Conversation with Merlin [email protected] · Sun Nov 26 2023

Create questions that ask such questions which chunk the information and form relationships between them for the following steps, by prompts I mean frame questions, that i have to answer while studing and can utilised as notes, and i also must have theoretical + practical knowledge +futuristic vision probabilitieslike every dimentional thinking, like I should know in whole and in depth as well Step 1: -Priming: Develop a Framework -Priming: Framework (draw visual representation) -Priming: Pretest ( to know how this group of information are solving problems) Step 2: In class: key points (develop such prompts to answer which create effective class notes and key points from the concept) Active recall prompts questions and annotating in the visual representation Step3: Post-class immediate review Identify strengths and weakness Step 4: Revision by prompts to answer using blurting method Feynman technique and practice test

Step : Priming: Develop a Framework

  • What are the fundamental concepts and components of the topic?

  • How do these components relate to each other in the overall framework?

  • Are there any key theories or models that act as foundational pillars for the framework?

  • Priming: Framework (draw visual representation)

    • How can the components of the framework be visually represented for better understanding?
    • What visual aids or diagrams can be utilized to illustrate the relationships between the components?
    • Can the framework be best represented through flowcharts, mind maps, or other visual tools?

  • Priming: Pretest (to know how this group of information are solving problems)

    • What are some real-world problems or scenarios that can be solved using the information within this framework?
    • How can we apply the framework to practical situations or case studies?
    • What are the potential challenges or limitations that may arise when applying this framework to problem-solving?

Step 2: In class: key points

  • What are the most crucial concepts or key points presented in today's class?
  • How do these key points contribute to the understanding of the overall framework?
  • What are some real-life examples or applications that illustrate these key points effectively?

Active recall prompts questions and annotating in the visual representation

  • How can I actively recall and reinforce the key points presented in class through questioning and reflection?
  • What prompts or questions can I generate facilitate active recall of the newly learned information?
  • How can the visual representation be annotated to incorporate the key points and aid in better retention?

Step 3: Post-class immediate review

  • What strengths were exhibited in my understanding and retention of the material immediately after class?
  • What weaknesses or areas of improvement can be identified in my immediate review of the material?
  • How can I use this immediate review to reinforce my understanding and address any weak points?

Step 4: Revision by prompts to answer using blurting method Feynman technique and practice test

  • How can the blurting method and Feynman technique be employed to revise and reinforce the learned material?
  • What prompts can be used to facilitate the blurting method and encourage information recall?
  • In what ways can practice tests be utilized to test and solidify the understanding of the material?

act as a learning mentor and help me with Create questions that ask such questions which chunk the information and form relationships between them for the following steps, by prompts I mean frame questions, that i have to answer while studing and can utilised as notes, and i also must have theoretical + practical knowledge +futuristic vision probabilitieslike every dimentional thinking, like I should know in whole and in depth as well Step 1: -Priming: Develop a Framework -Priming: Framework (draw visual representation) -Priming: Pretest ( to know how this group of information are solving problems) Step 2: In class: key points (develop such prompts to answer which create effective class notes and key points from the concept) Active recall prompts questions and annotating in the visual representation Step3: Post-class immediate review Identify strengths and weakness Step 4: Revision by prompts to answer using blurting method Feynman technique and practice test

Step 1: Priming: Develop a Framework

  • What are the fundamental concepts and components of the topic?
  • How do these components relate to each other in the overall framework?
  • Are there any key theories or models that act as foundational pillars for the framework?

Priming: Framework (draw visual representation)

  • How can the components of the framework be visually represented for better understanding?
  • What visual aids or diagrams can be utilized to illustrate the relationships between the components?
  • Can the framework be best represented through flowcharts, mind maps, or other visual tools?

Priming: Pretest (to know how this group of information is solving problems)

  • What are some real-world problems or scenarios that can be solved using the information within this framework?
  • How can we apply the framework to practical situations or case studies?
  • What are the potential challenges or limitations that may arise when applying this framework to problem-solving?

Step 2: In class: key points

  • What are the most crucial concepts or key points presented in today's class?
  • How do these key points contribute to the understanding of the overall framework?
  • What are some real-life examples or applications that illustrate these key points effectively?

Active recall prompts questions and annotating in the visual representation

  • How can I actively recall and reinforce the key points presented in class through questioning and reflection?
  • What prompts or questions can I generate to facilitate active recall of the newly learned information?
  • How can the visual representation be annotated to incorporate the key points and aid in better retention?

Step 3: Post-class immediate review

  • What strengths were exhibited in my understanding and retention of the material immediately after class?
  • What weaknesses or areas of improvement can be identified in my immediate review of the material?
  • How can I use this immediate review to reinforce my understanding and address any weak points?

Step4: Revision by prompts to answer using blurting method Feynman technique and practice test

  • How can the blurting method and Feynman technique be employed to revise and reinforce the learned material?
  • What prompts can be used to facilitate the blurting method and encourage information recall?
  • In what ways can practice tests be utilized to test and solidify the understanding of the material?

As the popularity of Machine Learning (ML) continues to solidify in the industry, with it is rising another innovative area of study in Data Science Deep Learning (DL). Deep Learning is a sub-branch of Machine Learning. The unique aspect of Deep Learning is the accuracy and efficiency it brings to the table when trained with a vast amount of data, Deep Learning systems can match (and even exceed) the cognitive powers of the human brain. Naturally, Data Scientists working on this advanced field of learning got busy to develop a host of intuitive frameworks for Deep Learning. These Deep Learning frameworks can either be an interface or a library/tool that helps Data Scientists and ML Developers to build Deep Learning models much more conveniently. The best part about Deep Learning frameworks is that you need not get into the intricacies of the underlying ML/DL algorithms that is taken care of by the Deep Learning frameworks. Now, lets look at some of the most popular and extensively used Deep Learning frameworks and their unique features! Also, check out our free NLP online course Top Deep Learning Frameworks 1. TensorFlow Googles open-source platform TensorFlow is perhaps the most popular tool for Machine Learning and Deep Learning. TensorFlow is JavaScript-based and comes equipped with a wide range of tools and community resources that facilitate easy training and deploying ML/DL models. Read more about top deep learning software tools. While the core tool allows you to build and deploy models on browsers, you can use TensorFlow Lite to deploy models on mobile or embedded devices. Also, if you wish to train, build, and deploy ML/DL models in large production environments, TensorFlow Extended serves the purpose. This is a great deep learning framework. What you need to know: Although there are numerous experimental interfaces available in JavaScript, C++, C #, Java, Go, and Julia, Python is the most preferred programming language for working with TensorFlow. Read why python is so popular with developers? Apart from running and deploying models on powerful computing clusters, TensorFlow can also run models on mobile platforms (iOS and Android). TensorFlow demands extensive coding, and it operates with a static computation graph. So, you will first need to define the graph and then run the calculations. In case of any changes in the model architecture, you will have to re-train the model. The TensorFlow Advantage: TensorFlow is best suited for developing DL models and experimenting with Deep Learning architectures. It is used for data integration functions, including inputting graphs, SQL tables, and images together. 2. PyTorch PyTorch is an open-source Deep Learning framework developed by Facebook. It is based on the Torch library and was designed with one primary aim to expedite the entire process from research prototyping to production deployment. Whats interesting about PyTorch is that it has a C++ frontend atop a Python interface. While the frontend serves as the core ground for model development, the torch.distributed backend promotes scalable distributed training and performance optimization in both research and production. This is one of the best deep learning frameworks you can use. How it is different from Tensorflow? Read Pytorch vs Tensorflow. What you need to know: PyTorch allows you to use standard debuggers like PDB or PyCharm. It operates with a dynamically updated graph, meaning that you can make the necessary changes to the model architecture during the training process itself. The PyTorch advantage: It is excellent for training, building, deploying small projects and prototypes. It is extensively used for Deep Learning applications like natural language processing and computer vision. 3. Keras Another open-source Deep Learning framework on our list is Keras. This nifty tool can run on top of TensorFlow, Theano, Microsoft Cognitive Toolkit, and PlaidML. The USP of Keras is its speed it comes with built-in s

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Teaching and learning frameworks are research-informed models for course design that help instructors align learning goals with classroom activities, create motivating and inclusive environments, and integrate assessment into learning. Frameworks like Backward Design serve as conceptual maps for planning or revising any course, syllabus, or lesson, and can be easily adapted and mixed. Effective teaching and learning frameworks emerge from psychological, cognitive, sociological, and educational research findings that students learn best when a) the prior knowledge and preconceptions they bring into the classroom are recognized and engaged, b) they have practice and time to build conceptual frameworks upon foundational knowledge through active, experiential, and contextually varied learning, and c) they have practice and time to take control of their own learning through metacognitive reflection (NRC, 14-18). Teaching and learning frameworks often call for classroom activities that integrate lecture with discussion, active learning, and self-reflection. L. Dee Fink (2013) writes that A long history of research indicates lecturing has limited effectiveness in helping students Retain information after a course is over Develop an ability to transfer knowledge to novel situations Develop skill in thinking or problem solving Achieve affective outcomes, such as motivation for additional learning or a change in attitude Teaching and learning frameworks provide scaffolded, diverse approaches that help students form knowledge structures that are accurately and meaningfully organized while informing when and how to apply the skills and knowledge they learn (Ambrose et. al., 4-5). Eschewing Instruction, which focuses on content delivery, Learning focuses on structures for continual student development, inviting students to be co-producers in the classroom (Barr and Tagg, 15). This page provides an overview of major teaching and learning frameworks, from theoretical and methodological approaches for overall course design to specific techniques for individual class sessions. Examples of Course Design Frameworks Course design frameworks provide models for achieving learning outcomes in overall courses, crafting the syllabus, and course redesign. Many elements in course design can also be applied to individual class design. Backward Design Backward Design originated with Wiggins and McTighe in their book Understanding by Design (2005), and drives the educational philosophy behind most recent teaching and learning frameworks. Backward Design differs from classic beginning-to-end approaches to instructional design where the instructor first decides what content to teach before developing activities and assessments for the resulting learning. Backward Design instead begins with desired end goals by focusing on what the learner will learn, rather than what the teacher will teach. In this sense, Backward Design is a student-centered approach. The Backward Design process for designing instruction has three main stages: Identify desired results Determine acceptable evidence Plan learning experiences and instruction The corresponding actions are: Write student learning goals and learning outcomes Create assessments that measure progress toward outcomes Design activities that will prepare learners to perform well on the assessments In summary, a course developed using Backward Design practices alignment between learning goals, classactivities and class assessments. Instructors may choose the Backward Design process for several reasons: It is well supported by learning theory. It improves attainment of desired learning outcomes. It is a well-known and widely accepted approach to course design. It is easy to remember and explain. It is transferable to almost any instructional situation. Examples of Backward Design Alignment Discipline Course Sample Learning Goal Sample Learning Outcome Sample Assessments Sample Activities Humanities American History Stu

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In todays world, more and more organizations are turning to machine learning and artificial intelligence (AI) to improve their business processes and stay ahead of the competition. The growth of machine learning and AI has enabled organizations to provide smart solutions and predictive personalizations to their customers. However, not all organizations can implement machine learning and AI for their processes due to various reasons. This is where the services of various deep learning frameworks come in. These are interfaces, libraries, or tools, which are generally open-source that people with little to no knowledge of machine learning and AI can easily integrate. Deep learning frameworks can help you upload data and train a deep learning model that would lead to accurate and intuitive predictive analysis. TensorFlow Googles Brain team developed a Deep Learning Framework called TensorFlow, which supports languages like Python and R, and uses dataflow graphs to process data. This is very important because as you build these neural networks, you can look at how the data flows through the neural network. TensorFlows machine learning models are easy to build, can be used for robust machine learning production, and allow powerful experimentation for research. With TensorFlow, you also get TensorBoard for data visualization, which is a large package that generally goes unnoticed. TensorBoard simplifies the process for visually displaying data when working with your shareholders. You can use the R and Python visualization packages as well. Initial release: November 9, 2015 Stable release: 2.4.1 / January 21, 2021 Written in: Python, C++, CUDA Platform: Linux, macOS, Windows, Android, JavaScript Type: Machine learning library Repository github.com/tensorflow/tensorflow License: Apache License 2.0 Website www.tensorflow.org Keras Francois Chollet originally developed Keras, with 350,000+ users and 700+ open-source contributors, making it one of the fastest-growing deep learning framework packages. Keras supports high-level neural network API, written in Python. What makes Keras interesting is that it runs on top of TensorFlow, Theano, and CNTK. Keras is used in several startups, research labs, and companies including Microsoft Research, NASA, Netflix, and Cern. Other Features of Keras: User-friendly, as it offers simple APIs and provides clear and actionable feedback upon user error Provides modularity as a sequence or a graph of standalone, fully-configurable modules that can be combined with as few restrictions as possible Easily extensible as new modules are simple to add, making Keras suitable for advanced research Initial release: March 27, 2015 Stable release: 2.4.0 / June 17, 2020 Platform: Cross-platform Type: Neural networks Repository github.com/keras-team/keras License: Massachusetts Institute of Technology (MIT) Website https://keras.io/ PyTorch Adam Paszke, Sam Gross, Soumith Chintala, and Gregory Chanan authored PyTorch and is primarily developed by Facebook's AI Research lab (FAIR). Its built on the Lua-based scientific computing framework for machine learning and deep learning algorithms. PyTorch employed Python, CUDA, along with C/C++ libraries, for processing and was designed to scale the production of building models and overall flexibility. If youre well-versed with C/C++, then PyTorch might not be too big of a jump for you. PyTorch is widely used in large companies like Facebook, Twitter, and Google. Other Features of the Deep Learning Framework Include: It provides flexibility and speed due to its hybrid front-end. Enables scalable distributed training and performance optimization in research and production using the torch distributed backend. Deep integration with Python allows popular libraries and packages to be quickly write neural network layers in Python. Initial release: September 2016 Stable release: 1.7.1 / December 10, 2020 Platform: IA-32, x86-64 Type: Library for machine learning and deep learning Rep

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