📱Artificial intelligence
📱Hands-On AI: RAG using LlamaIndex

📱Artificial intelligence
📱Hands-On AI: RAG using LlamaIndex

📱Artificial intelligence
📱Hands-On AI: RAG using LlamaIndex

🌐 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 back-propagation.

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 Conventional 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.

Advancements 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.

🔅 Deep Learning: Model Optimization and Tuning

🌐 Author: Kumaran Ponnambalam
🔰 Level: Advanced

⏰ Duration: 54m

🌀 Learn about various optimization and tuning options available for deep learning models and use them to improve models.

📗 Topics: Deep Learning, Machine Learning

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📱Deep Learning: Model Optimization and Tuning

🔗 The cost of knowledge will become zero

In a test in Nigeria kids learned with the help of AI tutors the same amount of knowledge compared to two years with human teachers.

We don’t need schools anymore besides as a social room for meeting other kids.

Human teachers were still needed in this test. However, the question is how long.

🔅 Introduction to Large Language Models

🌐 Author: Jonathan Fernandes
🔰 Level: Intermediate

⏰ Duration: 1h 5m

🌀 Learn about large language models—what they are, what they can do, and how they work.

📗 Topics: Large Language Models

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📱Introduction to Large Language Models

🔗 How to use AI to learn anything faster

🔅 Deep Learning and Generative AI: Data Prep, Analysis, and Visualization with Python

🌐 Author: Gwendolyn Stripling
🔰 Level: Intermediate

⏰ Duration: 1h 56m

🌀 Learn the knowledge and practical skills needed to effectively utilize deep learning techniques using the Python programming language.

📗 Topics: Generative AI, Deep Learning, Python

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📱Artificial intelligence
📱Deep Learning and Generative AI: Data Prep, Analysis, and Visualization with Python

🔅 Machine Learning and AI Foundations: Classification Modeling

🌐 Author: Keith McCormick
🔰 Level: Intermediate

⏰ Duration: 2h 5m

🌀 Classification methods are among the most important in modern data science. Learn classification strategies and algorithms for machining learning and AI.

📗 Topics: Machine Learning, Artificial Intelligence, Data Classification

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📱Artificial intelligence
📱Machine Learning and AI Foundations: Classification Modeling

🔰 Artificial Intelligence A-Z 2025: Build 7 AI + LLM & ChatGPT

🌟 4.4 - 43970 votes 💰 Original Price: $89.99

📖 Combine the power of Data Science, Machine Learning and Deep Learning to create powerful AI for Real-World applications!

🔊 Taught By: Hadelin de Ponteves, Kirill Eremenko

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