Learning new Tools and Technologies

My self Vineel Kumar Polavarapu, a passionate and driven postgraduate in the steam of Computer Applications with a robust technical foundation and a keen interest in cutting-edge technologies. With proficiency in Python, SQL and Data Structures & Algorithms (DSA), I’ve developed a versatile skill set tailored for the dynamic demands of today’s tech industry.
My hands-on experience spans across powerful Python libraries and frameworks such as TensorFlow, NumPy, Pandas, and Matplotlib, which I’ve leveraged to build real-world projects involving deep learning and data analysis. I had the opportunity to intern at Coincent.ai, an innovative firm affiliated with Microsoft, where I deepened my practical understanding of AI-driven solutions.
I thrive in fast-paced environments that challenge my logical reasoning, problem-solving abilities, and communication skills. I am deeply motivated by the evolving landscape of the IT world and always eager to explore new tools, tackle complex problems, and contribute meaningfully to impactful projects.
I’m now ready to begin the next chapter of my professional journey bringing enthusiasm, innovation, and technical excellence to every opportunity I embrace.
PROJECTS
• Built a deep learning model to generate images from text using Bi-LSTM RNNs and CNNs in Python. Text features were extracted via TF-IDF and processed through Bi-LSTM, then mapped to images using CNN layers.
• Trained on the Flickr Text-to-Image dataset with an 80/20 split, achieving up to 98% accuracy. Enabled text-to-image generation through a GUI with modular steps for preprocessing, training, and inference.
• Evaluated results via visual inspection, ensuring semantic alignment between text and generated images.
• Tools: Python, TensorFlow, Keras, NumPy, Pandas, Matplotlib, TF-IDF, OpenCVMatplotlib
• Built a sentiment analysis model on the IMDB dataset using TensorFlow, Keras, and Python. Applied sequence padding, embedding, and an LSTM layer to classify reviews as positive or negative.
• Trained the model using categorical cross-entropy loss and Adam optimizer; evaluated with accuracy metric. Saved the model and implemented a text decoding and prediction pipeline.
• Tools: TensorFlow, Keras, Python, NumPy, IMDB Dataset
Learning new Tools and Technologies
Solving Patterns
Adapting AI/ML concepts