🤛About Me: Sara EL-ATEIF is an ML GDE, Google Ph.D. Fellow at ENSIAS, NVIDIA DLI Instructor, and Lead of TFUG Casablanca. Working on Deep Multimodality learning for disease diagnosis. Actively contributing to AI4Good projects along with the AI Wonder Girls. 📒Pursuing: Currently pursuing the Mindvalley Business Coaching Certification, with the purpose of helping small businesses overcome their struggles and reignite their whole workflow for a thriving business journey. 🌟 Sara El-Ateif's purpose: Empowering Changemakers to Reach New Heights 🌟 Experienced Mentor | Purpose-driven Leader | Communication Specialist | AI Expert 🔸 Proven mentor with a demonstrated history of guiding individuals to unlock their full potential and achieve remarkable success. 🔸 Extensive experience in mentoring and coaching, helping clients uncover their true passions and define their purpose. 🔸 Strong background in Deep Learning, Computer Vision, applied to healthcare and SDG goals. 🔸 Passionate about empowering individuals to set and exceed their goals, supporting them on their transformative journeys. 🔸 Exceptional communication skills honed through years of experience, fostering meaningful connections and impactful collaborations. 🔸 Avid enthusiast of AI tools and their potential to enhance performance, leveraging cutting-edge technologies to drive results. 🔸 Well-versed in leadership development, equipping clients with the skills to lead with confidence, inspire others, and effect change. 🔸 Recognized for helping inspiring individuals stand out in their respective fields, positioning them as influential thought leaders and change agents. Ready to unlock your full potential and make a lasting impact? Let's embark on a transformative journey together. 💪✨ #UnlockYourPotential #MakeAnImpact

My Mentoring Topics

  • Uncover your potential
  • Figure out your purpose
  • Plan your goals
  • Evolve your communication skills
  • Improve your performance by using AI tools
  • Build/Polish your leadership skills
  • Stand out in your field
15.August 2023

"I'm incredibly grateful to Sara for our insightful mentoring call yesterday. Her unparalleled expertise in AI and data management, along with her dedication to coaching, were evident during our conversation. The valuable advice she provided has already positively influenced my career aspirations. I'm excited to continue this mentoring journey, knowing I'm in capable hands as I strive towards my goal. I highly recommend Sara to anyone seeking growth in IT and career management. Her expertise, kindness, and invaluable insights make her an outstanding mentor. Thank you for this enriching opportunity 😊 "

1.August 2023

what I really like about this mentorship, is her passion and energy. This really effect me in a good way. I really like to discuss different topics with her.

31.July 2023

Sara was the best coach that i met. She helped me to listen my heart and be more nurturing with my clients and with myself. We worked with the next 2-3 goals per the next month.She gave me the direction that i really needed. Thank you sara! I highly recommend her if you feel stuck and lost and you are looking to have a little change in your life and a clear direction.

15.June 2023

My session with Sara was the highlight of my day, she clarified things I was struggling to understand over years, in a matter of an hour. She fluidly nudged the conversation in a direction that guided me to find what I really want to do and what is standing in the way of it. her methods are simple to understand yet surprisingly have a very strong impact!!! I highly recommend her as a coach and a mentor, she's a great Giver and a sympathizer.

19.May 2023

Definitely coming back for more Insha'Allah. She's super understanding, asks the right questions, and shares great techniques to manage work-entrepreneurship balance.

The Compound Effect
Darren Hardy LLC

Key Facts and Insights from "The Compound Effect" The power of consistency and small decisions can lead to significant outcomes over time, this is the principle of the Compound Effect. Choices, no matter how small, add up over time to create a large impact. Momentum is a powerful force that can help you achieve your goals, but it requires a consistent effort to build and maintain. Habits are integral to success, and bad habits can be replaced with beneficial ones by understanding their triggers. Goals should be clear and specific, and there should be a system for tracking progress towards them. Peer influence plays a vital role in our decisions and habits, so we should choose our associations wisely. Success is not about doing extraordinary things but about doing ordinary things consistently and extraordinarily well. Personal development and continuous learning are central to success. One should take responsibility for their life, choices, and the results they achieve. Time management and focusing on high-value activities are crucial for success. An In-Depth Analysis of "The Compound Effect" The principle of the Compound Effect is the main theme of the book. It emphasizes that little, everyday decisions will either take you to the life you desire or to disaster by default. This concept is not new; it's reminiscent of Robert Collier's idea that success is the sum of small efforts, repeated day in and day out. However, Hardy's book explores this idea in a contemporary context, making it relevant to modern readers. The book’s primary emphasis is on the power of consistency. It reminds us of Aristotle's words, "We are what we repeatedly do. Excellence, then, is not an act, but a habit." Consistency, according to Hardy, is key to achieving long-term success and living an extraordinary life. The idea is that small, consistent actions add up over time, creating significant changes. The Compound Effect also addresses the role of choices in our lives. Hardy argues that we are the product of our choices and that even small decisions can have a significant impact over time. This aligns with the concept of 'locus of control' in psychology, which refers to the degree to which people believe they have control over their life. Momentum is another crucial concept Hardy introduces. He asserts that momentum, once built through consistent effort, can help achieve goals more easily. Similarly, in physics, an object in motion tends to stay in motion. The book also delves into the importance of habits. Hardy offers a practical guide to understanding and changing habits for personal and professional growth. He suggests identifying triggers that lead to harmful habits and replacing them with positive ones. This approach aligns with Charles Duhigg's 'habit loop' theory, which explains the cycle of cue, routine, and reward that forms a habit. Goal setting and tracking are also discussed extensively. Hardy advocates for SMART (Specific, Measurable, Achievable, Relevant, Time-bound) goals and emphasizes the importance of tracking progress. This approach is supported by research in the field of positive psychology that indicates that goal setting and tracking can enhance motivation and self-efficacy. The book also highlights the influence of peers or associations on our decisions and habits. This concept relates to the 'social impact theory,' which posits that people's thoughts and behaviors are shaped by those around them. Personal development and continuous learning are other key themes in the book. Hardy suggests that constant learning and self-improvement are central to success. This aligns with the idea of 'lifelong learning,' a concept widely recognized in educational psychology. Hardy also stresses the importance of taking responsibility for one's life, choices, and results. This concept is closely related to the psychological principle of 'self-determination,' which emphasizes autonomy and personal responsibility. Finally, the book discusses the importance of time management and focusing on high-value activities. Hardy argues that by focusing on tasks that create the most impact, one can maximize their productivity and success. This concept is reminiscent of the 'Pareto Principle' or the '80/20 rule,' which suggests that 80% of results come from 20% of efforts. In conclusion, "The Compound Effect" by Darren Hardy offers a practical and straightforward guide to achieving success through consistent effort, smart choices, and personal responsibility. The book's concepts align with many well-established psychological theories and principles, attesting to its value for those seeking personal and professional growth.

Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow - Concepts, Tools, and Techniques to Build Intelligent Systems
Aurélien Géron

Key Insights from the Book: Practical introduction to Machine Learning: The book provides a hands-on approach to learning machine learning, emphasizing practical implementation over theoretical understanding. Focus on Scikit-Learn, Keras, and TensorFlow: These three libraries are some of the most popular tools in the field of machine learning and deep learning. The book provides detailed instruction on how to use them effectively. End-to-end Machine Learning Project: The book walks the reader through a complete machine learning project, from gathering the data to training the model and evaluating its performance. Deep Learning Techniques: The book covers a variety of deep learning techniques, including convolutional neural networks, recurrent neural networks, and long short-term memory (LSTM) networks. Understanding of Neural Networks: The book aids in developing a solid understanding of neural networks and how they function. Model Evaluation and Fine-Tuning: The book goes into detail about how to evaluate a model’s performance, and how to fine-tune it to improve its accuracy. Feature Engineering: The book covers feature engineering in depth, which involves preparing the input data to make the machine learning algorithms more effective. Deployment of Machine Learning Models: The book provides guidance on how to deploy machine learning models into a production environment. Insight into the Future of AI and Machine Learning: The book discusses the future prospects and trends in AI and machine learning. Exploration of Reinforcement Learning: The book introduces the readers to reinforcement learning, a type of machine learning where an agent learns to behave in an environment, by performing certain actions and observing the results. Detailed Analysis and Summary: "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow" by Aurélien Géron is an invaluable resource for anyone seeking to delve into the world of machine learning. The book provides an in-depth exploration of machine learning, deep learning, and the tools required to build intelligent systems. Unlike many other books on the subject, it emphasizes practical implementation over theoretical understanding, making it particularly suitable for those who learn best by doing. The book places a strong focus on Scikit-Learn, Keras, and TensorFlow, some of the most popular libraries in the field of machine learning and deep learning. With the help of these libraries, users can implement powerful machine and deep learning models with relative ease. The book provides comprehensive guidance on how to use these tools effectively, including the implementation of various machine learning algorithms. One of the book's most salient features is the walkthrough of an end-to-end machine learning project. From gathering and preparing the data to training the model, evaluating its performance, and fine-tuning it to improve its accuracy, readers gain practical experience in machine learning. This hands-on approach is an effective way to learn and comprehend the various stages involved in a machine learning project. Deep learning techniques form a major part of the book. It covers a variety of these techniques, including convolutional neural networks, recurrent neural networks, and long short-term memory (LSTM) networks. These techniques are essential for tasks such as image and speech recognition, and natural language processing. The book offers a solid understanding of neural networks, the backbone of many modern machine learning algorithms. It explains how these networks function and how to train them, providing readers with the knowledge they need to build their own neural networks. The book also delves into model evaluation and fine-tuning, two crucial aspects of machine learning. It explains how to evaluate a model’s performance using various metrics and how to improve its accuracy through fine-tuning. This knowledge is crucial for developing effective machine learning models. Feature engineering, another important aspect of machine learning, is covered in depth. This process involves preparing the input data to make the machine learning algorithms more effective. The book provides practical guidance on how to perform feature engineering effectively. The book also provides guidance on how to deploy machine learning models into a production environment. This involves converting the trained model into a form that can be used in real-world applications, a crucial step in the machine learning pipeline. The book concludes with a discussion on the future prospects and trends in AI and machine learning, providing readers with an insight into the direction the field is likely to take in the coming years. Lastly, the book introduces the readers to reinforcement learning, a type of machine learning where an agent learns to behave in an environment by performing certain actions and observing the results. This is a rapidly growing area in machine learning, with applications in areas such as robotics and game playing. In conclusion, "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow" is a comprehensive guide that provides practical and actionable knowledge on various aspects of machine learning. Whether you are a beginner looking to enter the field or a seasoned professional seeking to update your knowledge, this book is a valuable resource that will help you understand and implement machine learning effectively.

Deep Learning
Ian Goodfellow, Yoshua Bengio, Aaron Courville

Key Insights from "Deep Learning" The primary focus of the book is on deep learning, a subset of machine learning that aims to formulate and solve problems by leveraging large amounts of data. The book provides a comprehensive background on machine learning, introducing concepts like linear algebra, probability, and information theory that are foundational to understanding deep learning. Deep learning algorithms are based on artificial neural networks, specifically those with several hidden layers, making them "deep" structures. The book delves into the details of different types of deep architectures including: Feedforward Neural Networks, Convolutional Networks, Sequence Modeling with Recurrent and Recursive Nets, and Practical Methodology. It covers backpropagation, the primary training algorithm for neural networks. The authors discuss regularisation for deep learning, including early stopping, parameter norm penalties, dataset augmentation, noise robustness, and semi-supervised learning. Goodfellow, Bengio, and Courville explore the nuances of optimization for training deep models. The book presents a comprehensive look at convolutional networks, a class of artificial neural networks that are particularly effective for image classification tasks. The authors also explore the realm of sequence modeling, offering insights into recurrent and recursive nets. There is a focus on practical methodology, providing guidance on how to choose the right architecture, dataset, and training strategies. The book concludes by discussing research perspectives on deep learning, suggesting potential future developments in the field. An In-depth Analysis of "Deep Learning" The book "Deep Learning" by Ian Goodfellow, Yoshua Bengio, and Aaron Courville is a comprehensive guide that presents an insightful overview of the rapidly developing field of deep learning. As an experienced professor in this field, I found that the authors have successfully condensed complex concepts into understandable, digestible content. The book begins by laying a strong foundation in machine learning, introducing essential concepts like linear algebra, probability, and information theory. This approach is crucial for beginners, as a solid understanding of these concepts is fundamental to grasping deep learning. A significant aspect that the authors delve into is the architecture of deep neural networks. Central to the book is the comprehensive exploration of artificial neural networks, particularly those with several hidden layers, acknowledging the depth of these structures. The authors also describe various types of deep architectures such as Feedforward Neural Networks and Convolutional Networks, offering the reader a holistic understanding of the subject. The authors' focus on backpropagation, the primary training algorithm for neural networks, offers valuable insights. They lucidly explain the backpropagation process, emphasizing its significance in adjusting weights within the network to minimize the difference between the actual and predicted outputs. Furthermore, the book offers an in-depth look at the nuances of optimization for training deep models, including topics like gradient descent and its variants, momentum, adaptive learning rates, and second-order methods. These details are crucial for implementing deep learning algorithms effectively. One of the highlights of the book is its comprehensive coverage of convolutional networks. As these networks are particularly effective for image classification tasks, the authors' exploration of this topic is both timely and relevant. They discuss the structure and functionality of convolutional networks, detailing how they emulate the human visual cortex's hierarchical pattern recognition. The authors also delve into sequence modeling, focusing on recurrent and recursive nets. This section is particularly interesting as it covers architectures designed to handle data where temporal dynamics and sequence are important, such as in language modeling or time-series prediction. The practical methodology section is another highlight, providing practical tips on how to choose the right architecture, dataset, and training strategies. This advice is invaluable for beginners and experienced practitioners alike, as it highlights the key considerations in building effective deep learning models. In conclusion, "Deep Learning" by Goodfellow, Bengio, and Courville is a comprehensive resource that offers a detailed overview of the field. It effectively bridges the gap between theory and practice, making it a valuable addition to the bookshelf of any student or practitioner interested in deep learning.