Snow leopard. Thick fur. Adaptations in animals as per habitat.

8.5.2 p 93

Comments

Post a Comment

Popular posts from this blog

8 Life Lessons from John D. Rockefeller’s Letters to His Son

  John D. Rockefeller, America’s first billionaire, left behind not just immense wealth but also timeless wisdom in letters to his son. These lessons remain relevant for success, character, and fulfillment.   #### **1. Success Demands Hard Work, Discipline, and Perseverance**   Rockefeller believed success wasn’t accidental but the result of relentless effort. He emphasized rising early, disciplined time management, and deliberate decision-making. His message: cultivate a strong work ethic early—it’s the foundation of lasting achievement.   #### **2. Shortcuts Lead to Short-Lived Success**   Integrity was non-negotiable for Rockefeller. He warned against unethical shortcuts, stressing that reputation takes years to build but seconds to destroy. His lesson: honesty in business ensures long-term respect and stability.   #### **3. Manage Money Wisely, No Matter How Rich You Are**   Despite his wealth, Rockefeller practiced fr...
Read more

AI project lifecycle explained by an analogy- two examples

 A.  Here’s an analogy using a science fair project to explain the AI project cycle : 1. Problem Scoping → Choosing a Science Fair Project Topic Before starting a science project, you first decide what problem you want to solve. For example, "How can we make plants grow faster?" 2. Data Acquisition → Collecting Materials & Information You gather what you need—seeds, soil, water, and information on plant growth. In AI, this step is like collecting data from various sources. 3. Data Exploration → Observing and Preparing the Data Before planting, you check if your soil is good, your seeds are healthy, and if anything needs to be adjusted. In AI, this is where you clean and analyze data to make sure it’s useful. 4. Modelling → Conducting the Experiment You plant seeds in different conditions (e.g., more sunlight, less water) and observe the growth. In AI, this is like training different models to see which one works best. 5. Evaluation → Checking the Results After ...
Read more