Thanks for your course, what I want to ask is whether you can upload the pratice course file or related document to website etc. It maybe help for all of those who want to follow the course and do some practices. Many thanks!

AMAZING lOVED THIS WAY OF EXPLAINING THE NEURAL NETWORKS

LITERALLY ALL US UNIVERSITIES ARE SEGREGATION AND SEXISM CENTRAL

AI for Science is rapidly transforming the landscape of research across multiple scientific fields by enabling more efficient data analysis, accelerating discovery, and offering new methods of problem-solving. Machine learning algorithms, particularly deep learning and reinforcement learning, are being harnessed to tackle complex scientific challenges that were once thought to be beyond computational reach. In fields like biology, AI is revolutionizing drug discovery by predicting molecular interactions, identifying potential drug candidates, and speeding up the process of designing personalized treatments. In astronomy, AI models analyze vast amounts of data from telescopes and space missions, helping scientists detect exoplanets, study galaxy formation, and understand cosmic phenomena. AI is also playing a critical role in climate science, where it helps model and predict climate patterns, evaluate environmental risks, and develop solutions for sustainability. In chemistry, AI is being used to predict chemical reactions, optimize synthetic routes, and design new materials with specific properties, such as advanced batteries or carbon capture materials. In physics, AI aids in simulating complex systems like quantum phenomena or high-energy particle collisions, assisting scientists in deriving insights that would be impossible through traditional approaches. One of the most exciting prospects of AI in science is its potential to drive interdisciplinary innovation, bringing together insights and methodologies from various fields to solve problems in novel ways. However, the integration of AI into scientific research also raises concerns related to ethics, data privacy, and the interpretability of AI-driven results. It is crucial for researchers to ensure that AI models are transparent, reliable, and used responsibly, particularly when making decisions with wide-reaching consequences, such as in healthcare or environmental policy. Overall, AI for science has the potential to dramatically accelerate progress across disciplines, uncovering new insights and opening doors to discoveries that could lead to groundbreaking innovations in technology, medicine, and sustainability.

Building AI models in the wild refers to the process of deploying and training artificial intelligence models in real-world, unstructured environments where data may be noisy, incomplete, or constantly changing. Unlike controlled laboratory settings where data is curated and processes are streamlined, "in the wild" implies a much more dynamic and unpredictable scenario. This often involves gathering data from diverse sources like sensor networks, social media platforms, IoT devices, or human interactions, and deploying models that must adapt to these conditions in real-time. Building AI models in the wild presents unique challenges, including data privacy concerns, dealing with biases inherent in real-world data, and ensuring model robustness in diverse and unpredictable environments. For example, AI models trained on data from one geographic location or demographic may perform poorly when applied elsewhere, highlighting the importance of generalization and fairness. Furthermore, models must be designed to handle noisy, missing, or inconsistent data while still making accurate predictions or decisions. Despite these challenges, there are significant opportunities in deploying AI in real-world settings. From autonomous vehicles navigating busy streets to AI-powered healthcare solutions that assist in diagnosing conditions from diverse patient populations, real-world AI models are enabling transformative applications. Key to success in building AI models in the wild is the continuous feedback loop, where models are updated and retrained based on new data and experiences. Ultimately, the goal is to build AI systems that are both resilient and adaptable, capable of learning and improving in real-time while being mindful of ethical considerations such as privacy, fairness, and transparency. As AI technology evolves, building models in the wild will continue to push the boundaries of what is possible, driving innovation in fields such as transportation, healthcare, and urban planning.

Generative AI for media is transforming the way content is created, distributed, and consumed. By leveraging deep learning models like Generative Adversarial Networks (GANs) and transformers, AI can now generate highly realistic images, videos, music, and text, offering new opportunities for creativity and innovation. In the media industry, this technology has been used to produce synthetic news articles, generate photorealistic visuals, create deepfake videos, and even compose original music, all with minimal human intervention. In filmmaking and video production, generative AI can automate tasks like video editing, scene generation, and special effects, drastically reducing production time and costs. AI-powered tools can help artists create visual effects that were once labor-intensive, while also enabling more personalized content tailored to individual preferences or cultural trends. Similarly, in music production, AI algorithms can assist in composing new tracks or generating background scores that match specific moods or themes. However, the rise of generative AI in media also raises ethical and societal concerns. Deepfakes, where AI is used to create hyper-realistic but fake videos, have sparked debates over misinformation, privacy violations, and the potential for manipulation. Furthermore, the ability of AI to generate content without a human creator challenges traditional notions of authorship, copyright, and creativity. These issues highlight the need for responsible AI deployment and governance, ensuring that generative media is used ethically and transparently. Despite these challenges, generative AI holds immense potential for revolutionizing media production by enhancing creative expression, enabling more diverse forms of content, and even personalizing media experiences for individual users. With proper regulation and ethical considerations, AI can become a powerful tool that reshapes the future of media, making it more accessible, innovative, and dynamic.

Language models have undergone significant advancements in recent years, pushing the boundaries of what artificial intelligence can achieve in natural language processing (NLP). These models, particularly deep learning-based architectures like **transformers**, have made it possible for AI to understand, generate, and even reason with human language at an unprecedented level of sophistication. By learning from vast amounts of text data, language models can perform tasks such as translation, summarization, sentiment analysis, and content creation with impressive accuracy. Models like GPT (Generative Pretrained Transformer) have shown remarkable abilities not only to generate coherent and contextually appropriate text but also to answer questions, hold conversations, and even solve complex problems across various domains. These breakthroughs have opened new frontiers in fields such as automated customer service, education, content creation, and healthcare, where AI can assist in generating insights, drafting documents, or providing tailored information to users. However, as language models evolve, new challenges and ethical concerns arise. The potential for bias in generated content, misuse in spreading misinformation, and the lack of interpretability in how these models arrive at conclusions are important issues that need to be addressed. There are also concerns about their environmental impact, as training such models requires vast computational resources. Despite these challenges, the continuous evolution of language models offers exciting possibilities. Future developments in multimodal models that integrate text, images, and other forms of data could lead to even more powerful AI systems. Additionally, with improvements in efficiency, fairness, and transparency, language models will likely continue to reshape how we interact with technology, opening up new avenues for human-AI collaboration and enhancing fields ranging from research and creativity to healthcare and law.

Reinforcement Learning (RL) is a type of machine learning where an agent learns to make decisions by interacting with an environment, aiming to maximize cumulative rewards over time. Unlike supervised learning, where the model is trained on labeled data, RL focuses on trial and error. The agent takes actions in the environment, receives feedback in the form of rewards or penalties, and adjusts its actions to improve future outcomes. This process involves exploring different strategies (exploration) and exploiting known strategies (exploitation) to find the most effective solution. RL is widely used in areas such as robotics, game playing, and autonomous systems. Notable examples include AlphaGo, the AI that defeated the world champion in the game of Go, and self-driving cars, where RL helps the vehicle learn optimal driving policies. In robotics, RL allows machines to learn complex tasks like grasping objects, navigating environments, or interacting with humans. A key strength of RL is its ability to solve problems with sparse or delayed rewards, making it suitable for applications where the consequences of actions are not immediately apparent. However, RL also faces challenges, such as high computational costs, the need for vast amounts of training data, and difficulties in ensuring safety and stability in real-world applications. Despite these challenges, RL continues to advance, with recent developments focusing on improving sample efficiency, enabling multi-agent systems, and addressing ethical concerns such as fairness and transparency. As research progresses, RL has the potential to revolutionize fields such as healthcare, finance, education, and more by enabling autonomous systems to learn and adapt to complex environments.

Deep Generative Modeling refers to a class of machine learning techniques that focus on learning the underlying distribution of data to generate new, synthetic samples that resemble the original data. Unlike discriminative models, which aim to classify or predict specific outcomes, generative models seek to understand and replicate the structure and patterns of the data itself. These models have gained significant attention for their ability to generate realistic data, whether it's images, text, music, or even complex 3D shapes. One of the most prominent techniques in deep generative modeling is Generative Adversarial Networks (GANs), which consist of two networks: a generator that creates fake data and a discriminator that tries to differentiate between real and fake data. Over time, through adversarial training, the generator improves to the point where it produces data indistinguishable from real data. Another important approach is Variational Autoencoders (VAEs), which model the probability distribution of the data and can generate new samples by sampling from the learned latent space. Deep generative models have proven particularly powerful in tasks such as image synthesis, data augmentation, style transfer, and even drug discovery, where new molecular structures can be generated based on known patterns. In the field of creative arts, these models have been used to generate realistic art, music, and writing, blurring the lines between human creativity and AI. However, deep generative models also face challenges. They often require vast amounts of training data and computational power, and controlling the quality of generated data (e.g., avoiding artifacts or unrealistic outputs) remains a significant hurdle. Additionally, interpretability and ethical concerns-such as the potential for misuse in creating deepfakes or biased data-are important areas of ongoing research. Despite these challenges, deep generative modeling continues to open up new frontiers in AI, with potential applications across diverse industries like entertainment, healthcare, and manufacturing.

Convolutional Neural Networks (CNNs) are a class of deep learning models that have revolutionized computer vision and image processing. Inspired by the structure of the visual cortex, CNNs are designed to automatically and adaptively learn spatial hierarchies of features from input images, making them exceptionally effective at tasks like image classification, object detection, facial recognition, and medical image analysis. CNNs operate by applying convolutional layers to input data, where small filters (or kernels) slide over the data to detect local patterns like edges, textures, and shapes. These patterns are then combined in deeper layers to recognize more complex structures. Pooling layers are used to reduce the spatial dimensions, making the model more computationally efficient while preserving important features. The final layers often consist of fully connected layers that interpret the features and make predictions. The power of CNNs lies in their ability to learn features directly from raw data, eliminating the need for manual feature engineering. This has led to significant breakthroughs in fields ranging from autonomous driving, where CNNs are used to detect objects on the road, to healthcare, where they assist in analyzing medical scans like MRIs and X-rays to detect abnormalities like tumors. However, CNNs are not without challenges. They require large amounts of labeled data and significant computational resources to train effectively. Overfitting can also be a problem when the model becomes too specialized to the training data. Despite these limitations, CNNs continue to be one of the most powerful tools in machine learning, with ongoing research focused on improving their efficiency, generalization, and interpretability for even broader applications.

Deep sequence modeling is an advanced technique in machine learning that focuses on understanding and predicting sequences of data, such as DNA, protein sequences, time series, and text. This approach applies deep learning models, particularly recurrent neural networks (RNNs), long short-term memory networks (LSTMs), and transformers, to capture complex dependencies within sequences and make predictions about future elements or classifications. In biological contexts, deep sequence modeling has shown remarkable success in areas like genomics and proteomics, where it can predict the structure, function, and interactions of proteins from their amino acid sequences or analyze genomic sequences to identify mutations related to diseases. It has revolutionized areas like drug discovery, where it helps predict the binding affinity of molecules or anticipate the behavior of specific genetic sequences under various conditions. Beyond biology, deep sequence modeling is widely used in natural language processing (NLP) to understand human language, enabling applications like language translation, speech recognition, and sentiment analysis. The ability of deep sequence models to learn from vast amounts of sequential data without the need for hand-crafted features makes them incredibly powerful, often outperforming traditional methods in terms of accuracy and efficiency. However, challenges remain in deep sequence modeling, particularly in managing the vast amount of data required for training and improving model interpretability. As the field advances, methods to enhance the computational efficiency of these models, while maintaining or improving performance, will open the door to even more sophisticated applications, particularly in personalized medicine, genomics, and other data-intensive fields.

Deep learning, a subset of machine learning, has become one of the most transformative technologies of the 21st century. It involves the use of artificial neural networks-complex algorithms inspired by the human brain’s structure and function-that can learn from large amounts of data. Deep learning models are capable of recognizing patterns, making decisions, and improving their performance over time without explicit programming. These models have led to breakthroughs in fields such as computer vision, natural language processing, speech recognition, and autonomous systems. One of the key strengths of deep learning is its ability to process vast amounts of unstructured data, such as images, audio, and text, making it particularly useful in tasks that involve complex, real-world input. For example, deep learning powers advancements in facial recognition, language translation, medical image analysis, and even creative applications like art generation and music composition. However, deep learning is not without challenges. It requires massive amounts of labeled data and computational resources to train effectively, which can be expensive and time-consuming. Additionally, deep learning models can sometimes be opaque, meaning they lack transparency in their decision-making processes, raising concerns about trust and accountability in critical applications. Despite these challenges, deep learning continues to drive innovation and is poised to further revolutionize industries ranging from healthcare to finance, transportation, and beyond. As research progresses, improvements in model efficiency, interpretability, and generalization will likely unlock even more powerful applications and help address current limitations.

OMG, it's so intuitive !🤩

Make "MORE" of these videos Alexander. I appreciate your effort. Lots of love from Nepal.💝💝😘😘

thank you so much. the explanation on self-attention is so clearly

ATTENTION | NOITNETTA

The website aint working since a few days :/

Another great attention explanation: kzbin.info/www/bejne/haqpe4qIo9mSd7s&ab_channel=PascalPoupart

Can we get the slides? The slides are not present at the link present in the description here.

As of November 2024, the example used in this talk, "What's the shape of the red object", can be easily solved by LLMs with vision capabilities. Do LLMs use neurosymbolic approaches or the way to think about the limitation of non-symbolic deep learning was just flawed?

Thankyou Alex, this was really a great foundational course on Neural Networks. Will continue with other uploads in this series.

Thanks for Sharing this course and thanks for making it so simple to understand

Thanks for the content, as an FYI the URL is flagged as dangerous and not secure, perhaps the certificate has expired. ANyway, it may be impacting your bounce rate. Thanks again

Nothing to say except thank you 😌😌😌😌

Too long useless intro…

really very informative.....

Very nice

INCREDIBLE CONTENT, THANK MIT AND ITS INSTRUCTORS

Great Instructor but it will be more impactful if u would use white board or blackboard in this case and explain the convolution using some drawings

Alot of talk about "artificial intelligence" from people who cant define actual intelligence. At the same time building systems that deliberately consume an terminal amount of energy. Literally planning the extinction of the earth like it's a casual thing. Academics= idiots Get out there in the world and look at the consequences of all your self ritgheous ego maniacal "science". Scientist, researchers and engineers are never held accountable for their creations.

Another academic with no idea what he's doing. But still doing it!

Can I use this video for my website

Fabulous efficiency

amazing lecture !

Briliant Ava. Explained one of the most complex concept GAN, cycle GAN brilliantly.

who can tell me some book to leran the basic of neural networks

No words to salute for exceptional lecture kn Deep learning, its one of the best lecture in my career, hat's off your awesome skills ❤

The best Introduction to Deep Learning ever!

00:04 Introduction to MIT 6.S191 - Deep Learning Course 03:02 AI's realism in generating hyperrealistic content 07:36 Teaching machines to process data and inform decision-making abilities 10:02 Introduction to foundations of neural networks and upcoming guest lectures 14:22 Introduction to Deep Learning Paradigm Shift 16:38 GPUs and open source tools drive deep learning advancements 20:42 Different types of nonlinear activation functions in neural networks 22:32 ReLU activation function introduces nonlinearity in neural networks 26:30 Sigmoid function divides space based on input value 28:25 Neural networks have millions or billions of parameters, making visualization challenging 32:26 Building a single layer neural network is simple and modern deep learning libraries provide tools to easily implement it. 34:32 Introduction to two-layered neural network with weight matrices 38:22 Building a neural network to predict class performance based on lecture attendance and project hours 40:19 Neural networks need to be trained with data and feedback to make accurate predictions 44:08 Training neural networks involves finding the weights that minimize loss. 46:14 Gradient descent helps find local minimum by updating weights. 50:05 Computing gradients for weights in neural network 52:02 Overview of forward and back propagation in neural networks 55:43 Setting adaptive learning rates to navigate minima and maxima 57:34 Training neural networks involves optimizing weights with billions of dimensions efficiently. 1:01:22 Mini batches offer faster convergence and parallel computation 1:03:24 Overfitting and underfitting in machine learning 1:07:08 Monitor loss during training to prevent overfitting 1:08:57 Fundamental building blocks of neural networks fitonear.com

been softmaxxing since this one

How fascinating is it i wanted to learn about neural networks and just searched neural networks mit and found a course thankyou so much youtube and MIT.

What are the system requirements?

Even a child and Obama can understand, elemental, my dear negro

Thank you for an amazing lecture, easy to follow a complex topic.

I watched it a few times, but i finally god it! And it's a great resource. thank you.

"4. LLM results may be racist, unethical, demeaning, and weird" Is Trump an AI?

Thanks for this great lecture series. However the audio is muffled at some points

It seems to me that the data comprising the KEY matrix introduces a large external bias on the QUERY matrix, or am I mistaken? thx

what is the outcome of having just a single weight per neauron regardless the number of inputs, would it still learn? as it seem, the weight increases exponentially as the number of inputs... i.e a single node with 3 input will have 3 weight, why not just single weight?

ما مرده های متحرکیم ، که تایمش فعاله . تیک تاک ، تیک تاک 😂