How I evaluate data science candidate?

• Different business problems solved and their ML lessons learned, Deep Dive on Implementation, Algo used, Features Evaluated
• Data pipeline set up and challenges faced
• How do you keep track of new papers / evaluating and learning different frameworks
• How much do you code on a daily basis for work / personal learning
• Ability to bring different perspective/techniques solving problems

The field is evolving on a daily basis. We need passionate, curious learners and experimentation mindset!!!

Day #236 - Save Keras in Tensorflow pb format

This project was useful for conversion from Keras to Tensorflow pb format

Command
python keras_to_tensorflow.py --input_model="path/to/keras/model.h5" --output_model="path/to/save/model.pb"

Example
python keras_to_tensorflow.py --input_model="D:\\classification_3.h5" --output_model="D:\\classification_3.model.pb"

Happy Learning!!!

2.0 Lifestyle Skills

To survive we need a newer set of skills and a better awareness about yourself

• Building Culture of Learning
• Training and Experimenting Mindset
• Emotional and Communication Skills
• Fail and Learn Mindset
• Balance Attitude dealing with Depression, Life Struggles

Happy Finding Yourself!!!

Finding Great Candidates

• Communicate at the simplistic level
• Create end to end experiments than certifications
• Rely on passion, Consistent learning and good team players
• Look for people who intend to make a change, consistent performance matters 
• Move out of puzzles, programs. Project or a prototype that requires reasonable design, code, use cases, an end to end implementation matters
• Puzzles and program can find a good coder but doing end to end projects requires more skills than just coding
• People who share what they learn can impact a change in culture than people who work in silos
• Great Skills takes years, Passionate about technology to see how it evolves matters

Happy Learning!!!

Day #235 - Audio Analysis

Happy Mastering DL!!!

Day #234 - NLP with Deep Learning | Winter 2019 | Lecture 1

Key Lessons

• Get better in finding words that make them feel less alone
• Writing is ability to communicate knowledge, Knowledge sent to places
• Writing is 5000 years old
• Meaning - Expression for Idea, Art, Writing

• Use NLTK for Synonyms and Hypernyms
• Wordnet fine distinction between senses of word
• Words represented as one hot vectors
• Building word similarities tables to map to similar words
• Dense Vector - Word Embeddings Representation

Word2vec

• Framework for learning word vectors
• Every word represented by vector
• c - center word, o - context outside word
• Calculate the probability

• Similarity between words the orange part
• Exp turn positive or negative into number

Maths

• Calculus Chain Rule
• Vector Dot product
• Multivariate calculus

Happy Mastering DL!!!

Day #233 - Tensorflow 2.0 notes

Summary of Notes

• Adopted Keras for high level API, tf.keras
• Common Pieces for - layers, models, optimizers
• Keras - Pythonic and Easy to learn
• For Larger Scale data, Estimators used - For Fault Tolerance
• Estimators are powerful machines, All estimators moved to keras
• 1.0 - No Session, 2.0 Eager mode
• Graphs even in eager context
• Eager execution is a way to train a Keras model without building a graph
• One set of Optimizers, Full Serializeable
• Losses consolidated into single set
• RNN layers update in Tensorflow, Unified RNN layers
• Tensorboard for Performance profiling, Model performance
• tf.distribute.Strategy API - Designed to handle many distribution architectures (Multi-Gpu)

To Update
pip install -q tensorflow==2.0.0-alpha0



Happy Mastering DL!!!

Day #233 - Pytorch Examples

Happy Mastering DL!!!

Day #232 - Kafka + Spark Integration - Big Data Setup - Part I

Experimenting with Kafka and Spark using Pyspark

Example 1 - Kafka Publish - Consume
Example 2 - Kafka Publish - Spark Consume

Happy Learning!!!

Day #231 - Evaluating Existing Pytorch - ReId - Models

On Ubuntu System

• Download Market 1501 Dataset - Link
• Download Code from Link
• Comment CUDA References 

• Run the code

Happy Mastering DL!!!