the relationships between all words at the same time.

Why are Transformers better than older models?

RNNs (Recurrent Neural Networks) → Read words one by one (slow, forgets long-term context).

LSTMs (Long Short-Term Memory) → Better at remembering, but still limited in long sentences.

Transformers → Look at everything at once, making them super fast and better at understanding complex meaning.

Transformers are used in models like BERT, GPT, T5, LLaMA, and they power chatbots like ChatGPT!

2️⃣ Self-Attention Mechanism: The Secret Sauce of Transformers

Teacher: Okay, now let’s dive into what makes Transformers so powerful—the Self-Attention Mechanism. This is like the brain of Transformers!

What is Self-Attention?

Imagine this sentence: 📝 "The cat sat on the mat because it was tired."

👉 What does "it" refer to? The cat! 🐱

But how did your brain figure that out? You paid attention to the important words in the sentence!

That’s exactly what Self-Attention does:

It looks at all words in a sentence at the same time.

It decides which words are most important in understanding the meaning.

It assigns higher attention scores to important words.

How does Self-Attention work? (Step by Step)

Every word in a sentence looks at every other word.

It calculates how related each word is to every other word.

It assigns an attention score (higher means more important).

The model then focuses on important words while ignoring less important ones.

💡 Example:

Sentence: "She opened the gift and was surprised to see a puppy." 🎁🐶

The model will give higher attention to the words "gift" → "puppy" → "surprised" because they are related!

That’s how AI understands context better than older models! 🧠✨

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[programmingandengineering]

DATA 255 Deep Learning Technologies – Homework -4 Solved

Problem 1: Use the IMDB Movie review dataset: (1+5 pts) Build the sentiment analysis model using Text preprocessing steps: Tokenization, Stopwords removing, HTML removing, Convert to lower case, Lemmatization/stemming Perform combination of different word embeddings (e.g., Word2Vec, Glove, and so on) and sequential models (e.g., RNN, LSTM, GRU, and so on). Provide a table that include results…

[krupa192]

How RNNs Imitate Memory: A Friendly Guide to Sequence Modeling 

In today’s fast-moving world of artificial intelligence and machine learning, understanding how models process sequences of data is essential. Whether it’s predicting the next word in a sentence, transcribing speech, or forecasting stock prices, Recurrent Neural Networks (RNNs) play a crucial role. But how exactly do these models manage to "remember" past information, and why are they so powerful when it comes to handling sequential data? Let’s break it down in simple terms. 

What Are RNNs and Why Do They Matter? 

At their core, Recurrent Neural Networks are a type of neural network designed specifically to work with sequences. This sets them apart from traditional feedforward networks, which treat each input independently. RNNs, however, take into account what has come before — almost like they have a built-in short-term memory. This allows them to understand the order of things and how past events influence the present, making them perfect for tasks where timing and sequence matter. 

How Do RNNs Mimic Memory? 

RNNs don’t literally have memory like a human brain, but they do a good job of approximating it. Here’s how: 

1. Passing Information Forward 

Imagine reading a sentence one word at a time. With each word, you remember the previous ones to make sense of the sentence. RNNs do something similar by passing information from one step to the next using what's called a hidden state. 

This hidden state is updated every time the model processes a new input. So at each time step, the network not only looks at the current input but also considers what it "remembers" from before. The formula might look technical, but in essence, it's just constantly refreshing its understanding of context. 

2. Maintaining Continuity 

Because of this hidden state, RNNs can handle data where one piece depends on what came before — like understanding a sentence, predicting the next value in a time series, or generating music. They essentially maintain a thread of continuity, similar to how our brains follow conversations or narratives. 

3. Handling Longer Sequences 

Standard RNNs can struggle with long-term memory due to issues like the vanishing gradient problem, which makes it difficult for them to retain information over long sequences. That’s where advanced models like Long Short-Term Memory (LSTM) networks and Gated Recurrent Units (GRU) come in. These architectures introduce gates that help the network decide what to keep and what to forget — much like how we might focus on important details and disregard irrelevant ones. 

Where Do We See RNNs in Action? 

The practical applications of RNNs are everywhere: 

Chatbots and virtual assistants rely on RNNs to maintain context and generate coherent replies. 

Speech-to-text systems use them to process audio signals in sequence, converting speech into accurate text. 

Financial forecasting and weather prediction models use RNNs to look at historical data and predict future trends. 

Even video analysis applications use RNNs to understand sequences of frames and recognize patterns over time. 

Why Learning RNNs and Sequence Modeling Matters 

While it’s fascinating to read about RNNs, working with them in real-world projects brings a completely new level of understanding. Building models, tuning hyperparameters, and dealing with real data challenges are skills best learned through practical, hands-on training. 

If you’re eager to dive into this field and you're in India — especially around Kolkata — the Machine Learning Course in Kolkata is an excellent place to start. 

Learn from Experts at the Boston Institute of Analytics, Kolkata 

The Boston Institute of Analytics (BIA) is known globally for providing industry-relevant training in machine learning, AI, and data science. Their Machine Learning Course in Kolkata is designed to help aspiring data professionals gain practical knowledge and hands-on experience. 

Here’s what you can expect from their program: 

Hands-on projects using real-world data sets that help you move beyond theory. 

In-depth modules covering neural networks, RNNs, LSTMs, GRUs, and other advanced architectures. 

Training in popular tools and libraries like Python, TensorFlow, Keras, and PyTorch. 

Access to experienced instructors who are active in the data science and AI industry. 

Strong placement support and career guidance to help you make the transition into a data-driven career. 

Trust, Authority, and Experience Matter 

When you choose to learn something as complex and future-focused as machine learning and deep learning, it’s important to do so from a credible, trusted institution. The Boston Institute of Analytics has built its reputation through: 

An impressive track record of alumni placed in companies like Google, Amazon, and Deloitte. 

Strong industry partnerships and endorsements. 

Transparent, practical, and well-structured courses that are globally recognized. 

This ensures that when you complete their program, you’re not just gaining knowledge — you're gaining the confidence to apply it in real-world scenarios. 

The Future of Sequence Modeling: Endless Possibilities 

As AI continues to grow, sequence modeling will only become more relevant. Technologies that understand time, order, and context are key to unlocking new levels of human-computer interaction. Whether it’s smarter voice assistants, real-time language translation, or predictive healthcare analytics, RNNs and their evolved forms (like LSTMs and GRUs) will continue to be at the heart of these innovations. 

Final Thoughts 

RNNs are powerful because they mimic a type of memory, enabling machines to understand sequences and patterns that unfold over time. From simple tasks like predicting the next word in a sentence to complex applications like forecasting stock prices or analyzing video footage — they’re everywhere. 

But more importantly, they’re accessible. With the right training, anyone with curiosity and commitment can learn how to use these models. If you’re looking to start your journey in AI and machine learning, enrolling in the Data Science Course could be the perfect first step. 

[shakshi09]

How do transformers enable autoregressive text generation capabilities?

Transformers enable autoregressive text generation capabilities through their unique architecture, which relies on self-attention mechanisms. Unlike previous models like RNNs or LSTMs, transformers allow for parallel processing of sequences, which leads to faster training and better handling of long-range dependencies within text. This is particularly beneficial for autoregressive text generation, where the model predicts the next word in a sequence based on previous words.

In autoregressive models, the text generation process is sequential: the model generates one token at a time, using the previously generated tokens as context for generating the next. The transformer’s self-attention mechanism helps here by allowing each token to attend to all other tokens in the sequence, thus capturing the dependencies across different positions. This means the model can generate contextually relevant text that is more coherent and accurate.

The transformer architecture uses a multi-head attention mechanism, which helps the model focus on different parts of the input sequence simultaneously. This allows the model to generate more diverse outputs and understand complex patterns in the data. The encoder-decoder structure in transformers, particularly in models like GPT, BERT, and T5, makes them highly effective for text generation tasks. In GPT models, for instance, only the decoder is used, where the autoregressive process begins by predicting the next word based on the input and all previous words generated.

By leveraging transformers, generative models can create high-quality, human-like text for various applications such as chatbots, writing assistants, and creative content generation. If you're interested in mastering these techniques, consider enrolling in a Generative AI certification course to develop deep expertise in this rapidly growing field.

[codezup]

Real-World Time Series Forecasting with LSTM Networks

1. Introduction 1.1 Explanation and Importance Time series forecasting is a critical task across various domains, from finance to healthcare, where predicting future events based on historical data is essential. LSTM (Long Short-Term Memory) networks, a type of Recurrent Neural Network (RNN), excel in capturing temporal dependencies, making them ideal for time series tasks. 1.2 Learning…