Skip to main content

Movie Review : The Cabinet of Dr. Caligari

Title : The Cabinet of Dr. Caligari
Language : Silent Movie
Year : 1920
Director : Robert Wiene
Genre : Horror
IMDB Link
Watch movie on YouTube
Lead Role :  Friedrich Feher, Werner Krauss

The movie is widely acknowledged as one of the landmark revolutionary offerings from the long gone era when movies did not speak. It may be technically incorrect to call a silent film German, nevertheless it was made in Germany during a time period when the European nation was in turmoils after the devastating World War I.


The story begins with a young man by the name of Francis starts narrating the hardships faced by him and his fiancee (Jane) and the very peculiar, even horrifying doings of a strange man, Dr. Caligari. Dr. Caligari owns a stall at a nearby exhibition, and on display is a somnambulist Caeser, who allegedly has slept for 23 straight years! The doctor awakens him, and he answers questions asked by the spectators. To the horror of the locals, his prophecies comes true. Meanwhile, the town is gripped in fear as a series of murders takes place.


The makers have chosen very unorthodox lighting and skewed drawings throughout the picture, and there is a reason which I shall not reveal for the delight of watching it. Anyways, I am pretty sure the movie would not end where or how you expected it to.


A lot has been discussed about this movie since then, even drawing parallels to the autocratic rule of Adolf Hitler and his Nazi party.





Comments

Popular posts from this blog

Machine Unlearning #0 (Intro)

You might be familiar with the term Machine Learning. Worry not if you have not, cause I have tried to give a gist of the concept here. The term has been in the limelight of late and has been tossed around rather liberally to denote anything related to artificial intelligence, robotics, and data mining. Machine Learning, as the name suggests, could simply mean the field of study of enabling the “machines” (computers) to “learn” from past experiences and make informed decisions in the future.   Wait a minute! Learning from past experiences is something humans do, right? Exactly! The computer folks want computers to behave more and more like us. As if there aren't enough of us already. As the machines are becoming more like us, we are becoming more like them. Introspection time! Most of us wake up every morning like clockwork! Then we rush through the morning routines - get dressed, wade through the traffic, and reach our offices or schools or wherever people expect us to be. We spe

The High State

 Before The Judgement I believe I must begin by addressing the pressing question - Was planning a vacation in the midst of a pandemic a recommended move?  No. Yet we went ahead with it. Here is why.  We (Nithya & I) were newly married, and our vividly planned vacation at the island of Langkawi was stolen away from us by the virus. Our stay in Delhi was coming to an end due to job-related moves, and we felt it would be a waste not to utilize this opportunity in exploring at least one of the tourist hot spots easily accessible from the national capital region. Let us end this section by answering another question - Are the reasons listed above good enough to risk a vacation during a pandemic? No. We had taken a calculated risk. Arrival at Manali There are two phases to this - planning and execution. We had not started planning with Manali in mind. There were numerous choices - starting from Jaipur and Amritsar to Nainital, Shimla, and Manali. After a bit of reading and deliberations,

Machine Unlearning #1 (Classification)

You can’t conclude a discussion on Machine Learning without mentioning classification. Classification is a machine learning technique where the machine is trained to predict the label of the given input data. Alright, let’s cut the jargon and get some real-world examples. Oranges and Bananas. Let’s assume that we have a box of fruits that contain some oranges and some bananas. You are asked to pick one fruit at random and tell if it is an orange or a banana. Pretty basic, right? For us, it is straightforward. We would know the answer at first sight. But, how would a computer be able to tell the difference? In classification, the machine would first be trained on some pre-labeled data. It would be shown an orange and we would tell it that the fruit is an orange. The machine would study the orange and remember its features - orange color and round shape. Then it would be shown a banana and the process is repeated. What are these features? A feature is anything that helps us uniquely labe