MIT Introduction to Deep Learning (2023) | 6.S191

[@billhab1]

Hello, My name is Moro and am enjoying your class from Ghana. A big thank you to all the organizers of such intellectually simulating lecture series.

[@sarveshprajapati3878]

Thank you for making this amazing fast-paced boot camp on introduction to deep learning accessible to all!

[@SuperJAC1969]

This was an awesome and easy to follow presentation. Thank you. I have noticed that more and more professionals working in this field are some of the most lucid and eloquent speakers. Thanks again.

[@labsanta]

Takeaways: • [00:09] Introduction by Alexander Amini as a course organizer of Introduction to Deep Learning at MIT, alongside Ava • [00:42] The course will cover a lot of material in just one week and provide hands-on experience with software labs • [01:04] AI and deep learning have had a huge resurgence in the past decade, with incredible successes and problem-solving ability • [01:38] The past year has been the year of generative deep learning, using deep learning to generate brand new types of data that never existed before • [02:10] Introduction video of the course played, which was synthetically generated by a deep learning algorithm • [03:26] Deep learning can be used to generate full synthetic environments to train autonomous vehicles entirely in simulation and deploy them on full-scale vehicles in the real world • [04:03] Deep learning can generate content directly from the language we speak and imagine things that have never existed before • [05:04] Deep learning can be used to generate software and algorithms that can take language prompts to train a neural network • [06:40] Intelligence is the ability to process information to inform some future decision or action, while artificial intelligence is the ability to build algorithms that can do exactly this • [07:18] Machine learning is a subset of AI, which focuses specifically on teaching machines how to process data and extract features through experiences or data • [07:44] Deep learning is a subset of machine learning, which focuses explicitly on neural networks to extract features in the data to learn and complete tasks • [08:11] The program is split between technical lectures and software labs, with updates this year in the later lectures and guest lectures from industry and academia • [09:13] Dedicated software labs throughout the week will be provided, and a project pitch competition will be held on Friday, with significant prizes for the winners. • 12:13 - The speaker explains the fundamental building block of deep learning, which is extracting and uncovering core patterns in data to use when making decisions. • 15:11 - The speaker introduces the perceptron, a single neuron that takes inputs, multiplies them by corresponding weights, adds them together, applies a non-linear activation function, and outputs a final result. • 17:00 - The speaker uses linear algebra terms to express the perceptron equation as a vector and dot product. They also introduce the sigmoid function as an example of a non-linear activation function. • 18:04 - The speaker introduces more common non-linear activation functions, including the sigmoid function and the ReLU function. They explain the importance of non-linear activation functions in deep learning. • 19:28-19:53: Real world data is highly non-linear, so models that capture those patterns need to be non-linear. Non-linear activation functions in neural networks allow for this. • 21:01-21:35: A perceptron uses three steps to get its output: multiplying inputs with weights, adding the results, and applying a non-linearity. The decision boundary can be visualized as a two-dimensional line. • 23:11-23:39: A multi-layered neural network can be built by initializing weight and bias vectors and defining forward propagation using the same three steps as the perceptron. The layers can be stacked on top of each other. • 27:02-27:55: Each node in a layer applies the same perceptron equation to different weight matrices, but the equations are fundamentally the same. • [28:52] Sequential models can be defined one layer after another to define forward propagation of information from the layer level. • [29:18] Deep neural networks are created by stacking layers on top of each other until the last layer, which is the output layer. • [29:53] A simple neural network with two inputs (number of lectures attended and hours spent on final project) is used to train the model to answer the question of whether a student will pass the class. • [30:52] The neural network has not been trained and needs a loss function to teach it when it makes mistakes. • [32:16] A loss function is a way to train the neural network to teach it when it makes mistakes. • [33:22] A loss function can be referred to as an objective function, empirical risk, or cost function. • [34:29] Different loss functions can be used for different types of outputs, such as binary cross-entropy for binary classification and mean squared error for continuous variables. • [35:32] The neural network needs to find the set of weights that minimizes the loss function averaged over the entire data set. • [37:11] The optimal weights can be found by starting at a random place in the infinite space of weights and evaluating the loss function, then computing the gradient of the loss function to find the direction of steepest descent towards the minimum loss. Introduction to computing derivatives of functions across the space of weights using the gradient, which tells the direction of the highest point. Gradient Descent algorithm involves negating the gradient and taking a step in the opposite direction to decrease loss. Gradient Descent algorithm is initiated by computing the gradient of the partial derivative with respect to the weights, updating weights in the opposite direction of the gradient. The gradient is a line that shows how the loss changes as a function of the weights, and computing it is critical to training neural networks. Back propagation is the process of computing the gradient by propagating these gradients over and over again through the network, from output to input. Challenges in optimization of neural networks include setting the learning rate, which determines how big of a step to take in the direction of the gradient. Setting the learning rate too low may converge slowly or get stuck in a local minimum, while setting it too high may overshoot and diverge from the solution. One option is to try out a bunch of learning rates and see what works best, but there are more intelligent ways to adapt to the neural network's landscape. Adaptive learning rate algorithms depend on how large the gradient is in that location and how fast the algorithm is learning. • The Labs will cover how to put all the information covered in the lecture into a single picture that defines the model at the top [47:24] • For every piece in the model, an optimizer with a learning rate needs to be defined [47:24] • Gradient descent is computationally expensive to compute over an entire dataset, so mini-batching can be used to compute gradients over a small batch of examples [48:20-50:30] • Mini-batching allows for increased gradient accuracy, quicker convergence, increased learning rate, and parallelization [50:30-51:04] • Regularization techniques, such as dropout and early stopping, can be used to prevent overfitting in neural networks [51:41-56:19] Introduction to putting all information into a single picture for defining the model and optimizing the lost landscape with a learning rate. • [48:20] The idea of batching data into mini-batches for faster and more accurate computation of gradients using a batch size of tens or hundreds of data points. • [51:41] Discussion on overfitting and the need for regularization techniques such as Dropout and early stopping to prevent the model from representing the training data more than the testing data. • [56:45] The importance of stopping training at the middle point to prevent overfitting and producing an underfit model. • [57:12] Summary of the three key points covered in the lecture: building blocks of neural networks, optimizing systems end to end, and deep sequence modeling with RNNs and Transformer architecture.

[@carmyLOL-y6r]

thanks for this nick

[@RahulRamesh91]

Do you use any tools to take notes with timestamp?

[@labsanta]

@@RahulRamesh91 workflow 1. Open Transcript.txt 2. Write bullet points 3. Copy and paste in YT comments

[@God_is_real_iguess]

@@RahulRamesh91 chatgpt 😂

[@1guruone]

Hi Nick, Thanks for adding. Did you use AI-ML to generate? Regards.

[@thecoderui]

This is the first time that I have watched a course about Deep Learning. I want to say it is the best Intro for this topic, very organized and clear. I Just understanded about 75% of the content but I got what I need to know. Thank you

[@nikkione9901]

Thanks for making this video ❤

[@jimshtepa5423]

Great video! The MIT faculty has done an exceptional job of explaining deep learning concepts in a clear and understandable manner. Their expertise and ability to break down complex ideas into simple terms is impressive. It's evident that they are passionate about educating and inspiring the next generation of AI and machine learning professionals. Thank you for sharing this informative and engaging video. It's no surprise that it has received such positive feedback from viewers. Keep up the excellent work!

[@seanleith5312]

I stopped watch when he brought osama on, disgusting, never come back again.

[@vinayaka.b1494]

I'm doing computer vision research right now and love to watch these every new year.

[@melttherhythm]

Best course I've seen in a while! Super friendly to self-teaching. Thank you!

[@amitjain9389]

Hi Alex, Thanks for sharing the 2023 lectures. I've following your lectures from 2020 and these have helped me immensely in my professional career. Many thanks.

[@KarlyVelez-u2k]

Great Content!Informative, consice and easy to comprehend.What a time to be alive!. Thank you Mit allowing us to watch high quality teaching.

[@jazonsamillano]

I look forward to this MIT Deep Learning series every single year. Thank you so much for making this readily available.

[@AAmini]

Thank you!!

[@masternobody1896]

​@@AAminiI like ai

[@kushinvestment1851]

Alexander Amini, you're a gem! I'm taking Machine Learning course this semester and the course lecture is already finished but when I evaluate myself against course goals and how much I understand what Machine Leaning is in general, deep learning/Neural Network/ specifically I felt like I did not either attend the class or I'm not smart enough to know exactly what it does. Then, I directly ran to You tube and came across your great lecture and now I know what it is and I can apply to solve a real business world problem. I need to be honest with you guys this course lecture is really helpful and awesome to attend seriously. Indeed wonderful, easy and great takeaway of this semester for me! Thank you so much!

[@adbeelomiunu]

I never thought deep learning could be explained so plainly thought it had to be complex since it's called deep learning...but you did justice to this I must admit.

[@acornell]

Awesome lecture and really easy to digest in terms of content, speed, and taking the small moments to re-iterate or go back a bit to bring everyone up to speed. Less lingo == better for new students. Nice work

[@guruprakashram2868]

In my opinion, what makes a lecture either interesting or boring is not just the content of the lecture itself, but also the lecturer's approach to presenting the material. A good lecturer is one who is able to empathize with the students and present the information in a way that is easy to understand, making an effort to simplify complex concepts. This is what I believe makes a lecture truly worthwhile and enjoyable. Alexander did an outstanding job in making the lecture engaging and captivating.

[@AAmini]

Thank you! Glad you enjoyed it, next week will be even better 🙂

[@sriram.a1407]

@@AAmini❤

[@hassanjaved906]

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[@JeanLuemusic]

It's the student job to learn the fundamentals first. Learn how to walk before learning how to run.

[@ddaa-te6rz]

person perfect

[@techgarage7898]

its great Introduction lecture ever i seen , It covers our whole deep learning subject syllabus., Actually I am the Final year Student from India , Now I understand why my country has Low Quality Engineers .

[@wagsman9999]

After watching just a few KZbin videos I have a neural network running on my computer (Python), built from scratch, and no fancy libraries (except NumPy). Forward propagation, non-linear activation, backward propagation, gradient descent... maybe 50 lines of code... that's it. It was able to train itself to recognize handwritten digits (0 - 9) in a couple of minutes. I'm completely blown away - can hardly imagine what serious networks accomplish. Looking forward to this series for a deeper understanding.

[@vimukthirandika872]

Thanks to this Course and I startd my ML journey...Today I am doing ML Engineer Internship...Thank you MIT..

[@roba9189]

Thank you so much! This is the best explanation to deep neural networks that I could find on KZbin.

[@Chuspal]

I am 16 and from a rural part of South ASIA and I will be forever thankful for the resources available on KZbin thanks to IVY league Universities like MIT (6S191), Harvard(CS50) and others. Thanks you so much for making these resources publicly available for free of cost. I owe a debt of gratitude to all.

[@HilalShaath]

Alexander, I am a Kaggle expert ( 2 bronze one silver and counting). This lecture is the clearest explanation of deep learning that I came across, thank you so much for sharing this. I hope you are considering writing a book about the topic The clarity you explained this is remarkable. Best wishes for continued success

[@jamesannan4189]

Just perfect!!! Cant wait for more amazing lectures from you. Well done!!!

[@HuyNguyen-g1k7p]

Thank you for such incredible jobs and for making this available to everyone!

[@aghilannathan8169]

Actual legend for making all of this (lecture + labs + lab solutions) accessible and free.

[@sadiarashid7882]

Thank you so much!!! everything is so clearly explained and I finally understood how neural network works, stay blessed. 👏

[@woodworkingaspirations1720]

Beautiful presentation. Very clear and concise. Everything makes sense with just 1 "watch" iteration.

[@DBasedAlex]

I want to take a moment to applaud Alexander Amini for his clarity in speech and appropriate pacing. Many video series are impossible to watch on 2x speed because it’s simply hard to understand what they are saying, or they skip through slides in matters of seconds. This speaker does an amazing job of avoiding both.

[@technowey]

Thank you for posting this. I'm a retired electrical engineer who spend much of my career doing software. I'm excited and motivated, as well as concerned, by AI breakthroughs.

[@AlexTreyster]

Dear Alexander, thank you for your AI course on KZbin! It is the best among all of these on KZbin.

[@Dannydrinkbottom]

This is why I like MIT. Open Source Knowledge.

[@nehagupta2942]

hello Alexander sir...u just look like sushant singh rajpoot, he was a bollywood actor...ur lectures are just amazing..its like icecream...automatically swallowed...so easily understandable..

[@md.sabbirrahmanakash7083]

I started it today. I will be continuing with you Cause currently I have started a research work on image processing. Thank You

[@capyk5455]

Amazing delivery and presentation, thank you for sharing this material with us.

[@LeoLu-c2e]

As a new deep learning learner, I hope this video could help me learn efficiently.

[@micbab-vg2mu]

Thank you for the video - it is easy to understand even for not IT experts.

[@lantianyu1050]

The best intro to deep learning lecture I've ever heard! Thank you so much!!!

[@limuell.3421]

This is the best lecture I've seen in KZbin about deep learning.

[@mdmodassirfirdaus4528]

Thank you very much Professor to make this lecture series open to all. Thank you very much again from India

[@Mrnobody-qj7zl]

Hi @Alexander Amini I am a graduated student of Master's of Computer Science and Engineering from KUET, Bangladesh. I have my thesis on Protein Secondary Structure determination by RNN (LSTM & GRU). It took me lots of time and effort to understand the basics of NN. Moreover, I have a paper published on EICT 2021 on this field. However, today as I am watching your lecture, I found you made those complex explanations very easy. I really appreciate your work. I understand I have zero knowledge on NN but if there is a chance to work with you or any way to reach you, I would be very grateful to you. Thanks. Soumen Ghosh.

[@haodongzhu8347]

That sounds very aweaomeS!!! We can see deep learing is changing our world!

[@God_is_real_iguess]

Amazing, im 14 years old and i never thought id understand those concepts. But because of this great presentation I learned so many things and can even utilise some of the shown things. Thank you very much 🤗

[@sohumgautam5610]

Im 16 and I got some of it, but not all the math. How are you able to understand all of this?

[@God_is_real_iguess]

⁠​⁠​⁠@@sohumgautam5610I always try to visualize it being used in the "real" world. Additionally i have a coding background in Python and solved over 600 coding problems on codewars. It makes you think much more analytical and ofc familiar with basic concepts of math like sums (? the sigma notation thing with the k and stuff idk how its called in english, im german xD). But most of the theoretical math shown here was also new to me. As i already said i try to make sense of it somehow through stuff i learned by coding. If i dont understand something immediately i view it in context to what i already understood. So yeah thats basically how i managed to not be confused by some abstract math. Good thing about a video is that you can pause and think about it if you want to. Starting to learn how to code was probably one of my best decisions ever, so id advise you to start too or just keep grinding on whatever language you like 👍

[@yashoswal7899]

@Alexander Amini. Thanks for such an amazing video. I am currently pursuing my Masters and this video came at the very right time. Thanks once again for your work and publishing the material for students like us.

[@Nobody313]

I saw this content since 2018 and I always have learnt something new. Congrats and thank you so much.

[@oussamabouaiss7928]

One of the best courses I hv ever seen, congrats

[@sawfhsawfh00]

thank you so much Mr.Amini (ممنون از شما )

[@Lycoriste]

I was rejected by MIT last month. But hey, doesn't change the fact I can still learn from them :)) thanks for the lecture

[@NStillman]

Greetings from New Zealand. This is amazing. Thank you so much! So excited for these!

[@nikhilsharma1106]

The amount of effort that has been put into the presentation is highly commendable.

[@sanchaysat9944]

Hi! It is very interesting introduction video. Now I'm working in small company in my country as DS/ML specialist. It's helping me to approve my chances to get a job in foreign country and to be part of AI world. Thanks for sharing with us!

[@justinkim7202]

This lecture is exceptional. Keep them coming!

[@bingo242003]

The start of my learning in this field ! Wish me luck 🍀

[@dr.mikeybee]

Well done! These are the best descriptions of overfitting and regularization I've heard/seen. Your example of testing loss makes it clear why we take checkpoints. Every topic you cover has a great thought-provoking graphic, and each example is just right for the topic.

[@AdAstraCan]

Thank you for making this available.

[@sankalpvk18]

Thank you so much for making this course accessible for free. I feel so lucky today 🙏

[@yacubfahmilda9238]

What a handsome instructor! I can stand for hours sitting in your class.

[@aroxing]

The clearest explanation I've ever heard. Thanks!

[@alexanderinga4430]

Hello World!

[@abdalazezali8440]

Hello😊

[@subhrajyotibasu830]

Its not a hello world thing

[@HYPERFOCUSED-h3k]

Hello human!

[@utnapishtim307]

No

[@Abishek_Nair1999]

​@@utnapishtim307😂

[@Isysnation]

Thank you Mit allowing us to watch high quality teaching

[@jj2006h]

@AAmini thank you very much for a detailed master piece . i am watching this video repeatedly to understand each second. until 30 min , i am clear.

[@flimdejong2030]

Absolutely fantastic. Thank you!

[@aeronesto]

Such a well put together lecture! It was so easy to understand.

[@ayanah4821]

Omg everything makes sense! Your explanations were so simple and easy to understand 😭🙏

[@cassidaymoriarity]

It'd be nice if these lectures started off with a "What you should know before now"

[@dineshkhatri3859]

fantastic video, reaching an unknown part of the world. Love from Nepal.

[@CBMM_]

After watching many courses and reading books, I finally understood deep learning.

[@theinvisibleghost141]

this one lecture contains everything in depth.

[@HarpaAI]

🎯 Key Takeaways for quick navigation: 00:09 📚 *Introduction to Deep Learning Course Overview* - Introduction to the MIT Introduction to Deep Learning course. - Overview of the course content and structure. - Emphasis on the significance of deep learning and AI advancements. 02:01 🌟 *Impact of Deep Learning in Various Fields* - Deep learning's transformative impact on fields like robotics and medicine. - Introduction to the concept of generative AI. - Discussion of AI-generated content and its realism. 04:25 🧠 *Applications of Deep Learning in Language and Code Generation* - Deep learning's ability to generate content from natural language prompts. - Example of generating imaginative and novel images. - Mention of generating software code using deep learning. 07:04 🔑 *Fundamentals of Artificial Intelligence and Machine Learning* - Explanation of intelligence, artificial intelligence, and machine learning. - Introduction to deep learning as a subset of machine learning. - The focus of the course on teaching computers to learn tasks from raw data. 08:00 🗂️ *Structure of the Course and Labs* - Overview of the course structure, including lectures and software labs. - Announcement of project pitch competition and its focus on innovation. - Prizes for labs and competition winners and their significance. 09:32 🧪 *Hands-On Experience and Project Competition* - Details about software labs and their importance in reinforcing concepts. - Explanation of the project pitch competition and evaluation criteria. - Significant prizes and incentives for competition participants. 11:21 🙏 *Acknowledgment of Sponsors and Support* - Gratitude towards sponsors for their support in making the program possible. - Recognition of the importance of sponsors in continuing the program. - An essential shout-out to the program's sponsors. 12:48 🧠 *The Significance of Nonlinear Activation Functions* - Explanation of the role of nonlinear activation functions in neural networks. - Illustration of how nonlinear functions help in modeling real-world data. - Introduction to popular nonlinear activation functions like sigmoid and ReLU. 15:32 🧮 *Understanding Perceptrons and Their Functionality* - In-depth explanation of perceptrons as fundamental building blocks. - A breakdown of the perceptron's components: inputs, weights, bias, and activation. - Mathematical formulation and vectorized representation of a perceptron. 19:42 🔒 *The Role of Nonlinear Activation Functions in Neural Networks* - The necessity of nonlinear activation functions to handle non-linear data. - The example of separating data points using linear functions vs. nonlinear activation. - Highlighting how nonlinear functions make neural networks powerful. 21:05 🔍 *Understanding the Perceptron and Neural Network Basics* - Perceptron components: dot product, bias, non-linearity. 23:27 🧠 *Building a Single Layer Neural Network* - Defining parameters: weight vector, bias vector. - Forward propagation of information in a neural network layer. 26:20 🧬 *Stacking Layers to Create a Deep Neural Network* - Creating sequential models by stacking layers. - Deep neural networks as a hierarchy of layers. 30:10 📊 *Training a Neural Network and Loss Functions* - Introducing the concept of loss functions. - The importance of training neural networks and minimizing losses. 35:18 📉 *Gradient Descent and Backpropagation* - Overview of gradient descent as an optimization algorithm. - Introduction to backpropagation for computing gradients. 41:41 🧮 *Back Propagation Algorithm* - Back propagation is the core algorithm for training neural networks. - It involves calculating gradients and recursively applying the chain rule. - The goal is to determine how changes in weights affect the loss function. 43:07 💡 *Challenges in Training Neural Networks* - Training neural networks can be complicated in practice. - Optimizing large deep neural networks involves dealing with complex landscapes. - Setting the learning rate appropriately is challenging. 44:35 🔄 *Adaptive Learning Rates* - Adaptive learning rate algorithms adjust the learning rate based on the gradient's magnitude. - Finding the right learning rate is essential for efficient training. - There are intelligent ways to adapt learning rates to the neural network's landscape. 47:23 🛠️ *Training Process Overview* - The training process involves defining the model, optimizer, and learning rate. - Gradients are computed and used to update the model's weights iteratively. - Parallelization and batching are crucial for efficiency in training. 48:19 🔢 *Mini-Batch Gradient Descent* - Mini-batch gradient descent improves efficiency and accuracy in gradient computation. - It strikes a balance between stochastic gradient descent and full-batch gradient descent. - Batch sizes of tens or hundreds are commonly used in practice. 50:40 🛡️ *Regularization and Overfitting* - Overfitting occurs when a model fits the training data too closely and doesn't generalize well. - Regularization techniques, like Dropout, discourage overly complex models. - Early stopping is another method to prevent overfitting by monitoring test loss. Made with HARPA AI

[@ramanraguraman]

Thank you Sir. I appreciate you from bottom of my heart for your services.

[@marktahu2932]

You are so clear and the topic is presented so effectively - in one foul-swoop you put in plain language what I have been using CHATGPT for, so many pennies have dropped and lights went on - thank you.

[@yousefabdelnaby3555]

thanks so much for your great explanation and before that for sharing the knowledge for all!

[@king069-xr3hm]

THANKS CANT BELIVE I UNDERSTOOD EVERTHING IN FIRST GO WHAT A GREAT TEACHER

[@monsineenakapanant4993]

Thank you for your wonderful explanation.

[@spacecowboy7549]

Great study material for the beginner of deep learning

[@Djellowman]

Happy to say i knew everything that was discussed in this video! Looking forward to the next one

[@niazizarif3810]

Proud! very well done. Mofaq bashi brother

[@Narang_Deepak]

Great Content! Informative, consice and easy to comprehend. What a time to be alive!

[@VijayasarathyMuthu]

The structure of the course 🔥

[@hassal4585]

Thanks I have learned a lot from your classes!

[@supergooglestar]

I really loved your lecture. Your lecture is so easy to understand. Thank you for posting this on KZbin

[@farzanehheidari8190]

Awesome. It was super clear and I just understand some terms that I thought they are very difficult to learn. Thank you 👍🏻

[@abdullahiabdislaan]

Finally the wait is ended

[@SyBa-SyKo]

G'day from Australia! 🤩 What a ride on generative AI atm! That is what led me here. It is an unprecedented time in human history and I simply must be a part of it! Thank you so much for making this course available online. What an amazing time to be alive!! ❤

[@tascker0]

This is the most brilliant and encouraging material that is on youtube. Many thanks for making this - great job. (Chat GPT corrects me).

[@oziomagospel9685]

Hi am ozioma. Enjoying your class from Nigeria. More power to you

[@mustafaalawi6242]

Hi Alex, I have seen several lectures from instructors from different universities around the world. One of the most excellent points that your lectures grabbed so much attention and found to be helpful for a much broader community is the point that how amazingly you connect the dots between theory and its actual application. For example, the codes that are provided correspondingly to each concept make it super easy to understand very complex topics and make your lecture unique. Thanks for making it available to everyone. Cheers, Mustafa

[@jacobsmith4284]

Never thought I’d ever be a fly on the wall at MIT

[@circuitlover853]

Thanks for the great lecture , Mr. Alexander

[@28nov82]

Thanks for making this introduction session!

[@ibrahimhasan6619]

Thanks a lot Alexander! You are doing great! So excited to watch future lectures.

[@muratdagdelen8163]

You are awesome. Thank you very much.

[@obeytweety]

How is all of this free?! Thank you so much for this.. I wish to work with this technology someday...

[@MicahBratt]

It’s been very impressive watching this field progress over the years.

[@ok-yg3yb]

one of the most important things students need to get experience in.

[@AlexandrBorschchev]

Given its huge relevance today, I agree

[@qbitsday3438]

The Meaning of "weights" and "Biases" should be explained too with examples , example = weight is like electrical resistance...etc

[@riyaprakash6000]

Very informative and precise. Thank you very much.

[@neuralclass]

Following this course since past 3 years.You are an amazing instructor!

[@Eric-zo8wo]

0:58: 🎓 Introduction to Deep Learning is a fast-paced program that covers a wide range of material in just one week. 12:07: 🔑 Deep learning allows machines to uncover core patterns in data, eliminating the need for hand-engineered features. 23:22: 🧠 A perceptron works by taking the dot product of inputs, applying a bias, and passing the result through a non-linear function. 35:31: 🧠 To train a neural network, we need to find a set of weights that minimizes the loss function across the entire dataset. 47:02: 💡 The Labs provide the opportunity to try different adaptive algorithms and understand their patterns and benefits. The model is defined at the top and an optimizer with a learning rate is needed for each piece. Batching data into mini batches is a powerful technique for training neural networks. Recap by Tammy AI

[@amazlin8271]

To search the ideale weights Who minimize the Cost function,you should look for the point Who minimize the Cost function , in this case : min (gradient(w))=0

[@dataha__sh]

This introduction is compact & I assume this is perfect for the beginners & intermediates to gain utmost clarity on neural networks & deep learning.... Loved the Great lecture 💯🔥

[@MALAYAPH24]

Thank you so much for a wonderful lecture. Indeed helpful to understand AI.