Data Science Tips for Beginners!

Deep Learning - Coursera Course Notes Deep learning is a subset of machine learning in artificial intelligence (AI) that has networks capable of learning unsupervised from data that is unstructured or unlabeled. Also known as deep neural learning or deep neural network. Deep learning is used across all industries for a number of different tasks. Commercial apps that use image recognition, open-source platforms with consumer recommendation apps and medical research tools that explore the possibility of reusing drugs for new ailments are a few of the examples of deep learning incorporation. 👉 Click Here to Download the Notes! 👈

Python is my most favorite tool for Data Science. Learning Python will definitely help you in your Data Science journey. Here is a great 63 pages comprehensive Python document that should help you. All the best! 👍🏻 👉 Click Here to Download the Book! 👈

The good news is that there are countless tutorials and helpful reference guides all over the internet, ripe for perusal. But with the sheer amount of content to sift through, you’re bound to find a few bad apples. Instead of going through every AI video tutorial out there, let us do some of the work for you. Abhishek Thakur( https://www.youtube.com/user/abhisheksvnit/about ) Krish Naik ( https://www.youtube.com/user/krishnaik06 ) Siraj Raval ( https://www.youtube.com/channel/UCWN3xxRkmTPmbKwht9FuE5A/about ) Simpli Learn ( https://www.youtube.com/user/Simplilearn ) Sentdex ( https://www.youtube.com/user/sentdex ) Lauis serrano ( https://www.youtube.com/channel/UCgBncpylJ1kiVaPyP-PZauQ )

I would recommend participating in competitions that are more suitable for beginners: Binary Classification:  Titanic: Machine Learning from Disaster Multi-class Classification:  Forest Cover Type Prediction Temporal Regression:  Bike Sharing Demand Conv Nets:  Digit Recognizer Denoising Autoencoders:  Denoising Dirty Documents Sentiment Prediction:  Sentiment Analysis on Movie Reviews Word2Vec:  Bag of Words Meets Bags of Popcorn Elite Tutorial:  elitedatascience Training Tokenizers:  https://www.kaggle.com/funtowiczmo/hugging-face-tutorials-training-tokenizer Introduction to Transformers:  https://www.kaggle.com/funtowiczmo/hugging-face-transformers-get-started Transformers Pipelines:  https://www.kaggle.com/funtowiczmo/hugging-face-transformers-how-to-use-pipelines Enjoy learning data science!

I will highlight some of the key changes with the latest version of Tensorflow 2.0 compared to earlier versions: Now Keras users who use multi-backend Keras with the TensorFlow backend are needed switch to tf.keras in TensorFlow 2.0. As it’s going to be better maintained. By default Tensorflow 2.0 will be having Eager execution that evaluates operations immediately, without building graphs. You can obviously switch to the style of building graphs, as it was in Tensorflow earlier as well It is now having a full lower-level API, which allows it to use for having custom training loops(which is needed by researchers a lot) With tf.GradientTape you can now do automatic differentiation One of the other features of Tensorflow is experimental support for mixed precision on GPUs and Cloud TPUs A clear consistent, intuitive, syntax across various APIs Tensorflow 2.1 bought support for TPU training in a simple and intuitive way which makes it’s a favourite for Kagglers like me Internal

Good news to those who are struggling to get past interviews! Here is the compiled counter moves for four of the most common interview questions. 1. Can you tell me a bit more about yourself? 2. What kind of value can you bring to this role? 3. Where do you see yourself in 5 years? 4. Why do you want to leave your last role? →  Click Here   ← to Access the PDF file.

→  Click Here   ← to Access the PDF file.