Top Bayesian Algorithms and Methods:

- Naive Bayes.
- Averages one-dependence estimators.
- Bayesian belief networks.
- Gaussian naive Bayes.
- Multinomial naive Bayes.
- Bayesian networks.

What kind of problems neural nets can solve?

Neural nets are good at solving non-linear problems. Some good examples are problems that are relatively easy for humans (because of experience, intuition, understanding, etc), but difficult for traditional regression models: speech recognition, handwriting recognition, image identification, etc.

AI Engineer

Deep Learning: Neural networks, CNNs, RNNs, transformers.
Programming: Python, TensorFlow, PyTorch, Keras.
NLP: NLTK, SpaCy, Hugging Face.
Computer Vision: OpenCV techniques.
Reinforcement Learning: RL algorithms and applications.
LLMs and Transformers: Advanced language models.
LangChain and RAG: Retrieval-augmented generation techniques.
Vector Databases: Managing embeddings and vectors.
AI Ethics: Ethical considerations and bias in AI.
R&D: Implementing AI research papers.

Hard Pill To Swallow: 💊

Robots aren’t stealing your future - they’re taking the boring jobs. 

Meanwhile:

- Some YouTuber made six figures sharing what she loves. 
- A teen's random app idea just got funded.
- My friend quit banking to teach coding - he's killing it.

Here’s the thing:

Hard work still matters. But the rules of the game have changed. 

The real money is in solving problems, spreading ideas, and building cool stuff.

Call it evolution. Call it disruption. Whatever.

Crying about the old world won't help you thrive in the new one.

Create something.✨

#ai

10 Things you need to become an AI/ML engineer:

1. Framing machine learning problems
2. Weak supervision and active learning
3. Processing, training, deploying, inference pipelines
4. Offline evaluation and testing in production
5. Performing error analysis. Where to work next
6. Distributed training. Data and model parallelism
7. Pruning, quantization, and knowledge distillation
8. Serving predictions. Online and batch inference
9. Monitoring models and data distribution shifts
10. Automatic retraining and evaluation of models

AI/ML Roadmap👨🏻‍💻👾🤖 -

==== Step 1: Basics ====

📊 Learn Math (Linear Algebra, Probability).
🤔 Understand AI/ML Fundamentals (Supervised vs Unsupervised).

==== Step 2: Machine Learning ====

🔢 Clean & Visualize Data (Pandas, Matplotlib).
🏋️‍♂️ Learn Core Algorithms (Linear Regression, Decision Trees).
📦 Use scikit-learn to implement models.

==== Step 3: Deep Learning ====

💡 Understand Neural Networks.
🖼️ Learn TensorFlow or PyTorch.
🤖 Build small projects (Image Classifier, Chatbot).

==== Step 4: Advanced Topics ====

🌳 Study Advanced Algorithms (Random Forest, XGBoost).
🗣️ Dive into NLP or Computer Vision.
🕹️ Explore Reinforcement Learning.

==== Step 5: Build & Share ====

🎨 Create real-world projects.
🌍 Deploy with Flask, FastAPI, or Cloud Platforms.

#ai #ml

ARTIFICIAL INTELLIGENCE 🤖

🎥 Siraj Raval - YouTube channel with tutorials about AI.
🎥 Sentdex - YouTube channel with programming tutorials.
⏱ Two Minute Papers - Learn AI with 5-min videos.
✍️ Data Analytics - blog on Medium.
🎓 Google Machine Learning Course - A crash course on machine learning taught by Google engineers.
🌐 Google AI - Learn from ML experts at Google.

Neural Networks and Deep Learning
Neural networks and deep learning are integral parts of artificial intelligence (AI) and machine learning (ML). Here's an overview:

1.Neural Networks: Neural networks are computational models inspired by the human brain's structure and functioning. They consist of interconnected nodes (neurons) organized in layers: input layer, hidden layers, and output layer.

Each neuron receives input, processes it through an activation function, and passes the output to the next layer. Neurons in subsequent layers perform more complex computations based on previous layers' outputs.

Neural networks learn by adjusting weights and biases associated with connections between neurons through a process called training. This is typically done using optimization techniques like gradient descent and backpropagation.

2.Deep Learning : Deep learning is a subset of ML that uses neural networks with multiple layers (hence the term "deep"), allowing them to learn hierarchical representations of data.

These networks can automatically discover patterns, features, and representations in raw data, making them powerful for tasks like image recognition, natural language processing (NLP), speech recognition, and more.

Deep learning architectures such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory networks (LSTMs), and Transformer models have demonstrated exceptional performance in various domains.

3.Applications Computer Vision: Object detection, image classification, facial recognition, etc., leveraging CNNs.

Natural Language Processing (NLP) Language translation, sentiment analysis, chatbots, etc., utilizing RNNs, LSTMs, and Transformers.
Speech Recognition: Speech-to-text systems using deep neural networks.

4.Challenges and Advancements: Training deep neural networks often requires large amounts of data and computational resources. Techniques like transfer learning, regularization, and optimization algorithms aim to address these challenges.

LAdvancements in hardware (GPUs, TPUs), algorithms (improved architectures like GANs - Generative Adversarial Networks), and techniques (attention mechanisms) have significantly contributed to the success of deep learning.

5. Frameworks and Libraries: There are various open-source libraries and frameworks (TensorFlow, PyTorch, Keras, etc.) that provide tools and APIs for building, training, and deploying neural networks and deep learning models.

Join for more: https://t.me/machinelearning_deeplearning

AI Engineers 🧬😂

ChatGPT Cheatsheet

#chatgpt

AI vs ML vs Neural Networks vs Deep Learning

𝐇𝐨𝐰 𝐭𝐨 𝐃𝐞𝐬𝐢𝐠𝐧 𝐚 𝐍𝐞𝐮𝐫𝐚𝐥 𝐍𝐞𝐭𝐰𝐨𝐫𝐤

→ 𝐃𝐞𝐟𝐢𝐧𝐞 𝐭𝐡𝐞 𝐏𝐫𝐨𝐛𝐥𝐞𝐦

Clearly outline the type of task:
↬ Classification: Predict discrete labels (e.g., cats vs dogs).
↬ Regression: Predict continuous values
↬ Clustering: Find patterns in unsupervised data.

→ 𝐏𝐫𝐞𝐩𝐫𝐨𝐜𝐞𝐬𝐬 𝐃𝐚𝐭𝐚

Data quality is critical for model performance.
↬ Normalize and standardize features MinMaxScaler, StandardScaler.
↬ Handle missing values and outliers.
↬ Split your data: Training (70%), Validation (15%), Testing (15%).

→ 𝐃𝐞𝐬𝐢𝐠𝐧 𝐭𝐡𝐞 𝐍𝐞𝐭𝐰𝐨𝐫𝐤 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞

𝑰𝐧𝐩𝐮𝐭 𝐋𝐚𝐲𝐞𝐫
↬ Number of neurons equals the input features.

𝐇𝐢𝐝𝐝𝐞𝐧 𝐋𝐚𝐲𝐞𝐫𝐬
↬ Start with a few layers and increase as needed.
↬ Use activation functions:
→ ReLU: General-purpose. Fast and efficient.
→ Leaky ReLU: Fixes dying neuron problems.
→ Tanh/Sigmoid: Use sparingly for specific cases.

𝐎𝐮𝐭𝐩𝐮𝐭 𝐋𝐚𝐲𝐞𝐫
↬ Classification: Use Softmax or Sigmoid for probability outputs.
↬ Regression: Linear activation (no activation applied).

→ 𝐈𝐧𝐢𝐭𝐢𝐚𝐥𝐢𝐳𝐞 𝐖𝐞𝐢𝐠𝐡𝐭𝐬

Proper weight initialization helps in faster convergence:
↬ He Initialization: Best for ReLU-based activations.
↬ Xavier Initialization: Ideal for sigmoid/tanh activations.

→ 𝐂𝐡𝐨𝐨𝐬𝐞 𝐭𝐡𝐞 𝐋𝐨𝐬𝐬 𝐅𝐮𝐧𝐜𝐭𝐢𝐨𝐧

↬ Classification: Cross-Entropy Loss.
↬ Regression: Mean Squared Error or Mean Absolute Error.

→ 𝐒𝐞𝐥𝐞𝐜𝐭 𝐭𝐡𝐞 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐞𝐫

Pick the right optimizer to minimize the loss:
↬ Adam: Most popular choice for speed and stability.
↬ SGD: Slower but reliable for smaller models.

→ 𝐒𝐩𝐞𝐜𝐢𝐟𝐲 𝐄𝐩𝐨𝐜𝐡𝐬 𝐚𝐧𝐝 𝐁𝐚𝐭𝐜𝐡 𝐒𝐢𝐳𝐞

↬ Epochs: Define total passes over the training set. Start with 50–100 epochs.
↬ Batch Size: Small batches train faster but are less stable. Larger batches stabilize gradients.

→ 𝐏𝐫𝐞𝐯𝐞𝐧𝐭 𝐎𝐯𝐞𝐫𝐟𝐢𝐭𝐭𝐢𝐧𝐠

↬ Add Dropout Layers to randomly deactivate neurons.
↬ Use L2 Regularization to penalize large weights.

→ 𝐇𝐲𝐩𝐞𝐫𝐩𝐚𝐫𝐚𝐦𝐞𝐭𝐞𝐫 𝐓𝐮𝐧𝐢𝐧𝐠

Optimize your model parameters to improve performance:
↬ Adjust learning rate, dropout rate, layer size, and activations.
↬ Use Grid Search or Random Search for hyperparameter optimization.

→ 𝐄𝐯𝐚𝐥𝐮𝐚𝐭𝐞 𝐚𝐧𝐝 𝐈𝐦𝐩𝐫𝐨𝐯𝐞

↬ Monitor metrics for performance:
→ Classification: Accuracy, Precision, Recall, F1-score, AUC-ROC.
→ Regression: RMSE, MAE, R² score.

→ 𝐃𝐚𝐭𝐚 𝐀𝐮𝐠𝐦𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧

↬ For image tasks, apply transformations like rotation, scaling, and flipping to expand your dataset.

#artificialintelligence

Important AI Terms

🚀 The Reality of Artificial Intelligence in the Real World 🌍

When people hear about Artificial Intelligence, their minds often jump to flashy concepts like LLMs, transformers, or advanced AI agents. But here’s the kicker: *90% of real-world ML solutions revolve around tabular data!* 📊

Yes, you heard that right. The bread and butter of Ai and machine learning in industries like healthcare, finance, logistics, and e-commerce is structured, tabular data. These datasets drive critical decisions, from predicting customer churn to optimizing supply chains.

📌 What You should Focus in Tabular Data?

1️⃣ Feature Engineering: Mastering this art can make or break a model. Understanding your data and creating meaningful features can give you an edge over even the fanciest models. 🛠️
2️⃣ Tree-Based Models: Algorithms like XGBoost, LightGBM, and Random Forest dominate here. They’re powerful, interpretable, and remarkably efficient for tabular datasets. 🌳🔥
3️⃣ Job-Ready Skills: Companies prioritize practical solutions over buzzwords. Learning to solve real-world problems with tabular data makes you a sought-after professional. 💼✨

💡 Takeaway: Before chasing the latest ML trends, invest time in understanding and building solutions for tabular data. It’s not just foundational—it’s the key to unlocking countless opportunities in the industry.

🌟 Remember, the simplest solutions often have the greatest impact. Don't overlook the power of tabular data in shaping the AI-driven world we live in!