Artificial Intelligence

AI vs ML vs Neural Networks vs Deep Learning

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Russia is currently hosting the AI Journey international conference, during which the second season of the AI4PLANET scientific and educational video podcast was released. The main topic of this season was the role of AI in the emergence of new professions and transformation of existing ones. The speakers of the podcast discussed in 10 episodes how AI is already helping experts and what are the prospects of using AI in the work of ecologists, climatologists, doctors, teachers, HR-specialists and security officers. The experts sought answers to the burning questions: ▫️ How will AI strengthen the skills of the in-demand specialist of the future? ▫️ Do scientists and researchers already need to master Data Science skills now? ▫️ AI-developer for sustainable development - a new profession or a collective image of coordinated interdisciplinary work of a large team? AI4PLANET is a technological “journey” through professions from different fields of sustainable development: from climate and ecology to psychology and professions of the future. We invite you to visit the AI Journey international conference page and listen to the AI4PLANET video podcast.

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

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Russia is currently hosting the AI Journey international conference, during which the second season of the AI4PLANET scientific and educational video podcast was released. The main topic of this season was the role of AI in the emergence of new professions and transformation of existing ones. The speakers of the podcast discussed in 10 episodes how AI is already helping experts and what are the prospects of using AI in the work of ecologists, climatologists, doctors, teachers, HR-specialists and security officers. The experts sought answers to the burning questions: ▫️ How will AI strengthen the skills of the in-demand specialist of the future? ▫️ Do scientists and researchers already need to master Data Science skills now? ▫️ AI-developer for sustainable development - a new profession or a collective image of coordinated interdisciplinary work of a large team? AI4PLANET is a technological “journey” through professions from different fields of sustainable development: from climate and ecology to psychology and professions of the future. We invite you to visit the AI Journey international conference page and listen to the AI4PLANET video podcast.

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.

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

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