Predicting the future isn't magic, it's Artificial Intelligence.

Introduction: This time, I don't want to write the hurdles in fastbook and how to overcome them because once you move to notebook 6 or 7 it shows that you have the strong mindset to overcome by yourself and finish the book. Here after we have to practise what we learn by participating in Kaggle Competitions .  Problem statement: Identify the type of disease present in paddy leaf images. Understanding the dataset Labels are given in a separate csv file, train data set contains 10 folders each containing an image of paddy disease with respect to folder name, test data contain the images we have to predict. Model Implementation Once we Explore the dataset, the next step is to build a data block for the problem. We can create using two ways they are: We may map the label of the disease name to the image by csv file. Or we may label the data according to the folder name. What I chose is mapping the label of the images with respect to folder names. Once the data loaders are ready we can ...

Notebook_5 deep dive to Image classification problem and explain the mathematical concepts in an easy understandable code way. I don't want to explain the concepts in the notebook because Jeremy and Sylvian explained in an amazing way. What I am going to discuss is that I face difficulties in some areas you might come across the same issue. That's what we are going to discuss. Once the dataset is downloaded you may come across why we are using the Path.BASE_PATH = path ,if we don't do this means we are going to write the entire path of the directory. Path.BASE_PATH = path performs the same as pathlib module. Data block is like a blueprint for the model we are going to build. While building the data block you may come across an interesting library called regex, don't get intimidated and learn everything about regex, I am sure you may get that in the deep learning journey. Go with the flow learn what you want to learn like just know what it's like $ took the last digi...

Basics of deep learning Lecture 3 is about data ethics, It's self Explanatory and Rachel’s Lecture is more than sufficient.  Lecture 4 is more about breaking the myth behind neural networks. And the basics of pytorch and how fastai api makes deep learning cool. You may come across jargon like Sigmoid, Relu, Stochastic Gradient Descent, Learning rate and so on. Their explanation is great. They explained maths with a piece of code and I completely enjoyed it. What i would suggest is play with notebook 4 and learn more things. In case you don't understand something, go through it again and again and you will catch up. If you want some help ask the question in the forum. You can practise lecture 4 by downloading the repository and going to the clean folder, there is only code not with a prose. It's the best place to practise what you have learned till now. The motto is, If you are in doubt, run the code .

Hi Friends, What i am so excited about notebook2 is we are going to deploy a model, which helps to understand the flow of deep learning. I am going to discuss the problem I faced, may be you may face in your journey. First and foremost thing is you may not be able to get the bing api. If you are getting it, It's ok but if you face any difficulties means use DuckDuck go api which comes very much handy. Once you downloaded the dataset Everything worked fine until the deployment part.  What I would suggest is if you are using the colab like me means you may face difficulties in using viola. What I will suggest is to go for Gradio which is much easier. With an few line of code you may built a amazing application. My project is about classication of great ape(Chimpanzee, Bonobos and Gorilla) eg: In case you may face any difficulties go through my notebook . Feel free to ask the doubts.

  If you are new to deep learning or stucked like me in the middle of your journey to Artificial Intelligence.  There is no good source as compared to fastai. I like the practical approach of the fastai and the teaching of the Jeremy's Top Down approach. I don't want to write a blog about what are the concepts in lecture 1 because there are so many people in the fastai forum who are wonderful in explaining the concepts. If you have any doubt ask in the forum, There is no such thing as a good question or bad question everyone in the community is helping each other out. No one is going to judge you. I am writing this because you may also face the same problem like me and it may help you in the journey. For each notebook I am going to share my experience and this is about Notebook Lecture 1(Intro). I strongly suggest you watch Jeremy's lectures before going through the notebook. First oscillation in my mind is which platform I should use for the lecture whether it is google c...

Let me tell you something, I was studying machine learning and deep learning for 7 month. In this journey, Whatever i learned it gives me the feeling of emptyness. During the journey, I came across Fastai which is an high level api built on top of the Pytorch.  Many times , I started and end up of not completing the course due to various reason but this time, I was so commited to complete it. I don't want to write this blog for an expert in Fastai or an deep learning expert. This is for the person who are one steps behind me. I will be sharing my experience may be you may face in the journey. This is an Introduction, So i dont want to dump anything which myself feels annoying for the begginers. Speaking from the experience people in the fastai forum is really amazing whatever the doubt you may come across there is always some one to help you in the journey. I begin my deeplearning journey with various online courses dumping the knowledge without knowing what and how they actually u...

After learning the Sci-kit learn library, I started to think What, Where,  How should I start learning deep learning which library should go on, I find myself Tensorflow is easy to begin with because TensorFlow is developed by google and they providing a stable and extensive documentation and so on... While learning Image classification I came to know the power of GPU, Everything comes in colab which is very much handy. Main thing I learned about deeplearning is You can do anything as long as you can convert into object into Tensors (Array of numbers) Classification of 101 image of food data image data is done with EfficientnetB0 model using transfer learning. There I, learned the power of Transfer Learning. TensorflowHub providing various Pretrained model, we can use based on our need.  And there is something called Tensorboard whwere we can the evaluate the various model, we build with deeplearning. I attached the sample image of Tensorboard.  And there are so many reso...