Think Forward.

Part 4/5: Research, Rants, & Ridiculousness: The Lighter Side of PhD Madness

Anas Bedraoui, 9 mon.
0 min read.
PhD: the art of turning coffee, chaos, and code into a degree, one panic attack at a time. - My machine learning model predicted I'd finish my PhD on time. Spoiler: Even AI has a sense of humor. - Neurotoxicity research: figuring out if it's the toxins affecting the brain, or just the endless hours in the lab. - Snake venom for drug discovery? Sure, because handling deadly snakes is less frightening than asking my advisor for a deadline extension. - I told my computer to find a cure for snake bites. It opened a travel site to Antarctica. No snakes, no bites, problem solved!

Morocco, Spain, and Portugal World Cup 2030: An Unforgettable Cultural Experience

Dr Rochd, 9 mon.
2 min read.
The 2030 World Cup organization by Morocco, Spain, and Portugal can be analyzed using various approaches. In this article, I want to open a path for an anthropological and cultural analysis of this subject. Morocco, Spain, and Portugal's joint bid for hosting the FIFA World Cup in 2030 has been recognized as a historic decision. The triumph is a significant event for these three nations, as it marks the first time they have worked together to showcase their unique cultural heritage, stunning landscapes, and passion for football on the global stage. The spirit of cooperation and unity among these diverse nations is reflected in this collaborative effort, which embodies not just football passion but also the spirit of cooperation and unity. Football's capacity to bridge borders and foster international friendships can be reflected by hosting the tournament jointly. Combining the vibrant culture of the kingdom of Morocco with the rich traditions of Spain and Portugal, the World Cup 2030 will offer an immersive fusion of flavors, music, and festivities. Morocco gives guests the opportunity to encounter the charming climate of the dynamic medinas, investigate the noteworthy engineering of cities such as Marrakech and Fez "Founded under Idrisid rule during the 8th–9th centuries CE," and enjoy within the country's delightful food. Fans will pick up a more profound understanding and appreciation of Morocco's social traditions through witnessing traditional music and dance performances. Spain, on the other hand, brags a captivating blend of old-world charm and present day dynamic quality.Whether it's exploring the antiquated lanes of Barcelona, seeing the emotional flamenco exhibitions of Andalusia, or indulging in mouth-watering tapas and paella, fans will be inundated within the wealthy cultural tapestry that Spain should offer. Portugal, known for its pleasant scenes, noteworthy cities, and warm neighborliness, includes another layer of social differing qualities to the World cup 2030 experience. Guests can meander through the cobbled roads of Lisbon, visit medieval castles in Sintra, and taste the popular Harbour wine in Porto. The captivating sounds of traditional Fado music, filled with feeling and energy, will transport fans into the soul of Portuguese culture. All three nations have a profound cherish for football, and it is interlaced into their social texture. The World Cup 2030 will be an opportunity for fans to witness firsthand the passion and fervor that football brings to these countries. From exuberant road celebrations to colorful pre-match customs, each viewpoint of the competition will be implanted with the social traditions of Morocco, Spain, and Portugal. The joint bid of Morocco, Spain, and Portugal winning the organization of the World cup 2030 guarantees a genuinely exceptional social involvement for everybody included. From investigating noteworthy cities and reveling in nearby cuisines to drenching oneself within the traditions and music of each nation, the competition will be a celebration of social differing qualities and solidarity. Football fans from around the world can see forward to not as it were seeing top-class matches but moreover being portion of an immersive social travel that will take off enduring memories

Part 3/5: 9 Hilarious Truths Only Ph.D. Students Will Understand

Anas Bedraoui, 9 mon.
Ph.D. Life: When Your Brain Expands, and Your Social Life Disappears! - Coffee Transformation: You start to believe that coffee is a basic human right. - Time Bender: Somehow, all your deadlines are 'tomorrow'. - Lab Maze Runner: You know your lab better than your own home. - Conference Life: You're there for the free coffee and snacks. - Email Excavation: Finding an old email feels like a treasure hunt. - PDF Collector: You have more unread academic papers than unread emails. - Thesis Magic: Turning random experiments into a thesis feels like a magic trick. - Jargon Juggler: You speak a strange language that only five people in the world understand. - Nap Ninja: Mastering the art of napping anywhere, anytime.

"Supervised and Unsupervised Learning in 90 Seconds of Reading"

Tima EL, 9 mon.
1 min read.
** Brief Definition : ** Supervised and unsupervised learning are two fundamental facets of machine learning, each specifically tailored to handle distinct types of data. In supervised learning, the machine learning algorithm is trained on a labeled dataset, where each data point consists of both input features and corresponding output labels. The goal is for the algorithm to learn the mapping from inputs to outputs based on these labeled examples. In unsupervised learning, the machine learning algorithm is trained on an unlabeled dataset to find hidden patterns, structures, or relationships within the data. Unlike supervised learning, there are no predefined output labels for the algorithm to learn from. ** Intuition 🙂 : ** In supervised learning, envision having a jigsaw puzzle featuring a picture of a dog, where each puzzle piece is labeled with its correct position in the completed picture. The model learns from these labeled examples, figuring out the relationships between the shapes and colors of the pieces and their correct locations.This process, often referred to as the training step, allows the model to internalize the patterns within the labeled data. Subsequently, after training, the model is adept at taking a new puzzle of a dog and precisely assembling it based on the knowledge acquired during the training process. Now, imagine you have a bag of puzzle pieces without a picture or labels — just a mix of colors and shapes. In unsupervised learning, the model explores the characteristics of the puzzle pieces without any predefined labels or information about the complete picture, identifying groups that share similar colors, shapes, or patterns. The model doesn't know what the complete picture looks like, but it discovers that certain pieces belong together based on shared features. These groups represent clusters of similar puzzle pieces. In this puzzle analogy, supervised learning entails constructing a model with labeled examples to tackle a specific task, while unsupervised learning involves the model autonomously uncovering patterns or relationships within the data without explicit direction.

Rock Lined Pockets

Taha Mrichi, 9 mon.
0 min read.
See the alarm in the shark’s cadence, Hear the sharp seagull’s cry: The merling king has come! The merling king has come! Jellyfish floating around his cloudy crystal crown Like translucent passive thoughts of aggression. Will he forgive your primate indiscretion? In his court of slime and rock sublime He beckons you with open tentacles to join his circle of hedonistic companions. The mermaid is a murderous creature. The dolphins are wanton and wild. What’s that in your pockets? The inquisitive mollusk asks. Rocks. Of the precious kind? No. His soft limbs curl back in plain disappointment. From under a shell a faint voice cautioned, Do not trust in the soft bodied rogue’s trade. Down here the written word is as fleeting as the spoken one.

Part 2/5: Humor in the Halls of Academia: A Light-Hearted Look at PhD Life

AnasBedraoui, 9 mon.
Here are some humorous and light-hearted "PhD" abbreviations: - Permanently head Damaged (PhD): A playful nod to the intense intellectual effort involved in earning a PhD. - Piled higher and Deeper (PhD): A humorous take on the depth and complexity of PhD-level research. - Patiently hoping for a Degree (PhD): Reflects the long and often uncertain journey towards completing a PhD. - Probably half Delirious (PhD): Acknowledges the stress and mental strain that can come with pursuing a doctorate. - Pizza hut Delivery (PhD): A fun twist, imagining a PhD as something entirely different. - Project half Done (PhD): For those times when it feels like the thesis will never be completed. - Philosophically Disturbed (PhD): A witty take on the deep and often complex thinking required for a PhD. These are meant in good humor and to bring a light-hearted perspective to the serious and commendable pursuit of a PhD.

Part 1/5: Why You Should Apply for a PhD Regardless of Your Background

AnasBedraoui, 9 mon.
Less than 2% of the world's population holds a doctorate degree. Do you aspire to be part of the average, or will you strive to join the ranks of these distinguished individuals? - Expanding Knowledge: Deepen expertise in your chosen field, enhancing critical thinking and problem-solving skills. Gain unparalleled understanding and push the boundaries of what's known. - Personal Growth: Develop resilience, independence, and management skills through challenging research projects. Cultivate self-discipline and adaptability, crucial for success in any endeavor. - Career Opportunities: Opens doors to advanced roles in academia (research, teaching) and industry (R&D, consultancy, management). Elevates your professional profile and broadens career prospects. - Networking: Connect with professionals and academics for future collaborations and career advancement. Build a valuable network of contacts that can support your career for years to come. - Contribution to Field: Make significant contributions to your field, influencing both academic research and industry practices. Your work could lead to new discoveries, innovations, or methodologies. - Inclusivity and Diversity: Encourages a mix of perspectives, challenging stereotypes and promoting inclusivity in academia and industry. Contributes to a more diverse and equitable professional landscape. - Professional and Personal Transformation: A PhD is a journey of both professional expertise and personal development, beneficial for all backgrounds. It's an opportunity to grow intellectually, professionally, and personally. - Leadership Skills: Develop leadership abilities by guiding research projects, mentoring students, and collaborating with various stakeholders. - Global Perspective: Gain exposure to international research communities, broadening your understanding of global challenges and solutions. - Recognition and Prestige: Achieve a level of recognition and prestige in your field, establishing yourself as an authority and thought leader.

"Understanding Overfitting and Underfitting in a Quick 90-Second Read"

Tima EL, 9 mon.
1 min read.
Overfitting and underfitting represent two common issues in machine learning that affect the performance of a model. In the context of overfitting, the model learns the training data too precisely, capturing noise and fluctuations that are specific to the training set but do not generalize well to new, unseen data. Underfitting, on the other hand, occurs when a model is enabled to capture the underlying patterns in the training data, resulting in poor performance not only on the training set but also on new, unseen data. It indicates a failure to learn the complexities of the data. **Analogy : ** Intuitively, returning to the example of the student that we presented in the definition of the machine learning concept, we discussed the possibility of considering a machine learning model as a student in a class. After the lecture phase, equivalent to the training step for the model, the student takes an exam or quiz to confirm their understanding of the course material. Now, imagine a student who failed to comprehend anything during the course and did not prepare. On the exam day, this student, having failed to grasp the content, will struggle to answer and will receive a low grade; this represents the case of underfitting in machine learning. On the other hand, let's consider another student who, despite having a limited understanding of the course, mechanically memorized the content and exercises. During the exam, when faced with questions reformulated or presented in a new manner, this student, having learned without true comprehension, will also fail due to the inability to adapt, illustrating the case of overfitting in machine learning. This analogy between a machine learning model and a student highlights the insightful parallels of underfitting and overfitting. Just as a student can fail by not grasping the course or memorizing without true understanding, a model can suffer from underfitting if it's too simple to capture patterns or overfitting if it memorizes the training data too precisely. Striking the right balance between complexity and generalization is crucial for developing effective machine learning models adaptable to diverse and unknown data. In essence, this educational analogy emphasizes the delicate equilibrium required in the machine learning learning process.