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.
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.
My Mentoring Topics
- Find your purpose
- Improve your performance
- Improve your public speaking
- Integrate AI/emerging technologies in your workflow
- Develop leadership skills
The Compound Effect
Darren Hardy LLC
No gimmicks. No hyperbole. Finally, just the truth on what it takes to earn success As the central curator of the success media industry for over 25 years, author Darren Hardy has heard it all, seen it all, and tried most of it. This book reveals the core principles that drive success. The Compound Effect contains the essence of what every superachiever needs to know, practice, and master to obtain extraordinary success. Inside you will find strategies on: How to win--every time! The No. 1 strategy to achieve any goal and triumph over any competitor, even if they're smarter, more talented or more experienced.Eradicating your bad habits (some you might be unaware of!) that are derailing your progress.Painlessly installing the few key disciplines required for major breakthroughs.The real, lasting keys to motivation--how to get yourself to do things you don't feel like doing.Capturing the elusive, awesome force of momentum. Catch this, and you'll be unstoppable.The acceleration secrets of superachievers. Do they have an unfair advantage? Yes, they do, and now you can too! If you're serious about living an extraordinary life, use the power of The Compound Effect to create the success you desire. Begin your journey today!View
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow - Concepts, Tools, and Techniques to Build Intelligent Systems
Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how. By using concrete examples, minimal theory, and two production-ready Python frameworks—Scikit-Learn and TensorFlow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you’ve learned, all you need is programming experience to get started. Explore the machine learning landscape, particularly neural nets Use Scikit-Learn to track an example machine-learning project end-to-end Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods Use the TensorFlow library to build and train neural nets Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning Learn techniques for training and scaling deep neural netsView
Ian Goodfellow, Yoshua Bengio, Aaron Courville
An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives. “Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.” —Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.View
Definitely coming back for more Insha'Allah. She's super understanding, asks the right questions, and shares great techniques to manage work-entrepreneurship balance.